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This paper presents an innovative method devised to study some of the harmful effects of moisture on power transformers, considering the possible formation of bubbles and free water. Its novelty stems from the fact that it comprises: (a) a rating system from "A" to "E" and their respective recommended actions and (b) an analysis that involves varying the different levels of electrical loads applied to the equipment. The importance of this work is confirmed when power transformers are exposed to unfavorable conditions that can critically affect their insulation. To this end, a tool to study moisture was developed, which uses the finite difference method, based not only on the experience of experts/analysts but also on existing studies in the area and on the analysis of a database of actual field tests. The contribution of this work is an efficient method, implemented via a computational algorithm, which provides a technique for the real-time or offline rating of power transformers, helping maintenance teams to diagnose problems and decide whether or not to repair the equipment. The tool provided the satisfactory results, which are presented in a case study of a real transformer operating at the local electric utility company. These results were obtained by applying different levels of electrical loads, for which thermal simulations were performed. [ABSTRACT FROM AUTHOR]

Copyright of Acta Paedagogica Vilnensia is the property of Vilnius University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the last few years, the study of the surface color of historical buildings has progressed as part of conservation and restoration efforts to comply with the aesthetics of historical and pre-existing materials. The main critical element related to this type of study is represented by the succession of various interventions of restoration and maintenance over time that has altered the original aesthetics of monuments by using materials and colors selected according to criteria, trends, and needs of succeeding historical eras. In this paper, the investigation of plasters and colored finishing layers applied on two historical buildings located in Rome and dating to the early 1900s is reported. Results were obtained through a specific diagnostic investigation plan focused on microscopic, morphological, and chemical characterizations of plasters and colored layers detected on the monument surfaces. All post-intervention phases and dates have been identified by comparing the results obtained with historical data. Important information was uncovered that helps in tracing the original aesthetic conception of the buildings and in planning restoration approaches which can be compatible both aesthetically and materially with the original work. [ABSTRACT FROM AUTHOR]

Industrial components and systems undergo degradation in process operations. Prognostics and health management (PHM) is a process to assess and predict health conditions of components and can be applied to condition-based monitoring and maintenance. PHM is commonly utilized to analyze the health condition of a single lifecycle until failure. When maintenance occurs, degradation can be removed, and the PHM model can be restarted with new parameters related to the expected postmaintenance conditions. Maintenance actions, mostly imperfect repairs, may not entirely reset the condition to as good as new, and further degradation may occur at a higher rate. Maintenance repairs should be considered in prognostic models to predict component health more accurately. Furthermore, processes typically have more than one component that degrade and influence process measurements. The dependence of process measurements to multiple fault modes and related degradation can make individual component health monitoring complex. Commonly, faults and their related effects on process parameters must be isolated. In these cases, the diagnostics and prognostics framework should handle unsynchronized failure and maintenance reinitialization of different components for multiple fault processes. This research paper presents the Maintenance-Dependent Monitoring and Prognostics Model (MDMPM) to detect anomalies, decouple faults for different components, and predict future health conditions to calculate remaining useful life (RUL). The model is demonstrated with semisimulated nuclear power plant (NPP) data, with simultaneous condenser pump degradation and condenser tube fouling. The MDMPM shows a reliable prediction of RULs of NPP maintenance-dependent processes with interacting component degradation modes. [ABSTRACT FROM AUTHOR]

• The nanophotonic field has advanced to be used as a method to detect infectious diseases. • Multiplex nanophotonic diagnostics enable rapid and simultaneous detection of viral pathogens. • High sensitivity and specificity of multiplex nanophotonic diagnostics enable real-time identification of viruses without the need for nucleic acid amplification. • Photonic diagnostics such as localized surface plasmon resonance (LSPR), magnetic resonance imaging (MRI), optical coherence tomography (OCT), and photoacoustic imaging (PAI), can be developed as point-of-care diagnostic tools and used as procedures for treating viral diseases. The field of nanophotonics has advanced and can be utilized as a method to detect different infectious diseases. The introduction of multiplex nanophotonic diagnostics has enabled the speedy and simultaneous detection of viral infections and specific biomarkers. The quick reaction times, high sensitivity, and specificity of multiplex nanophotonic diagnostics enable real-time identification of viruses without the need for nucleic acid amplification. This review presents an overview of nanophotonic tools used to identify diseases and particular biomarkers. The paper also examines possible research areas for the development of unique, cutting-edge multiplex nanophotonic diagnostics capable of concurrently detecting various diseases or biomarkers/biomolecules. Furthermore, it discusses barriers to further advancement and offers insight into anticipated trends. [Display omitted] [ABSTRACT FROM AUTHOR]

The paper describes how the use of MIMOSA open source data model supports the development of a low-cost monitoring system that is capable to carry out automatic diagnosis and prognosis. MIMOSA follows the ISO 13374 definitions (condition monitoring) and links well with the ISO 17359 (diagnosis) and ISO 13381 (prognosis). The MIMOSA data model defines all the necessary ontology for the automatic monitoring system. As a use case the paper describes the installation of the MIMOSA data model in a Raspberry where MariaDB is used as the database engine. A low-cost MEMS accelerometer has been installed to a Raspberry thus enabling the collection of vibration data from rolling element bearings of a conveyor. The goal is to compare the results of low cost devices to a more expensive data acquisition system. The necessary signal analysis functions are programmed with VTT O&M Analytics, which provides the ability to conveniently perform signal analysis, offering a comprehensive set of algorithms that can detect a bearing failure and calculate the Remaining Useful Life (RUL). The amplitudes of the bearing fault frequencies can be reliably seen using envelope analysis, and the magnitude of the amplitudes can be used to determine whether the bearing is defective. In addition, the article discusses the system architecture, which enables data transfer from cloud to cloud conveniently and reliably, regardless of the number or location of the clouds. In conclusion, the paper summarises the key role of MIMOSA in building and using this kind of automatic monitoring systems. [ABSTRACT FROM AUTHOR]

Pneumonia is a leading cause of mortality in limited resource settings (LRS), which are common in low- and middle-income countries (LMICs). Accurate referrals can reduce the devastating impact of pneumonia, especially in LRS. Discriminating pneumonia from other respiratory conditions based only on symptoms is a major challenge. Machine learning has shown promise in overcoming the diagnostic difficulties of pneumonia (i.e., low specificity of symptoms, lack of accessible diagnostic tests and varied clinical presentation). Many scientific papers are now focusing on deep-learning methods applied to clinical images, which is unaffordable for initial patient referral in LMICs. The current study used a dataset of 4500 patients (1500 patients affected by bronchitis, 3000 by pneumonia) from a middle-income country, containing information on subject population characteristics, symptoms and laboratory test results. Manual feature selection was performed, focusing on clinical symptoms that are easily measurable in LRS and in community settings. Three common machine learning methods were tested and compared: logistic regression; decision tree and support vector machine. Models were developed through a holdout process of training-validation and testing. We focused on six clinically relevant, easily interpreted patient symptoms as best indicators for pneumonia. Our final model was a decision tree, achieving an AUC of 93%, with the advantage of being fully intelligible and easily interpreted. The performance achieved suggested that intelligible machine learning models can enhance symptom-based referral of pneumonia in LRS and in community settings. [ABSTRACT FROM AUTHOR]

Copyright of Progress in Plant Protection is the property of Institute of Plant Protection and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• Social and biophysical indicators can be integrated for river basin diagnostics. • Indicator condition, target and influence provides strategic management synergies. • Indicator networks are constructed from influence scoring by stakeholder groups. • Indicator networks inform enabling pathways for stakeholder engagement. • Collaborative engagement broadens perspectives for better environmental outcomes. River basin governance has become increasingly challenged in many river basins around the world. At heart is the poor understanding of governance performance and its interaction with biophysical restoration targets. This paper aims to improve the Murray-Darling Basin governance for better environmental outcomes by understanding the interactions between governance performance indicators and biophysical indicators. A diagnostic framework consisting of social indicators (Collaboration , Leadership , Institutions and Learning) and biophysical indicators (River Flows , Water Quality , Biodiversity and Species Recovery) was used to estimate condition and target for each indicator from a range of stakeholder groups representing different interests and perspectives. In addition, stakeholders also measured synergistic effects of indicator progress towards target on other indicators using strength of indicator interaction scores. A network analysis of indicator condition and interaction scores was used to determine a composite enabling pathway to suggest stakeholder contributing roles to polycentric governance. Leadership and its role in regulation, compliance and operating rules is a recurring challenge mentioned by multiple stakeholder groups, requiring clarification of roles and subsidiarity decision-making through enhanced collaboration and institutional reform. The enabling pathway proposes collaborative learning by assigning co-implementation priorities based on diagnostics rather than stakeholder specific identified key challenges. This novel approach offers a tangible way for developing sectorised stakeholder engagement strategies where participation is conceived as active collaboration towards decision-making and co-implementation. [ABSTRACT FROM AUTHOR]

Testicular tumors in prepubertal boys account for 1–2% of all solid pediatric tumors. They have a lower incidence, a different histologic distribution and are more often benign compared to testicular tumors in the adolescent and adult group. This fundamental difference should also lead to a different approach and treatment. To provide a guideline for diagnosis and treatment options in prepubertal boys with a testicular mass. A structured literature search and review for testicular tumors in prepubertal boys was performed. All English abstracts up to the end of 2019 were screened, and relevant papers were obtained to create the guideline. A painless scrotal mass is the most common clinical presentation. For evaluation, high resolution ultrasound has a detection rate of almost 100%, alpha-fetoprotein is a tumor marker, however, is age dependent. Human chorionic gonadotropin (HCG) was not a tumor marker for testis tumors in prepubertal boys. Based on a summary of the literature on prepubertal testis tumors, the 2021 EAU guidelines on Pediatric Urology recommend a partial orchiectomy as the primary approach in tumors with a favorable preoperative ultrasound diagnosis. [ABSTRACT FROM AUTHOR]

The paper presents institutional diagnostics, which is sensitive to dynamic social and political processes ‘producing’ institutions underlying practices in resource management, climate change adaptation, and food security. The paper is based on a qualitative case study on watershed development interventions conducted in two villages in Amhara Region, Ethiopia. The research showed that resource management, adaptation, and food security institutions in Ethiopia are a result of struggles between containment strategies of the Ethiopian state and counter containment strategies of local communities. While the state’s containment institutions allowed it to mobilize a large number of rural residents for its resource management interventions, the counter containment strategies from local communities limited the potential contribution of the interventions for adaptation and food security endeavors of the state. From an institutional diagnostic perspective two conclusions are made, one empirical and another theoretical. The empirical part of the paper concludes that the Ethiopian state is using institutions to contain its population towards state-driven development pathways, which is essential to understand watershed development and state-led natural resource management interventions. The theoretical portion concludes that although institutions are often portrayed as static elements of social life, in fact they are also dynamic, socially produced, and could be coopted by powerful actors. [ABSTRACT FROM AUTHOR]

Measurement of the frequency response, from a few Hz to a few MHz, is now commonly used in the transformer industry for the condition assessment of transformer windings and has demonstrated its sensitivity for detecting various mechanical and electrical failure modes. The present generally applied practice for interpretation is visual comparison of frequency responses, either with a previous measurement on the same or an identical unit, or between the phases of a three-phase transformer. Examples of curve comparison for typical mechanical and electrical failure modes have previously been published in CIGRE and IEEE guides. Over the last 15 years, numerous technical papers have been published regarding the interpretation of the results in an aim to make it more objective and quantitative. In 2016, CIGRE initiated a new working group titled “Objective interpretation methodology for the condition assessment of transformer windings using Frequency Response Analysis (FRA)”. This paper, written on behalf of the new working group, reviews the basics of FRA interpretation and summarizes the state-of-the-art regarding the potential methods that can be applied to achieve a more objective and quantitative interpretation of the results. [ABSTRACT FROM AUTHOR]

Urinary tract infections (UTIs) are common in children and require appropriate diagnostic evaluation, management and follow-up. To provide a summary of the updated European Association of Urology (EAU) guidelines on Pediatric Urology, which were first published in 2015 in European Urology. A structured literature review was performed of new publications between 2015 and 2020 for UTIs in children. The guideline was updated accordingly with relevant new literature. The occurrence of a UTI can be the first indication of anatomical abnormalities in the urinary tract, especially in patients with a febrile UTI. The basic diagnostic evaluation should include sufficient investigations to exclude urinary tract abnormalities, but should also be as minimally invasive as possible. In recent years, more risk factors have been identified to predict the presence of these anatomical anomalies, such as a non-E. Coli infection, high grade fever and ultrasound abnormalities. When these risk factors are factored into the diagnostic work-up, some invasive investigations can be omitted in a larger group of children. In addition to the treatment of active UTIs, it is also essential to prevent recurrent UTIs and consequent renal scarring. With the increase of antimicrobial resistance good antibiotic stewardship is needed. In addition, alternative preventative measures such as dietary supplements, bladder and bowel management and antibiotic prophylaxis could decrease the incidence of recurrent UTI. This paper is a summary of the updated 2021 EAU guidelines on Pediatric Urology. It provides practical considerations and flowcharts for the management and diagnostic evaluation of UTIs in children. [ABSTRACT FROM AUTHOR]

Modern agriculture and plant breeding must continuously meet the high and increasingly growing requirements of consumers and recipients. In this context, one of the conditions for effective management of any farm is access to quick and efficient diagnostics of plant pathogens, the result of which, together with the assessment of experts, provide breeders with tools to effectively reduce the occurrence of plant diseases. This paper presents information about biodiversity and spectrum of endophytic and phytopathogenic bacterial species identified in plant samples delivered to the Plant Disease Clinic in 2013-2019. During the tests, using the Biolog Gen III system, the species affiliation of the majority of detected bacterial strains found in plant tissues as an endophyte and not causing disease symptoms on plants was determined. These data were compiled and compared with the number of found identifications for a given species and data on the pathogenicity of bacterial species towards plants. In this way, valuable information for the scientific community was obtained about the species composition of the bacterial microbiome of the crop plants studied by us, which were confronted with available literature data. In the study, special attention was paid to tomato, which is the plant most often supplied for testing in the Plant Disease Clinic due to its economic importance. [ABSTRACT FROM AUTHOR]

OBJECTIVE: Copy-number variation (CNV) is an important source of genetic diversity in humans. It can cause Mendelian or sporadic traits or be associated with complex diseases by various molecular mechanisms, including gene dosage, gene disruption, gene fusion and position effects. In clinical diagnostics, it is therefore fundamental to be able to identify such variations. The preferred techniques for CNV detection are MLPA, aCGH and qPCR, which have proven to be valuable, and they are complex, costly and require prior knowledge of the region to analyze. CNV calling from NGS data still suffers from data variability. Coverage can vary greatly from one region of the genome to another, depending on many factors like complexity, GC content, repeated regions and many others. In this paper, we describe how we developed a method for CNV detection. MATERIALS AND METHODS: Our method exploits CoNVaDING to detect single- and multiple- exon CNVs in targeted NGS data. RESULTS: We demonstrated that our CNV analysis has 100% specificity and 99.998% sensitivity. We also show how we evaluated the performance of this method based on internal analysis. CONCLUSIONS: The results indicate that the method can be used to screen prior to standard labs technologies, thus reducing the number of analyses, as well as costs, and increasing test conclusiveness. [ABSTRACT FROM AUTHOR]

In this paper, two kinds of dynamic models for shaft misalignment of rotating machinery are proposed for vibration analysis and diagnosis of the shaft misalignment state. In order to obtain the solution of the dynamic models and clarify the vibration signal features measured in the shaft misalignment state, the calculation method of vibration forces caused by misalignments is also shown. The results of computer simulation and experiment using the same rotating machine are shown to verify the efficiency of the dynamic analysis method proposed in this paper. Finally, the method for distinguishing structure faults of rotating machines (shaft misalignment state, unbalance state and looseness state) is discussed by using symptom parameters and spectra of the vibration signal measured in these states. [ABSTRACT FROM AUTHOR]

In this paper, we review hybrid approaches for fault detection and fault diagnosis (FDD) that combine data-driven analysis with physics-based and knowledge-based models to overcome a lack of data and to increase the FDD accuracy. We categorize these hybrid approaches according to the steps of an extended common workflow for FDD. This gives practitioners indications of which kind of hybrid FDD approach they can use in their application. [ABSTRACT FROM AUTHOR]

Faults during machining processes generate negative impacts on productivity, product quality and scrap rate. In recent years, the research of leveraging deep learning algorithms for developing fault diagnostics approaches has been actively conducted. However, the approaches have not been widely adopted by industries yet due to their inadaptability in addressing varying working conditions throughout machining process lifecycles. To overcome the limitation, this paper presents a novel deep transfer learning enabled adaptive diagnostics approach. In the approach, firstly, a Convolutional Neural Network (CNN) is designed to perform diagnostics on machining processes. Then, a transfer learning strategy is incorporated into the CNN to enhance the approach's adaptability for different machining conditions via the following steps: (1) Input datasets from machining conditions are optimally aligned to facilitate cross-domain data reuse; and (2) Weights of the trained CNN are regularized to minimize feature distribution mismatches to implement domain transfer learning. Based on the steps, the CNN can be adaptively applied across the conditions, and thereby re-training processes for the CNN from scratch can be alleviated. The developed approach was validated and benchmarked based on different parameters and settings. In the experiments, comparative results indicate that the approach achieved 94% in accuracy, which was significantly higher than other approaches without transfer learning mechanisms. Peer-review under responsibility of the scientific committee of the 27th CIRP Life Cycle Engineering (LCE) Conference. [ABSTRACT FROM AUTHOR]

The picosecond optical Kerr gate is an effective method for the separation of long-lived laser induced interference from instantaneous Raman scattering. In this paper, an application of the Kerr-gated Raman spectroscopy in combustion environment was demonstrated. The fast gating provided by a Kerr effect based optical shutter extends Raman spectroscopy measurements to sooty flames and flames rich in polycyclic aromatic hydrocarbons. The optical setup of the Kerr gate is described in detail and the gating time, efficiency and spatial uniformity of the ultrafast shutter are reported. To demonstrate the concept, the Kerr-gated Raman spectroscopy was applied in a laminar CH 4 /air diffusion flame to evaluate the capability to extract a Raman spectrum under different interference levels. In the region deemed inaccessible with µs-gated Raman spectroscopy, the laser induced interference was effectively suppressed by the Kerr gate. A test was also conducted in a C 2 H 4 /air premixed flame to explore the limitation of the Kerr-gated Raman technique. The result shows that after combining with polarization separation, Raman spectra can be extracted from locations with 65 ppb soot volume fraction. [ABSTRACT FROM AUTHOR]

Upon representative case studies, the paper illustrates importance of management, maintenance and reconstruction of steel footbridges. Particularly inspection importance for timely discovering imperfections, potentially reducing safety and load carrying capacity are illustrated at effected investigations of actually common structural systems. The paper presents a summary of the findings and conclusions from diagnostic of seven steel footbridges. Focus is paid on the corrosion of structural steel and its influence on overall condition of footbridge structures. [ABSTRACT FROM AUTHOR]

The paper focuses on new methods of Proton Exchange Membrane Fuel Cells diagnostics, which would allow increasing the reliability and stability of fuel cell operation. The paper considers the possibility to improve the diagnostic system used for fuel cell robustness check. Wavelet analysis as a diagnostic tool is introduced, and it is proven that this type of instrument allows acquiring a higher quality of early-stage problem diagnostics in comparison to existing methods. The suggested system assesses the possibility of determining the changes in the fuel cell operating mode on the basis of the analysis of its electrical fluctuations. Both theoretical and practical implications of the suggested diagnostics method are discussed. [ABSTRACT FROM AUTHOR]

The paper dwells upon the possibility of using adaptive algorithms based on fuzzy logic in vibration diagnostic systems. An urgent task of improving diagnostic systems by increasing diagnostic reliability with minimal costs for their testing is given in the paper. The authors prove fuzzy logic rules to be necessary for diagnostic tasks. There are functions of vibration acceleration values at various shaft rotation frequencies. An example of vibration diagnostic and monitoring system operation using fuzzy logic is given. The authors prove that using fuzzy logic-based algorithms allows moving forward to the next stage in vibration diagnostic system development. [ABSTRACT FROM AUTHOR]

The purpose was to systematically review the published reports for the clinical utility of quantitative objective tests commonly used for diagnosing musculoskeletal disorders in hand-arm vibration syndrome (HAVS). Two reviewers independently conducted a computerized literature search in PubMed and Scopus using predefined criteria, and relevant papers were identified. The articles were screened in several stages and considered for final inclusion. Quality of the selected papers was evaluated by a modified QUADAS tool. Relevant data were extracted as necessary. For this review, only 4 relevant studies could be identified for detailed examination. Grip strength, pinch strength, and Purdue pegboard tests were commonly used with their reported sensitivity and specificity ranging between 1.7 to 65.7% and 65.2 to 100%, 1.7 to 40% and 94 to 100%, and 44.8 to 85% and 78 to 95%, respectively. A considerable difference across the studies was observed with respect to patient and control populations, diagnostic performance and cut-off values of different tests. Overall, currently available English-language limited literature do not provide enough evidence in favour of the application of grip strength and pinch strength tests for diagnosing musculoskeletal injuries in HAVS; Purdue pegboard test seems to have some diagnostic value in evaluating impaired dexterity in HAVS. [ABSTRACT FROM AUTHOR]

Centralized monitoring techniques have become more widely used as business demands and budgetary cuts for companies require streamlined operation and maintenance of a company's assets. These assets may be located at a single site where the monitoring is taking place, or they may be located all over a state, country or the world. Local data collection with consolidated servers allows a central maintenance center to pool big data for fleet-wide monitoring purposes. Advanced pattern recognition (APR) software solutions have been on the forefront of managing big data for dealing with a multitude of assets. APR techniques can provide evidence that a machine is not operating as expected, but the condition detected could indicate many possible underlying faults. The root cause may still be unknown. Causal network analysis has been widely used in providing differential diagnosis in the medical field when a set of symptoms are known. This method is based on Bayesian probability which can handle uncertainty in the data, both input and output, and has a good theoretical foundation. This paper discusses methods to utilize pattern anomalies as symptoms for a causal network to diagnose asset conditions and to mitigate failures for predictive maintenance programs. [ABSTRACT FROM AUTHOR]

Copyright of Biotechnologie, Agronomie, Societe et Environnement is the property of Les Presses Agronomiques de Gembloux and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Croatian Journal of Education / Hrvatski Časopis za Odgoj i Obrazovanje is the property of Uciteljski Fakultet u Zagrebu and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract: Aim: The aim of this paper was to present diagnostic methods helping in the recognition of mycosis fungoides (MF) and Sezary syndrome (SS). Background: Mycosis fungoides is the most common form of primary cutaneous T-cell lymphomas. It is characterized by a distinctive long-term course and malignant T-cell proliferation. MF diagnosis is not easy, mainly due to the atypical clinical presentation of the disease at an early stage. Materials and methods: Low specific changes, which can be observed at the histopathological examination. Initially, the skin lesions may resemble psoriasis, atopic dermatitis or chronic eczema. Patients are qualified according to the available, and generally accepted WHO-EORTC classification, based on a combination of clinical and histopathological markers. From a clinical point of view, it is also important to carry out the qualification according to the TNMB assessment, which allows to specify the stage of the disease, and is helpful in the monitoring of the course of disease and therapeutic effects. Results: In this paper we try to present currently available diagnostic methods. Conclusion: Diagnosis of MF and SS still causes many problems due to less characteristic changes in the early stage of disease and requires wide interdisciplinary knowledge. [Copyright &y& Elsevier]

Abstract Anomaly Detection (AD) is a promising new approach for quality control in e.g. operational telecommunications and data networks. In this paper we have applied Supervised Machine Learning (SML) to a set of long term observation time series from a Cellular/Wireless network. We have shown that periodically collected Key Performance Indicators (KPIs) can be analyzed by supervised ML. Generally, the network creates a new big data periodically when different KPIs from e.g. all the cells (sectors of each 2G/3G/4G/5G base station) are output to a remote Database. We have applied a single class support vector machine in the first phase to find out outliers in range based KPI values. Then LSTM RNN (Recurrent Neural Network) is used for deeper understanding of their behavior over time. Both profile based KPIs and range based KPIs are used to filter out the FP (False Positive) or FN (False Negative) anomaly candidates. In this study, we have applied a novel approach to automatically label the huge data into a supervised training set. This is possible when the meaning of major KPIs is well understood. Both a time series profile based prediction and a logical combination of acceptable value ranges (Min/Max) are used for Anomaly Filtering (AF). A Min or a Max condition is omitted in a single threshold case. AF is used both for AD and for automatic labelling of the training set for ML. Automated labelling with AF performed well also for any large dataset. The pure time series graph profile based KPIs without applicable limits were not used for labelling nor for AF. This technique gave us better results than unsupervised learning based AD. Our enhanced supervised AD decreased the number of FP anomalies from 33 to 0, while the total anomalies decreased from 35 uncertain cases to 2 TP (True Positive), 0 FN. Finally, KNN algorithm is used to classify test data sets. Our proposed method seems to solve several major problems in the field of Cellular/ Wireless, Fixed, [Packet (e.g. IP)] Data Networks as well as within related network side and user equipment. Automation in general, including medical/ any critical systems and equipment is another possible application domain. Automated labelling with AF performed well also for any large dataset. [ABSTRACT FROM AUTHOR]

The application of current reverse engineering techniques can be integrated into a modern range of tools that support the development of the economy not only in the engineering industry. The main idea of this paper is to diagnose problems with a non-destructive method using a reverse-engineered device. The obtained data will then be merged into individual views and the obtained cloud of points transformed into spatial views of hard-to-reach locations of machines and equipment. The prototype device is then tested under laboratory conditions and its accuracy is compared with used commercial digitization devices. [ABSTRACT FROM AUTHOR]

Twenty years after publication of the previous version, the new and completely revised version of the US diagnostic system of psychiatric disorders, the Diagnostic and Statistical Manual of Mental Disorders - DSM-5, was introduced in 2013. Over the course of its preparation, which took many years, current knowledge was summarized and the respective consequences for the classification and diagnosis of psychiatric disorders were discussed in groups of high-ranking experts. In many regards, DSM-5 does not fulfil the original plans and expectations; however, for the most part it can be seen as an acceptable summary of current knowledge on mental disorders. Although universities and research groups throughout the world have shown great interest in DSM-5, in countries outside the USA the ICD-10 is still the mandatory classification system, until the official introduction of the ICD-11. From many perspectives, DSM-5 can be seen as a kind of predecessor model for ICD-11. However, ICD-11, despite the original plan for it to be very similar to DSM-5, will find many of its own solutions. This paper presents some important issues on DSM-5 that will be of interest to clinicians working in the field of mental disorders. In addition, it uses some examples to illustrate how DSM-5 operationally defines some disorders and how it differs from ICD-10 and DSM-IV. [ABSTRACT FROM AUTHOR]

Routing security has a great importance to the security of Mobile Ad Hoc Networks (MANETs). There are various kinds of attacks when establishing the routing path between the source and destination. The adversaries attempt to deceive the source node and get the privilege of data transmission. Then, they try to launch the malicious behaviors such as passive or active attacks. Due to the characteristics of the MANETs, e.g. dynamic topology, open medium, distributed cooperation, and constrained capability, it is difficult to verify the behaviors of nodes and detect malicious nodes without revealing any privacy. In this paper, we present PVad, an approach conducting privacy-preserving verification in the routing discovery phase of MANETs. PVad tries to find the existing communication rules via the association rules instead of making the rules. PVad consists of two phases, a reasoning phase deducing the expected log data of the peers, and a verification phase using a Merkle Hash Tree to verify the correctness of the derived information without revealing any privacy of nodes on the expected routing paths. Without deploying any special nodes to assist the verification, PVad can detect multiple malicious nodes by itself. To show that our approach can be used to guarantee the security of the MANETs, we conducted our experiments in NS3 as well as the real router environment, with the improvement of the detection accuracy by 4% on average compared to our former work. [ABSTRACT FROM AUTHOR]

If the control system besides the standard control functions also realizes the functions (known as safety functions), failures of which can influence safety of the controlled process, then the control system may be a source of risk for assets, that are within the scope of the controlled process. Early detection of these failures and subsequent negation of their effects can have a significant influence on the safety integrity level of the safety function and thus also on the elimination of risks related to the controlled process. Therefore, the diagnostics is the means which, if appropriately applied, can increase not only the availability, but also the safety of the control system. The paper deals with using the homogeneous Markov chains to influence the evaluation of on-line diagnostics on the hardware safety integrity of the safety function, depending on the application method of several simultaneously operating diagnostics mechanisms and their basic parameters -- the failures diagnostic coverage coefficient and the failure diagnostics time. [ABSTRACT FROM AUTHOR]

Although policymakers and scientists are increasingly embracing the food system perspective, it has been poorly reflected in institutional terms. We aim to fill this gap by addressing the question as to what forms of governance are most appropriate to govern food systems in a more holistic way. The article presents a diagnostic framework consisting of five principles: 1) system-based problem framing to deal with interlinked issues, drivers and feedback loops; 2) connectivity across boundaries to span siloed governance structures and include non-state actors; 3) adaptability to flexibly respond to inherent uncertainties and volatility; 4) inclusiveness to facilitate support and legitimacy; and 5) transformative capacity to overcome path dependencies and create adequate conditions to foster structural change. This framework is used to analyse the strengths and weaknesses of three food governance arrangements in South Africa, each of which deliberately aimed to embrace a holistic perspective. Although promising on paper, the outcomes are disappointing because of a reversion to a technical onedimensional problem framing during the implementation, the dominance of single departments, the limited attention to monitoring and flexible responses and the exclusion of those most affected by food insecurity. We conclude that the tensions between the ambitious objectives of the arrangements and the institutional constraints of implementing them can persist because of inadequate resources to facilitate transformative change. Finally, we propose an agenda to further elaborate the framework and improve its practical usefulness. [ABSTRACT FROM AUTHOR]

Blastocystis hominis (stramenopila), an absolute anaerobic organism, has been considered a commensal of human large intestine, as even its massive invasion usually is asymptomatic. Nowadays, Blastocystis spp. has been mentioned more frequently as a potential etiologic agent of a chronic diarrhea, mainly among immunocompromised individuals, but also among patients with functional bowel disorders, malnourished, with chronic disease, or after organs transplantations. Blastocystis may also be responsible for traveler's diarrhea. Gastrointestinal disorders are quite common problems for individuals returning from different climatic and sanitary areas -- countries located mainly in the tropics. Majority of such cases are patients suffering from self-limiting gastrointestinal tract. However, some patients, especially those who do not respond to empirical treatment in the primary health care, require more specialized diagnostics; this applies to the group of patients with prolonged diarrhea. Usually, they are caused by Escherichia coli infection. In this paper, we describe two travelers from Africa and India with prolonged diarrhea. The parasitic stool evaluation revealed massive Blastocystis hominis infestation. It was the reason to provide further diagnostics for acquired immunodeficiency, which gave the final diagnosis of HIV infection. [ABSTRACT FROM AUTHOR]

This paper proposes a fault tolerant control method for condenser fouling in water-cooled chillers. Condenser fouling is one of the common faults in water-cooled chillers that degrade its energy efficiency. Studies were conducted to develop fault detection and diagnostics (FDD) algorithms to address this issue. However, the current FDD techniques are too expensive for some faults that are not very significant such as condenser fouling, and a cheaper alternative may be needed to remove the fault impact. This study addresses this issue by proposing a control method without FDD algorithms to reduce the impact of condenser fouling in water-cooled chillers. It controls the opening of the balancing valve in the condensing water loop to adapt the chiller plant for the effectively smaller condenser. Verification of the control method is conducted by a model of a real chiller plant and comparison with the ordinary operation case is made under two different months and two fouling levels. The results show that the method can help the chiller plant to reduce the impact of condenser fouling by reducing its electricity consumption by 2.0% under normal situation and reducing the impact of fouling by 0.2 to 0.4%. However, the result is not very significant, and more studies with other control inputs are needed for more effective control methods. [ABSTRACT FROM AUTHOR]

This paper proposes a method for laboratory experiments, which allows controlling the technical state of high pressure hoses of hydraulic drives on the basis of the derived dependences of logarithmic decrement on operating time. Based on the developed method, the test bench for the study of the elastic properties of the hydraulic drive elements was designed. During the experiments, we obtained the dependences for high pressure hoses with different lengths and diameters. The obtained results have shown the sensitivity and sufficiency of the parameter “logarithmic decrement” for controlling the efficiency of the high-pressure hoses. The developed method can be used to determine the residual lifetime of HPHs during maintenance or repair of the machinery. [ABSTRACT FROM AUTHOR]

In the introduction of the paper the unconventional type of railway superstructure (so called ballastless track) is briefly characterized from the point of view of its historical development. Its advantages and disadvantages related to the classic railway superstructure (rail skeleton placed in gravel railbed) are also stated here. In the further parts of this paper the built-in system of ballastless track RHEDA 2000® is characterized, using the example of the first application of the unconventional railway superstructure in the conditions of ŽSR, related to the construction of the tunnel Turecký vrch in the modernised V. multimodal corridor (line section Nové Mesto nad Váhom-Púchov). The conclusion states the methods of diagnostics of ballastless track, transitional areas and connecting sections with classic railway superstructure. This diagnostics is a part of a scientific project of experimental monitoring of the geometrical position of the track in the ongoing warranty period of the respective modernised line section; realized for the needs of DG of ŽSR by the workers of the Department of Railway Engineering and Track Management in cooperation with the Department of Geodesy of the Faculty of Civil Engineering of the University of Žilina. [ABSTRACT FROM AUTHOR]

Copyright of Technologies of Computer Control is the property of RTU Publishing House and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract: The financial and economic activities of the company are exposed to adverse external and internal factors. Signs of adverse influences are the: decrease of liquidity, loss of profitability, financial instability, increase of costs, to name a few. This paper demonstrates the financial analysis and diagnosis of the financial condition of the company by applying the improved normative model of diagnostics. Furthermore, the paper analyses each factor that impacts enterprise's activities and derives the possible perspectives of the company under study through econometric models. [Copyright &y& Elsevier]

Abstract: In this paper, a steel structure of a motor vehicle hydrodynamic clutch (HDC) is analyzed in order to obtain the optimal design parameters. Realization of such a steel structure with those parameters enables achieving of the optimal efficiency coefficient. The paper presents large number of data, originated from the past research, which may be used in determination of the meridian cross-section shapes and the position of blade circuits within the operating space of the hydraulic turbo converter. The experimental results concerning the influence of the geometric, kinematic and hydrodynamic parameters on characteristics of the hydraulic turbo converter are presented also. The knowledge about all those relations may be very useful to all dealing with hydraulic torque converters. For conducting experimental investigations an indirect measuring method was used, based on defining the pressure distribution over the speed probes’ walls. The original experimental equipment was developed for that purpose. [Copyright &y& Elsevier]

Railway turnout systems are one of the most critical pieces of equipment in railway infrastructure. Early identification of failures in turnout systems is important to obtain increased availability and safety, and reduced operating and support costs. This paper aims to develop a method to identify 'drive-rod out-of-adjustment' failure mode, one of the most frequently observed failure modes. Support Vector Machine (SVM) with Gaussian kernel is used for diagnosis. In addition, the results of feature selection with statistical t-test and feature reduction with principal component analysis (PCA) are compared in the paper [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we consider the problem of parameter estimation in an air brake system. In an air brake system, the pressure of air in the brake chamber and the displacement of the pushrod and their derivatives form a set of states that characterize the system. The position of a valve or mass flow rate of air is an input and the pressure is the measured variable or the output. The pressure acting on the pushrod of the brake chamber causes motion, and the mode in which the system operates depends on the displacement of the pushrod. The mode-dependent nature of the system is a result of different sets of spring compliances associated with the piston in different ranges of its displacement. The mode to mode transition in the air brake system is governed by a parameter which is the clearance between the brake pads and the drum. The clearance between the brake pads and the drum can vary due to a variety of factors — for example, brake pad wear or brake fade. In these applications, characterizing the transition from one mode to another requires a lot of constitutive assumptions, and it can be difficult to calibrate the parameters associated with the constitutive assumptions. We therefore treat the air brake system as a system in which the parameter governing the transition from one mode to another (clearance between the brake pads and the drum) is not known exactly. Clearly, this parameter dictates the time delay and lag between the command and delivery of the brake torque at the wheels and affects the stopping distance of the vehicles considerably. The problem of identification considered in this paper is as follows. Suppose that the pressure of the fluid were to be measured and that the motion of the piston is not measured. Is it possible to estimate the final displacement of the piston without knowing the parameters that govern the system to transition from one mode to another? [Copyright &y& Elsevier]

Copyright of Automatika: Journal for Control, Measurement, Electronics, Computing & Communications is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the paper an intelligent monitoring system of local water supply system is described. The main task of this system concerns water leakages detecting. For inputs, this system uses information from few pressure or flow sensors, mounted on the pipeline network, the output is a piece of information about leakage detection and localization. A heuristic model of water supply network makes the main part of intelligent diagnostic system. The model was built with the use of artificial neural networks. This paper presents the structure and optimization of a heuristic model. The authors took advantage of methods of artificial intelligence and methods known from model-based process diagnostics to increase the accuracy with which system detects of water leakages. [ABSTRACT FROM AUTHOR]

Bearing condition monitoring has been the focus of a wide range of studies over the past years. Current monitoring techniques that focus on the identification of faults present in a bearing have various limitations. Typically they are applicable only under well-defined, specific and precalibrated operating conditions, thereby preventing continuous monitoring of a system operating in a variant environment. They are often limited in damage severity estimation and prognostic capability. This, in turn, prevents the development of optimal maintenance scheduling in favour of overall system safety and productivity. Research presented in this paper has yielded results that have extended bearing diagnostics and prognostics to address these limitations and to achieve optimal machinery maintenance scheduling. This paper discusses the current research status on the development of a new signal processing method with noise cancellation capability to provide early defect detection, the establishment of a diagnostic model to estimate bearing defect severity under variant conditions and the formulation of an adaptively tuned defect propagation model to track the time-variant nature of defect growth for the forecasting of bearing remaining utility. [ABSTRACT FROM AUTHOR]

Gas path diagnostics is one of the most effective condition monitoring techniques in supporting condition-based maintenance of gas turbines and improving availability and reducing maintenance costs of the engines. The techniques can be applied to the health monitoring of different gas path components and also gas path measurement sensors. One of the most important measurement sensors is that for the engine control, also called the power setting sensor, which is used by the engine control system to control the operation of gas turbine engines. In most of the published research so far, it is rarely mentioned that faults in such sensors have been tackled in either engine control or condition monitoring. The reality is that if such a sensor degrades and has a noticeable bias, it will result in a shift in engine operating condition and misleading diagnostic results.In this paper, the phenomenon of a power-setting sensor fault has been discussed and a gas path diagnostic method based on a Genetic Algorithm (GA) has been proposed for the detection of power-setting sensor fault with and without the existence of engine component degradation and other gas path sensor faults. The developed method has been applied to the diagnostic analysis of a model aero turbofan engine in several case studies. The results show that the GA-based diagnostic method is able to detect and quantify the power-setting sensor fault effectively with the existence of single engine component degradation and single gas path sensor fault. An exceptional situation is that the power-setting sensor fault may not be distinguished from a component fault if both faults have the same fault signature. In addition, the measurement noise has small impact on prediction accuracy. As the GA-based method is computationally slow, it is only recommended for off-line applications. The introduced GA-based diagnostic method is generic so it can be applied to different gas turbine engines. [ABSTRACT FROM PUBLISHER]