Relevant bibliographies by topics / Diagnostic Monitoring

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Diagnostic Monitoring'

Author: Grafiati
Published: 2 June 2025
Last updated: 19 July 2025

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Journal articles on the topic "Diagnostic Monitoring"

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Fine, Ronald D. "Diagnostic and monitoring techniques." Medical Journal of Australia 153, no. 5 (1990): 308. http://dx.doi.org/10.5694/j.1326-5377.1990.tb136933.x.

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&NA;. "Monitoring and Diagnostic Electrodes." Journal of Cardiopulmonary Rehabilitation 16, no. 4 (1996): 266. http://dx.doi.org/10.1097/00008483-199607000-00023.

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Gyzhko, Y., M. Gutorova, V. Zvaritch, et al. "APPLICATION OF MULTILEVEL SYSTEMS FOR SOLVING PROBLEMS OF MONITORING AND DI-AGNOSTICS OF ELECTRICAL EQUIPMENT UNITS." Praci Institutu elektrodinamiki Nacionalanoi akademii nauk Ukraini 2023, no. 66 (2023): 150–65. http://dx.doi.org/10.15407/publishing2023.66.150.

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Features of the construction and practical application of multi-level information and measurement systems for monitoring and diagnostics of electrical equipment are considered. The results of the construction and research of mathematical models of some diagnostic signals (acoustic emission, vibrations) accompanying the operation of electrical equipment units (including powerful generators) are given. Improved mathematical models of vibrational information-diagnostic signals are proposed, taking into account both the properties of diagnostic objects and the modes in which such equipment works. According to the results of the analysis of mathematical models of the considered information signals, a number of diagnostic signs are theoretically justified, which allows monitoring and establishing the technical condition of individual generator nodes. As illustrative examples, the use of acoustoemission and vibration diagnostic signals for the formation of training sets, which are part of a multi-level information and measurement system for monitoring and diagnostics of electrical equipment, is considered. The information base obtained at the experimental test stands of the IED of the National Academy of Sciences of Ukraine was used to form such educational groups. It is pointed out the need to take into account the influence of temperature and humidity on the assessment of diagnostic signs during monitoring and diagnostics of electrical equipment. Bible 26, fig. 8. Keywords: vibration and acoustic emission diagnostic signals, electrical equipment, monitoring and diagnostics system.
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Efanov, D. V. "Integration of Control and Monitoring Systems." World of Transport and Transportation 18, no. 1 (2020): 146–57. http://dx.doi.org/10.30932/1992-3252-2020-18-146-157.

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Stationary systems for monitoring devices of train traffic control are currently being implemented as external means of technical diagnostics and are located mainly centrally. The set of diagnostic parameters is scarce, and most measurements are indirect. This, ultimately, leads to low efficiency of monitoring systems, and the share of useful information from the total amount of data according to experts does not exceed 5 %. The development of monitoring technologies should follow the path of integration of measurement and control functions. The objective of the work is to draw the attention of the scientific community to the principles of monitoring and control systems implementation and the transition from their disunity to integration. Using methods of technical diagnostics and monitoring, it is proposed to switch to more advanced control systems with built-in means of troubleshooting and monitoring. The author has proposed the concept of integrated technical diagnostic tools with object controllers in the form of removable monitoring modules that transmit data along dedicated diagnostic transmission paths. Depending on the geographical location of the controller (centralized at the control station or decentralized near the controlled object), a set of diagnostic parameters is determined and a choice of diagnostic information processing methods is carried out. With decentralized location of controllers, the diagnostic modules can transmit diagnostic information from external and distributed sensors at railway infrastructure facilities. Implementation of the presented concept will allow receiving a much larger amount of initial data for operation of monitoring systems, including transition to obtaining digital twins of railway infrastructure facilities. In a broader sense, when organizing monitoring systems, it is necessary to focus not only on railway automation, but also pay attention to other infrastructure facilities serviced by personnel of adjacent sectors. Since all objects function together, such an approach to organization of monitoring will improve quality of diagnosis and prognosis, as well as provide an opportunity to assess the residual resource of technical objects.
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Tamori, Yoshikatu, Mitsuo Tanaka, and Tadashi Kanesa. "Engine Monitoring and Diagnostic System." JOURNAL OF THE MARINE ENGINEERING SOCIETY IN JAPAN 27, no. 6 (1992): 469–74. http://dx.doi.org/10.5988/jime1966.27.469.

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Thomas, R. A., N. Jones, and K. E. Donne. "Infrared thermography and diagnostic monitoring." Imaging Science Journal 48, no. 1 (2000): 23–32. http://dx.doi.org/10.1080/13682199.2000.11784342.

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Adenan, Mohammad Hijaz, Mohamed Khalil, Kai Sheng Loh, et al. "058 Does prolonging duration of monitoring in epilepsy monitoring unit improve diagnostic yield?" Journal of Neurology, Neurosurgery & Psychiatry 93, no. 9 (2022): e2.5. http://dx.doi.org/10.1136/jnnp-2022-abn2.102.

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Long-term video electroencephalographic (vEEG) monitoring is a valuable tool in the evaluation of par- oxysmal clinical events. However, vEEG itself is costly. We aimed to establish whether longer duration of monitoring (DOM) is associated with higher diagnostic yield. We performed a retrospective study of 501 patients admitted to the epilepsy monitoring unit for diagnostic evaluation. Overall, 436 (87%) patients had diagnostic studies. Of these, 67.9% patients received diagnosis within the first 7 days of monitoring with the highest during day 7. A total of 175 had DOM >7 days of which 80.1% were diagnostic. In cohort with DOM >7 days, patients with previous abnormal routine EEG, previous vEEG monitoring, earlier first event recorded and more number of events were more likely to have diagnostic studies. The most common reason for DOM >7 days was to capture more events (76%). Our study supports that longer DOM was associated with an increase in diagnostic yield. More than one third of our cohort were monitored beyond 7 days with majority of these (80.1%) were diagnostic. Our findings may guide clinicians in planning DOM and predicting likelihood of diagnostic studies in patients with prolonged DOM based on clinical char- acteristics and vEEG data.
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Genin, V. S., V. V. Koznov, and S. O. Fel’dman. "Diagnostic monitoring in a distribution network." Russian Electrical Engineering 86, no. 2 (2015): 79–82. http://dx.doi.org/10.3103/s1068371215020054.

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Vora, Parth, Harvinder Singh, Royston Rodrigues, Lavleen Jain, and Prof Sachin Takmare. "Network Monitoring and System Diagnostic Suite." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (2022): 80–83. http://dx.doi.org/10.22214/ijraset.2022.41154.

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Abstract: In several branches of computer science, network monitoring tools are used to continuously monitor a network system's daily functioning, incoming and outgoing packet data, and whether the system is executing its job well. Diagnostic tools, on the other hand, are used to identify faults in a system. Existing tools are typically disjointed, ineffective and out of reach for the average user, and while some may produce verbose outputs, their decoupled nature, as well as the fact that the majority of them do not have a particularly appealing appearance, all accounts for a frustrating user experience. The necessity for a user-friendly interface and the extension of legacy tools necessitates the creation of a suite which this project aims to create that all types of end users can access and use an integrated monitoring and diagnostic suite easily.
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Kościelny, Jan Maciej, Michał Syfert, and Paweł Wnuk. "ADVANCED MONITORING AND DIAGNOSTIC SYSTEM ‘AMandD'." IFAC Proceedings Volumes 39, no. 13 (2006): 635–40. http://dx.doi.org/10.3182/20060829-4-cn-2909.00105.

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Dissertations / Theses on the topic "Diagnostic Monitoring"

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Trill, Helen. "Diagnostic technologies for wound monitoring." Thesis, Cranfield University, 2006. http://hdl.handle.net/1826/1107.

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Chronic wound infections represent a worldwide problem, generating high morbidity and medical expense. Failure to control infections such as MRSA in the reparative process of a wound can cause disruption of normal anatomical structure and function, resulting in a chronic wound. Existing approaches to identifying infection largely involve surveying a range of physical parameters, and a limited use of non-invasive technologies. Evaluation is time consuming, and often results in inconsistencies in patient care. This project researches three possible alternative methodologies/technologies for the monitoring of wounds, by measuring components of wound fluid. Two of the three technologies are designed to be used by physicians and patients, similarly to commercially available home blood glucose test kits, and are based on the measurement of three biomarkers: glucose, ethanol and H2O2 in PBS, and in serum as surrogate wound fluid. The first is a voltammetric technique known as dual pulse staircase voltametry (DPSV), which produces peaks characteristic of particular analytes at an electrode. The second is an amperometric biosensor array, based on screen printed three electrode assembies of carbon, rhodinised carbon (glucose biosensor only) and Ag/AgCl reference. The glucose biosensor uses glucose oxidase enzyme as the biorecognition agent, the H2O2 biosensor is a mediated system using horseradish peroxidase enzyme and dimethylferrocene mediator, and the ethanol biosensor is a bienzyme mediated system utilising alcohol oxidase enzyme horseradish peroxidase enzyme and coupled dimethylferrocene mediator. Wounds are known to produce characteristic odours, therefore the third technology studied is a single sensor odour analyser with advanced data analysis to detect five commonly occuring wound bacteria, S.aureus, K.pneumoniae, S.pyogenes, E.coli and P.aeruginosa in growth media and surrogate wound fluid. This technology would be used as a 'near patient' monitoring system and is based on machine olfaction similar to that of a commercial electronic nose, but uses a single metal oxide sensor in combination with principle components analysis. DPSV scans of the individual analytes demonstrated distinctive peaks, exhibiting nonlinear relationships with concentration. A great deal of useful information was generated using this technique, however, limitations were discovered regarding repeatability and inter-analyte interference in mixtures. Limits of detection in surrogate wound fluid with the glucose biosensor, hydrogen peroxide biosensor, and ethanol biosensor were as follows: 169.5 µM glucose, 8.43 µM hydrogen peroxide, and 7.94 µM ethanol respectively (all at 99.7% confidence). Direct detection of ethanol from metabolically active S.aureus in surrogate wound fluid yielded a limit of detection of 1.23 x 108 CFU/ml at 99.7% confidence, and 19 µM in terms of ethanol specific response. The single sensor odour analyser demonstrated the ability to detect and discriminate between the three biomarkers, between five bacteria individually, and partial discrimination of paired bacteria (in broth and surrogate wound fluid). It was also found that S.aureus could be detected down to a cell density of 5x106CFU/ml in surrogate wound fluid, lower than that found for the biosensor concept.
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Lee, Seungkoo. "An architecture for a diagnostic/prognostic system with rough set feature selection and diagnostic decision fusion capabilities." Diss., Georgia Institute of Technology, 2002. http://hdl.handle.net/1853/14715.

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Bull, Steven M. (Steven Michael) 1979. "Diagnostic process monitoring with temporally uncertain models." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/16827.

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Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.<br>Includes bibliographical references (leaves 67-68).<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>This thesis develops a real-time trend detection and monitoring system based on previous work by Haimowitz, Le, and DeSouza [3, 5, 2]. The monitor they designed, TrenDx, used trend templates in which the temporal points where data patterns change are variable with respect to the actual process data. This thesis uses similar models to construct a monitoring system that is able to run in real time, based on a continuous, linearly segmented process data input stream. The instantiation of temporally significant template points against the process data is determined through a simulated annealing algorithm. The rankings of competing hypotheses in the monitor set is based on the distance of these template points from their expected temporal values, along with the area between the process data measurements and the value constraints placed on those parameters. The feasibility of the real-time monitor was evaluated in the domain of pediatric growth, particularly in comparison to previous versions of TrenDx, using an expert gold standard of the diagnoses of pediatric endocrinologists. Real-time TrenDx shows promise in its monitoring abilities and should be evaluated in other domains which are more suited to its continuous data stream input model.<br>by Steven M. Bull.<br>M.Eng.
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Gohil, Bhupendra. "Diagnostic alarms in anaesthesia." AUT University, 2007. http://hdl.handle.net/10292/956.

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Smart computer algorithms and signal processing techniques have led to rapid development in the field of patient monitoring. Accelerated growth in the field of medical science has made data analysis more demanding and thus the complexity of decision-making procedures. Anaesthetists working in the operating theatre are responsible for carrying out a multitude of tasks which requires constant vigilance and thus a need for a smart decision support system has arisen. It is anticipated that such an automated decision support tool, capable of detecting pathological events can enhance the anaesthetist’s performance by providing the diagnostic information to the anaesthetist in an interactive and ergonomic display format. The main goal of this research was to develop a clinically useful diagnostic alarm system prototype for monitoring pathological events during anaesthesia. Several intelligent techniques, fuzzy logic, artificial neural networks, a probabilistic alarms and logistic regression were explored for developing the optimum diagnostic modules in detecting these events. New real-time diagnostic algorithms were developed and implemented in the form of a prototype system called real time – smart alarms for anaesthesia monitoring (RT-SAAM). Three diagnostic modules based on, fuzzy logic (Fuzzy Module), probabilistic alarms (Probabilistic Module) and respiration induced systolic pressure variations (SPV Module) were developed using MATLABTM and LabVIEWTM. In addition, a new data collection protocol was developed for acquiring data from the existing S/5 Datex-Ohmeda anaesthesia monitor in the operating theatre without disturbing the original setup. The raw physiological patient data acquired from the S/5 monitor were filtered, pre-processed and analysed for detecting anaesthesia related events like absolute hypovolemia (AHV) and fall in cardiac output (FCO) using SAAM. The accuracy of diagnoses generated by SAAM was validated by comparing its diagnostic information with the one provided by the anaesthetist for each patient. Kappa-analysis was used for measuring the level of agreement between the anaesthetist’s and RT-SAAM’s diagnoses. In retrospective (offline) analysis, RT-SAAM that was tested with data from 18 patients gave an overall agreement level of 81% (which implies substantial agreement between SAAM and anaesthetist). RT-SAAM was further tested in real-time with 6-patients giving an agreement level of 71% (which implies fair level of agreement). More real-time tests are required to complete the real-time validation and development of RT-SAAM. This diagnostic alarm system prototype (RT-SAAM) has shown that evidence based expert diagnostic systems can accurately diagnose AHV and FCO events in anaesthetized patients and can be useful in providing decision support to the anaesthetists.
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Hult, Peter. "Bioacoustic principles used in monitoring and diagnostic applications /." Linköping : Univ, 2002. http://www.bibl.liu.se/liupubl/disp/disp2002/tek778s.pdf.

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Eastwood, Paul Graham. "Diesel engine condition monitoring : laser-based diagnostic techniques." Thesis, University of Southampton, 1989. https://eprints.soton.ac.uk/52246/.

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Bruyneel, Marie. "Ambulatory diagnostic and monitoring techniques for sleep disordered breathing." Doctoral thesis, Universite Libre de Bruxelles, 2015. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/216824.

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Techniques ambulatoires de diagnostic et de monitoring des troubles respiratoires liés au sommeil.Le syndrome d’apnées obstructives du sommeil (SAOS) est un trouble du sommeil très fréquent, fortement lié à l’obésité, ce qui explique sa prévalence en pleine expansion. En parallèle, la demande d’examens polysomnographiques (PSG) en laboratoire du sommeil, méthode diagnostique de référence, est en croissance. Comme l’accès à cette technique est peu aisé, de nombreux appareils simplifiés d’enregistrement de sommeil ont été récemment développés, mais restent imparfaits (mauvaise évaluation du temps de sommeil, sous-estimation de la sévérité du SAOS, faux négatifs, taux d’échec élevé) et sont d’un apport limité pour le diagnostic du SAOS. La PSG au domicile (PSG-d) est une alternative bien plus informative, permettant d’éviter nombre des désavantages rencontrés par l’usage d’appareils simplifiés. Nous l’avons dès lors étudiée pour le diagnostic du SAOS, au travers d’une étude randomisée comparant la PSG-d vs la PSG hospitalière. En termes d’efficacité diagnostique, les résultats sont excellents, avec un faible taux d’échec d’examens à domicile (4.7 vs 1.5%). Les patients préfèrent être enregistrés dans leur propre environnement où la qualité de leur sommeil est d’ailleurs meilleure. Nous avons ensuite voulu faire le point sur la littérature récente au travers d’un article de revue, en analysant les études prospectives randomisées comparant la PSG-d et au labo du sommeil. Les résultats de ces études concordent pour démontrer que la PSG-d constitue une excellente alternative aux tests réalisés à l’hôpital. Outre le SAOS, l’outil permet le diagnostic d’autres troubles du sommeil, comme les mouvements périodiques des jambes durant le sommeil, les troubles du rythme circadien, Une question restée jusqu’ici sans réponse était l’influence de la localisation du branchement des PSG-d, à l’hôpital ou à domicile. Une étude prospective randomisée nous a permis d’établir que la localisation du branchement des PSG-d n’influençait pas la qualité globale de l’examen, ce qui simplifiera l’utilisation de cet outil à l’avenir. Enfin, nous avons utilisé des techniques de télé monitoring (TM) pour contrôler, en temps réel, la qualité des PSG-d. Dans une première étude pilote, la faisabilité a été confirmée, malgré quelques difficultés techniques. Nous avons voulu appliquer la technique à une population de patients souffrant d’un syndrome coronarien aigu, incapables d’être enregistrés au labo du sommeil. Nous avons étudié la qualité du screening du SAOS par PSG vs polygraphie (PG). Les résultats se sont révélés surprenants :82% de cette population présentait des troubles respiratoires liés au sommeil, principalement centraux. La PSG était nettement plus sensible que la PG, et le TM améliorait la qualité des PSG. Chez les patients traités pour SAOS, nous avons ensuite utilisé un outil de monitoring, l’actigraphie (Act), afin d’observer, dans la vie de tous les jours, les changements de schémas de sommeil et d’activité physique engendrés par la pression positive continue (PPC). Dans un premier travail, rétrospectif, nous avons observé ces paramètres chez des SAOS avant traitement, puis au travers d’une étude prospective multicentrique, nous avons suivi 150 patients avant et après PPC, et observé chez eux une augmentation de temps de sommeil, mais pas de l’activité physique. En conclusion, nous avons démontré dans cette thèse l’intérêt clinique de deux excellents outils ambulatoires, la PSG-d et l’Act, pour la prise en charge du SAOS. Les implications potentielles sont une meilleure accessibilité diagnostique pour le SAOS, une initiation thérapeutique plus précoce et un suivi plus précis des SAOS traités, dans des conditions ambulatoires, plus confortables et plus adéquates pour les patients.<br>Ambulatory diagnostic and monitoring techniques for sleep disordered breathingSleep disordered breathing (SDB), including obstructive sleep apnea syndrome (OSAS), is directly related to obesity. Significant morbi-mortality is associated with OSAS, explaining the increasing demand for in-hospital polysomnography (PSG), the reference diagnostic method. As this technique is complex and time-consuming, many simplified portable monitoring (PM) devices for home sleep testing have been developed. However, the ability of PM devices to detect OSA remains limited: sleep time is not correctly assessed, OSA severity is underestimated, false negative results occur and the failure rate of the tests is high, up to 30%. Home-PSG (H-PSG) is an interesting alternative, avoiding many of these drawbacks. In the first part of this work, we studied the tool in an original study comparing H-PSG and in-lab PSG. Diagnostic efficacy was good and the failure rate low (4.7 vs 1.5%). Patients slept in their own environment and thus sleep quality was better. We were then interested by reviewing recent literature data regarding prospective randomised trials comparing H-PSG and in-lab PSG. We concluded that H-PSG is an excellent alternative for in-lab PSG, allowing not only OSA detection but also diagnosis of a large panel of other sleep disorders (periodic leg movements during sleep, circadian disorders,). As the best place to perform set-up for H-PSG remained unknown, we studied, in another prospective randomised study, the recording’s quality obtained in both settings. As no difference was observed, lab set up was found to be the simpler option for performing H-PSG. We then tested, in a prospective pilot study, real-time telemonitoring (TM) of H-PSG in order to enhance recording quality. Results were encouraging but we faced some technical problems. In a second study, we applied TM coupled with PSG to detect SDB in acute coronary syndrome, in patients too unstable to come in the sleep lab. We compared also PSG results to polygraphy (PG). Surprisingly, 82% of patients suffered from SDB. PSG was much more sensitive than PG to screen SDB in this population and TM improves recording quality. In the second part of this work, we have used actigraphy (Act) to assess sleep and physical activity in OSA patients in real-life conditions. Firstly, in a retrospective study, we documented these parameters before treatment. In a second multicentre study, we evaluated the changes in sleep schemes and physical activity under continuous positive airway pressure (CPAP) in 150 OSA patients. We observed that sleep time was increased under CPAP, but physical activity was not improved, contrarily to sleepiness and quality of life. In conclusion, we have shown through these works the clinical interest of two excellent ambulatory tools, H-PSG and Act, for OSA management. Potential clinical implications include enhanced healthcare accessibility, earlier treatment initiation and a closer follow-up of treated patients, through ambulatory tools, in a comfortable environment for the patients.<br>Doctorat en Sciences médicales (Médecine)<br>info:eu-repo/semantics/nonPublished
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Iranmanesh, Saam. "Wearable electroencephalography for long-term monitoring and diagnostic purposes." Thesis, Imperial College London, 2018. http://hdl.handle.net/10044/1/62277.

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Truly Wearable EEG (WEEG) can be considered as the future of ambulatory EEG units, which are the current standard for long-term EEG monitoring. Replacing these short lifetime, bulky units with long-lasting, miniature and wearable devices that can be easily worn by patients will result in more EEG data being collected for extended monitoring periods. This thesis presents three new fabricated systems, in the form of Application Specific Integrated Circuits (ASICs), to aid the diagnosis of epilepsy and sleep disorders by detecting specific clinically important EEG events on the sensor node, while discarding background activity. The power consumption of the WEEG monitoring device incorporating these systems can be reduced since the transmitter, which is the dominating element in terms of power consumption, will only become active based on the output of these systems. Candidate interictal activity is identified by the developed analog-based interictal spike selection system-on-chip (SoC), using an approximation of the Continuous Wavelet Transform (CWT), as a bandpass filter, and thresholding. The spike selection SoC is fabricated in a 0.35 μm CMOS process and consumes 950 nW. Experimental results reveal that the SoC is able to identify 87% of interictal spikes correctly while only transmitting 45% of the data. Sections of EEG data containing likely ictal activity are detected by an analog seizure selection SoC using the low complexity line length feature. This SoC is fabricated in a 0.18 μm CMOS technology and consumes 1.14 μW. Based on experimental results, the fabricated SoC is able to correctly detect 83% of seizure episodes while transmitting 52% of the overall EEG data. A single-channel analog-based sleep spindle detection SoC is developed to aid the diagnosis of sleep disorders by detecting sleep spindles, which are characteristic events of sleep. The system identifies spindle events by monitoring abrupt changes in the input EEG. An approximation of the median frequency calculation, incorporated as part of the system, allows for non-spindle activity incorrectly identified by the system as sleep spindles to be discarded. The sleep spindle detection SoC is fabricated in a 0.18 μm CMOS technology, consuming only 515 nW. The SoC achieves a sensitivity and specificity of 71.5% and 98% respectively.
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Maree, Charl. "Diagnostic monitoring of dynamic systems using artificial immune systems." Thesis, Stellenbosch : University of Stellenbosch, 2006. http://hdl.handle.net/10019.1/1780.

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Thesis (MScEng (Process Engineering))--University of Stellenbosch, 2006.<br>The natural immune system is an exceptional pattern recognition system based on memory and learning that is capable of detecting both known and unknown pathogens. Artificial immune systems (AIS) employ some of the functionalities of the natural immune system in detecting change in dynamic process systems. The emerging field of artificial immune systems has enormous potential in the application of fault detection systems in process engineering. This thesis aims to firstly familiarise the reader with the various current methods in the field of fault detection and identification. Secondly, the notion of artificial immune systems is to be introduced and explained. Finally, this thesis aims to investigate the performance of AIS on data gathered from simulated case studies both with and without noise. Three different methods of generating detectors are used to monitor various different processes for anomalous events. These are: (1) Random Generation of detectors, (2) Convex Hulls, (3) The Hypercube Vertex Approach. It is found that random generation provides a reasonable rate of detection, while convex hulls fail to achieve the required objectives. The hypercube vertex method achieved the highest detection rate and lowest false alarm rate in all case studies. The hypercube vertex method originates from this project and is the recommended method for use with all real valued systems, with a small number of variables at least. It is found that, in some cases AIS are capable of perfect classification, where 100% of anomalous events are identified and no false alarms are generated. Noise has, expectedly so, some effect on the detection capability on all case studies. The computational cost of the various methods is compared, which concluded that the hypercube vertex method had a higher cost than other methods researched. This increased computational cost is however not exceeding reasonable confines therefore the hypercube vertex method nonetheless remains the chosen method. The thesis concludes with considering AIS’s performance in the comparative criteria for diagnostic methods. It is found that AIS compare well to current methods and that some of their limitations are indeed solved and their abilities surpassed in certain cases. Recommendations are made to future study in the field of AIS. Further the use of the Hypercube Vertex method is highly recommended in real valued scenarios such as Process Engineering.
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DeNucci, Thomas W. "Diagnostic indicators for shipboard systems using non-intrusive load monitoring." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/33576.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Ocean Engineering; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2005.<br>Includes bibliographical references (leaves 87-88).<br>Field studies have demonstrated that the Non-Intrusive Load monitor (NILM) can provide real-time indication of the condition of electro-mechanical systems on board naval vessels. Results from data collected from engineering systems on board USCGC SENECA (WMEC-906), a 270-foot U.S. Coast Guard cutter, indicate that the NILM can effectively identify faults, failures and deviations from normal operating conditions on numerous shipboard engineering systems. Data collected from the sewage system identified metrics that can be applied, for example, to cycling systems (high pressure air, hydraulic systems, etc.) to differentiate between periods of heavy usage and fault conditions. Sewage system variability and randomness was minimized by employing a MATLAB simulation designed to permit exploration of system behavior that might not have been exposed during other conditions. Simulation data suggests that the presence, location and magnitude of a spike in the pump run distribution indicated the presence of a leak. Data from the actual shipboard system, when subjected to a quantifiable leak, displayed the same behavior. Data collected from the Auxiliary Seawater (ASW) System indicated that the NILM is able to predict the failure of a flexible coupling linking the pump and motor components.<br>(cont.) The ASW motor-pump system was modeled using a 5th order induction motor simulation to explore the electro-mechanical relationships between the motor, coupling and pump. Changes to the mechanical parameters of the coupling were captured in the electrical signature of the motor in both the simulation and shipboard data. Frequency domain analysis of the ASW System data also suggested that the clogging of a heat exchanger on a critical shipboard system can be identified with the NILM, although the extent of diagnosis is dependent on the system flow patterns. Further development of hardware and software, along with continued research into the behavior of shipboard systems, will allow the NILM to augment existing monitoring systems and potentially serve as a stand-alone indicator of critical system performance.<br>by Thomas W. DeNucci.<br>S.M.
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Books on the topic "Diagnostic Monitoring"

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Rao, Raj B. K. N., Joe Au, and Brian Griffiths, eds. Condition Monitoring and Diagnostic Engineering Management. Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0431-6.

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PUBLISHER, PRENTICE HALL. Florida diagnostic and progress monitoring tests. Prentice Hall, 2004.

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Fagan, William T. Monitoring literacy performance: Assessment and diagnostic tasks . Examiner's manual. Éditions de la chenelière, 1992.

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Baura, Gail. Biosystems Approach to Industrial Patient Monitoring and Diagnostic Devices, A. Springer International Publishing, 2008. http://dx.doi.org/10.1007/978-3-031-01625-7.

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Jones, G. R. High pressure arcs in industrial devices: Diagnostic and monitoring techniques. Cambridge University Press, 1988.

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University), International Congress on Condition Monitoring and Diagnostic Engineering Management (2nd 1990 Brunel. Condition monitoring and diagnostic engineering management: Proceedings of COMADEM 90 : the Second International Congress on Condition Monitoring and Diagnostic Engineering Management, Brunel University, 16-18 July 1990. Chapman and Hall, 1990.

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Frost & Sullivan. AIDS diagnostic, monitoring, and therapeutic markets: Technological assessment and financial impact. Frost & Sullivan, 1994.

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United States. National Aeronautics and Space Administration., ed. Final report on the Galileo PPS expert monitoring and diagnostic prototype. Aura, 1989.

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Dwivedi, Priyanka, Chinmay Chakraborty, Gwanggil Jeon, and Yan Zhang. Nanosensors as Robust Non-Invasive Diagnostic Tools for Remote Health Monitoring. CRC Press, 2025. https://doi.org/10.1201/9781003602729.

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International Congress on Condition Monitoring and Diagnostic Engineering Management (3rd 1991 Southampton Institute). Condition monitoring and diagnostic engineering management: Proceedings of COMADEM 91, the Third International Congress on Condition Monitoring and Diagnostic Engineering Management, held at Southampton Institute, 2-4 July 1991. A. Hilger, 1991.

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Book chapters on the topic "Diagnostic Monitoring"

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Wood, Paul L., and H. Gordon Dobbie. "Diagnostic Pitfalls." In Electronic Fetal Heart Rate Monitoring. Macmillan Education UK, 1989. http://dx.doi.org/10.1007/978-1-349-19875-7_6.

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Raugel, Pierre-Jean. "International Diagnostic Group." In Rapid Food Analysis and Hygiene Monitoring. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-58362-9_28.

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Rao, B. K. N. "Profitable Condition Monitoring & Diagnostic Engineering Management." In Profitable Condition Monitoring. Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1616-9_5.

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Baura, Gail. "Diagnostic Devices." In Biosystems Approach to Industrial Patient Monitoring and Diagnostic Devices, A. Springer International Publishing, 2008. http://dx.doi.org/10.1007/978-3-031-01625-7_4.

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Monieta, Jan. "Preliminary Investigations of Marine Diesel Engines Turbochargers Diagnostic." In Applied Condition Monitoring. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62042-8_7.

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Milne, Robert. "On-Line Diagnostic Expert System For Gas Turbines." In Profitable Condition Monitoring. Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1616-9_6.

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Diana, G., A. Vania, A. Vallini, and G. A. Zanetta. "Diagnostic Techniques in Condition Monitoring." In Diagnostics of Rotating Machines in Power Plants. Springer Vienna, 1994. http://dx.doi.org/10.1007/978-3-7091-2706-3_25.

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Barszcz, Tomasz, Adam Jabłoński, Cezary Worek, Łukasz Krzak, and Rafał Mrówka. "New Technologies for Wireless Energy-Efficient Machinery Diagnostic System." In Applied Condition Monitoring. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62042-8_13.

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Fidali, Marek, Paweł Wojciechowski, and Aleksander Pełka. "Fault Detection of Railway Point Machine Using Diagnostic Models." In Applied Condition Monitoring. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62042-8_25.

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Weindorf, Norbert, Ulrich Schultz-Ehrenburg, and Peter Altmeyer. "Diagnostic Assessment of Diabetic Microangiopathy by TCPO2 Stimulation Tests." In Continuous Transcutaneous Monitoring. Springer US, 1987. http://dx.doi.org/10.1007/978-1-4613-1927-6_15.

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Conference papers on the topic "Diagnostic Monitoring"

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Polla, D., L. Francis, W. Robbins, and R. Harjani. "MEMS for Integrated Diagnostic Applications." In ASME 1997 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1997. http://dx.doi.org/10.1115/imece1997-1266.

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Abstract Microelectromechanical Systems (MEMS) technology is currently being applied to the structural health monitoring of critical aircraft components. The approach integrates acoustic emission (AE) and vibration monitoring devices with signal processing electronics to provide real-time indicators of incipient failure of aircraft (helicopter) components with a known history of catastrophic failure due to fracture. This paper describes MEMS-based sensor development, piezoelectric microsensor design and fabrication, and signal processing approaches directed toward the realization of inexpensive, multiple, MEMS-based integrated diagnostics “coupons”. Initial feasibility data are presented.
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Loboda, Igor, Sergey Yepifanov, and Yakov Feldshteyn. "An Integrated Approach to Gas Turbine Monitoring and Diagnostics." In ASME Turbo Expo 2008: Power for Land, Sea, and Air. ASMEDC, 2008. http://dx.doi.org/10.1115/gt2008-51449.

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This paper presents an investigation of a conventional gas turbine diagnostic process and its generalization. A usual sequence of diagnostic actions consists of two stages: monitoring (fault detection) and subsequent proper diagnosis (fault identification). Such an approach neither implies fault identification nor uses the information about incipient faults unless the engine is recognized as faulty. In previous investigations for engine steady state operation conditions we addressed diagnostics problems without their relation with the monitoring process. Fault classes were given by samples of patterns generated by a static gas turbine performance model. This fault simulation took into account faults of varying severity including incipient ones. A diagnostic algorithm employed artificial neural networks to identify an actual fault. In the present paper we consider the monitoring and diagnosis as joint processes extending our previous approach over both of them. It is proposed to form two classes for the monitoring using the above-mentioned classes constructed for the diagnosis. A two-shaft industrial gas turbine has been chosen to test the proposed integrated approach to monitoring and diagnosis. A general recommendation following from the presented investigation is to identify faults simultaneously with fault detection. This permits accumulating preliminary diagnoses before the engine faulty condition is detected and a rapid final diagnosis after the fault detection.
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Marecek, Oto. "Monitoring and diagnostic system of power plant electrical equipment." In 2016 Conference on Diagnostics in Electrical Engineering (Diagnostika). IEEE, 2016. http://dx.doi.org/10.1109/diagnostika.2016.7736505.

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Iwasaki, Atsushi, Akira Todoroki, Tsuneya Sugiya, and Shinsuke Sakai. "Damage diagnosis for SHM of existing civil structure with statistical diagnostic method." In NDE for Health Monitoring and Diagnostics, edited by Tribikram Kundu. SPIE, 2004. http://dx.doi.org/10.1117/12.539467.

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Pan, Min-Chun, and Po-Ching Li. "Remote online machine fault diagnostic system." In NDE for Health Monitoring and Diagnostics, edited by Tribikram Kundu. SPIE, 2004. http://dx.doi.org/10.1117/12.537722.

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XU, XUEBING, and CHENG LIU. "DELAMINATION DAMAGE DETECTION OF CFRP COMPOSITE STRUCTURES USING DSAN-BASED DEEP TRANSFER LEARNING APPROACH." In Structural Health Monitoring 2023. Destech Publications, Inc., 2023. http://dx.doi.org/10.12783/shm2023/36866.

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Carbon fiber reinforced plastics (CFRP) is a kind of lightweight composite material widely used in aerospace. Since the progressive development of fatigue damage is complex and leads to potential safety risks in CFRP structures, structural health monitoring based on Lamb wave has been developed to track the growth of fatigue damages using a sensor network attached to the surface, which is experimentally intensive and expensive. To overcome the above challenges, a composite fatigue damage diagnosis method based on deep transfer learning is proposed to transfer the physical mechanism provided by numerical models to the diagnosis of real monitoring data. Firstly, numerical models of the composite structures are built to indicate the accumulation of fatigue damage during the full life cycle by introducing delamination between three sub-structures of cross-ply laminates. Then simulation signals with high fidelity are generated by virtual sensors and input into a data-driven diagnostic model with monitoring data. By aligning the data distribution of corresponding categories in simulation and experiment datasets respectively, the sub-domain adaptation is implemented and the physical mechanism provided by digital models is thereby fused with monitoring data. In this situation, the diagnostic model can still achieve more than 81% accuracy on a smaller training set, which performs better than conventional methods and significantly reduces the number of aging experiments.
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Schwarz, R., and M. Muhr. "Diagnostic methods for transformers." In 2008 International Conference on Condition Monitoring and Diagnosis. IEEE, 2008. http://dx.doi.org/10.1109/cmd.2008.4580445.

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Garudadri, Harinath, Yuejie Chi, Steve Baker, Somdeb Majumdar, Pawan K. Baheti, and Dan Ballard. "Diagnostic grade wireless ECG monitoring." In 2011 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2011. http://dx.doi.org/10.1109/iembs.2011.6090194.

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LIU, JINGXIAO, SUSU XU, MARIO BERGÉS, and HAE YOUNG NOH. "A HIERARCHICAL DOMAIN-ADVERSARIAL AND MULTI-TASK LEARNING ALGORITHM FOR BRIDGE DAMAGE DIAGNOSIS USING A DRIVE-BY VEHICLE." In Structural Health Monitoring 2021. Destech Publications, Inc., 2022. http://dx.doi.org/10.12783/shm2021/36277.

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Monitoring bridge health using vibrations of drive-by vehicles has various benefits, such as no need for directly installing and maintaining sensors on the bridge. However, many of the existing drive-by monitoring approaches are based on supervised learning models that require labeled data from every bridge of interest, which is expensive and time-consuming, if not impossible, to obtain. This labeling requirement is further exacerbated by having multiple diagnostic tasks (e.g., damage detection, localization, and quantification) because they have different learning difficulties. To this end, we introduce a multi-task domain adaptation framework that transfers the damage diagnosis model learned from one bridge to a new bridge without requiring any labels from the new bridge in any of the tasks. Our framework trains a hierarchical neural network model in an adversarial way to extract task-shared and task-specific features that are informative to multiple diagnostic tasks and invariant across multiple bridges. We evaluate our framework on experimental laboratory data collected from 2 bridges and 3 vehicles. We achieve accuracies of 95% for damage detection, 93% for localization, and up to 72% for quantification, which are ~2 times improvements from a baseline method.
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NICHIFOR, ALEXANDRU, and YONGZHI QU. "KOOPMAN OPERATOR BASED FAULT DIAGNOSTIC METHODS FOR MECHANICAL SYSTEMS." In Structural Health Monitoring 2021. Destech Publications, Inc., 2022. http://dx.doi.org/10.12783/shm2021/36299.

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Traditionally, dynamical systems can be simulated with physics-based model when the design parameters and material property are pre-known. However, when a system is deployed in field and has suffered potential degradation, a physics-based model might be infeasible to obtain. Moreover, the non-linearity and unknown coupling between the system and contacting constraints are often hard to determine accurately. The analysis of those systems becomes practically problematic. In this paper, the Koopman operator is used to learn and represent a dynamic system in a data driven manner. This paper proposes two methods of using the Koopman operator to extract and classify critical parameters of a non-linear dynamic mechanical system for fault diagnosis. The first method proposes a model to extract key features from a dynamic system and feed the features to a neural network to classify the existence of a fault. The second method uses parameters derived from the Koopman operator to create a prediction model with healthy data. This prediction model is then used to predict future system dynamics for a measured time evolution and compare that with direct measurements when future dynamics become available. Both methods are then tested via an experimental case study and the results are discussed.
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Reports on the topic "Diagnostic Monitoring"

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Beshouri. PR-309-11202-R01 Field Demonstration Test of Advanced Engine and Compressor Diagnostics for CORE. Pipeline Research Council International, Inc. (PRCI), 2013. http://dx.doi.org/10.55274/r0010569.

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Pipeline engines fitted with modem automation and control systems serve as ideal candidates for On Board Diagnostics (OBD) and Diagnostic Monitoring (DM). OBD is very effective for real time performance monitoring, pollution prevention and safety monitoring in the engine room. Diagnostic Monitoring (DM) extends the capabilities of OBD by using extensive historical data to characterize unit specific characteristics thereby dealing with engines as individuals. The work to date has focused on the development of a methodology to support real-time multiparameter analysis and crosscheck of engine data to quickly, accurately and precisely diagnose engine faults. The methodology uses a spreadsheet interface in combination with Mean Value Engine Models and an analytic table to perform the diagnosis. Field gathered data was then used Advanced Engine Technologies Corporation (AETC) then developed a playback simulator to replay data provided by PRCI members to test the methodology and confirm the ability to automatically detect engine and compressor faults.
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Piette, Mary Ann, Satkartar Kinney, Norman Bourassa, et al. Early evaluation of a second generation information monitoring and diagnostic system. Office of Scientific and Technical Information (OSTI), 2002. http://dx.doi.org/10.2172/840325.

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Carothers, Christopher D. FINAL REPORT: Predictive Modeling and Diagnostic Monitoring of Extreme Science Workflows. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1569262.

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Sun, Lina, Yanan Han, Hua Wang, et al. MicroRNAs as Potential Biomarkers for the Diagnosis of Inflammatory Bowel Disease: A Systematic Review and Meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.2.0027.

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Review question / Objective: The purpose of this systematic review was to systematically review the clinical studies regarding miRNAs as diagnostic biomarkers for inflammatory bowel disease and assess the overall diagnostic accuracy of miRNAs. Condition being studied: The symptoms of inflammatory bowel disease (IBD) are highly variable. The diagnosis of IBD must be made through medical history, physical, laboratory, radiologic, endoscopic, and histological examinations. However, these diagnostic techniques are not specific and sometimes even equivocal. Therefore, reliable biomarkers are urgently needed in the diagnosis of IBD. Several clinical and preclinical researches have shown that dysregulated microRNAs (miRNAs) play a crucial role in IBD development. miRNAs, as single-stranded noncoding RNAs that contain 22-24 nucleotides, can post-transcriptionally regulate gene expression by blocking mRNA translation or degrading target mRNAs. miRNAs are widely involved in physiological and pathological cellular processes, such as differentiation, proliferation and apoptosis. Besides, they are stable, noninvasive, and resistant to degradation by ribonucleases, making them valuable targets in the diagnosis, monitoring, prognosis, and treatment of diseases. To date, inconsistent results have been found about miRNA expression profiling in the patients with IBD. Moreover, the diagnostic accuracy of miRNAs for IBD has not been reported in any meta-analysis.
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Peterson, Bradley S., Joey Trampush, Margaret Maglione, et al. ADHD Diagnosis and Treatment in Children and Adolescents. Agency for Healthcare Research and Quality (AHRQ), 2024. http://dx.doi.org/10.23970/ahrqepccer267.

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Objective. The systematic review assessed evidence on the diagnosis, treatment, and monitoring of attention deficit hyperactivity disorder (ADHD) in children and adolescents to inform a planned update of the American Academy of Pediatrics (AAP) guidelines. Data sources. We searched PubMed®, Embase®, PsycINFO®, ERIC, clinicaltrials.gov, and prior reviews for primary studies published since 1980. The report includes studies published to June 15, 2023. Review methods. The review followed a detailed protocol and was supported by a Technical Expert Panel. Citation screening was facilitated by machine learning; two independent reviewers screened full text citations for eligibility. We abstracted data using software designed for systematic reviews. Risk of bias assessments focused on key sources of bias for diagnostic and intervention studies. We conducted strength of evidence (SoE) and applicability assessments for key outcomes. The protocol for the review has been registered in PROSPERO (CRD42022312656). Results. Searches identified 23,139 citations, and 7,534 were obtained as full text. We included 550 studies reported in 1,097 publications (231 studies addressed diagnosis, 312 studies addressed treatment, and 10 studies addressed monitoring). Diagnostic studies reported on the diagnostic performance of numerous parental ratings, teacher rating scales, teen/child self-reports, clinician tools, neuropsychological tests, EEG approaches, imaging, and biomarkers. Multiple approaches showed promising diagnostic performance (e.g., using parental rating scales), although estimates of performance varied considerably across studies and the SoE was generally low. Few studies reported estimates for children under the age of 7. Treatment studies evaluated combined pharmacological and behavior approaches, medication approved by the Food and Drug Administration, other pharmacologic treatment, psychological/behavioral approaches, cognitive training, neurofeedback, neurostimulation, physical exercise, nutrition and supplements, integrative medicine, parent support, school interventions, and provider or model-of-care interventions. Medication treatment was associated with improved broadband scale scores and ADHD symptoms (high SoE) as well as function (moderate SoE), but also appetite suppression and adverse events (high SoE). Psychosocial interventions also showed improvement in ADHD symptoms based on moderate SoE. Few studies have evaluated combinations of pharmacological and youth-directed psychosocial interventions, and we did not find combinations that were systematically superior to monotherapy (low SoE). Published monitoring approaches for ADHD were limited and the SoE is insufficient. Conclusion. Many diagnostic tools are available to aid the diagnosis of ADHD, but few monitoring strategies have been studied. Medication therapies remain important treatment options, although with a risk of side effects, as the evidence base for psychosocial therapies strengthens and other nondrug treatment approaches emerge.
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Piette, Mary Ann. Performance assessment and adoption processes of an information monitoring and diagnostic system prototype. Office of Scientific and Technical Information (OSTI), 1999. http://dx.doi.org/10.2172/789104.

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Moss, Eric. Strain Gauge Diagnostic Development for use in Vessel Health Monitoring for Hydro-shots. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1856127.

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Beshouri. PR-309-08208-R01 A Survey of Diagnostics Techniques for Compressor Engines. Pipeline Research Council International, Inc. (PRCI), 2009. http://dx.doi.org/10.55274/r0010730.

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The pipeline workforce faces several challenges over the next few years. Unprecedented numbers of experienced staff are retiring. They are being replaced by a smaller, younger workforce tasked with more responsibilities and therefore unable to develop specific expertise while roving crews fill labor gaps resulting in less experience with each individual engine compressor. To maintain, if not improve reliability and reduce maintenance costs better analysis tools are needed to fill the experience and expertise gap and assist with troubleshooting and diagnosis. Concurrently, regulatory expectations appear to be shifting from Continuous Emissions Monitoring System (CEMS) like quantifying systems to Green-light/Red-light automotive like On Board Diagnostics (OBD). OBD approaches rely on the proper operation of already installed sensors and actuators to maintain emissions compliance and can be very cost effective. This study investigated diagnostic methods to address both opportunities based on experience in related industries.
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Zanker. PR-343-14601-R01 Smart USM Diagnostics - Phase 3. Pipeline Research Council International, Inc. (PRCI), 2014. http://dx.doi.org/10.55274/r0010043.

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Ultrasonic flow meters, especially those that exploit multiple paths to achieve higher accuracies, are capable of extensive self-diagnosis. Each ultrasonic meter manufacturer has tended to devel-op diagnostic tools specific to his particular device. This has led to a confusing mix of offerings that are not transportable between meters and whose names and interpretations are not always consistent. The focus of this effort has been to examine four areas that yield information indicative of both the health of the meter and the quality of the measurements it is providing: (1) the meter design and how this impacts the individual diagnostics, (2) the timing aspects of the measurement, (3) the indicators of transducer performance and (4) the observed velocity profile characteristics. This work extends the previous analysis of Daniel and Instromet (Elster) ultrasonic meters to in-clude Sick and Krohne meters. It also updates the Daniel and Instromet information and looks at graphical displays that help with condition based monitoring.
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Hawley. PR-015-11707-R01 Test Diagnostic Methods for Turbine Gas Meters. Pipeline Research Council International, Inc. (PRCI), 2013. http://dx.doi.org/10.55274/r0010671.

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Similar to most metering technologies, turbine meters are known to be affected by abnormal flow or abnormal mechanical conditions which can cause bias in flow measurement. These types of flow conditions include blockage at the flow meter or straightening vanes, grime or liquid contamination on the internal meter components, damage to the internal meter components, and pulsation in the flow. With the introduction of ultrasonic and Coriolis meters for gas applications, the natural gas industry has embraced the concept of meters with embedded diagnostic capabilities. These capabilities allow the detection of potential problems with the flow behavior or meter condition that may lead to measurement error. Diagnostic measurements also exist for turbine meters. Some turbine meter manufacturers provide techniques for diagnosing proper meter performance through approaches that include unique design attributes (e.g., dual-rotors) or by monitoring the characteristics (shape, timing, etc.) of the pulses produced as blades pass a sensor. Various analog and digital signal analysis methods exist to interpret the output pulse characteristics to determine meter condition attributes such as bent blades and bearing wear. The objective of this research was to assess, through flow testing, the ability of various diagnostic methods to detect abnormal flow and abnormal mechanical conditions for both single and dual-rotor turbine meters. A secondary objective was to determine the amount of flow measurement error that could be present for the various flow conditions that were tested. The approach was to test three different diagnostic methods on a single-rotor and dual-rotor turbine meter at the Metering Research Facility at Southwest Research Institute. The selected diagnostic methods were the Smith MeterTM AccuLERT II from FMC Technologies, TurbinScope from Elster-Instromet, and The Turbo Corrector from Mercury Instruments. Tests were performed under controlled conditions and were designed to determine the ability of the selected diagnostics to detect various levels of flow meter or tube bundle blockage, grime buildup on the rotor or rotor bearings, damage to the rotor, or flow pulsations.
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