Oxidative stress is a consequence of normal and abnormal cellular metabolism and is linked to the development of human diseases. The effective functioning of the pathway responding to oxidative stress protects the cellular DNA against oxidative damage; conversely the failure of the oxidative stress response mechanism can induce aberrant cellular behavior leading to diseases such as neurodegenerative disorders and cancer. Thus, understanding the normal signaling present in oxidative stress response pathways and determining possible signaling alterations leading to disease could provide us with useful pointers for therapeutic purposes. Using knowledge of oxidative stress response pathways from the literature, we developed a Boolean network model whose simulated behavior is consistent with earlier experimental observations from the literature. Concatenating the oxidative stress response pathways with the PI3-Kinase-Akt pathway, the oxidative stress is linked to the phenotype of apoptosis, once again through a Boolean network model. Furthermore, we present an approach for pinpointing possible fault locations by using temporal variations in the oxidative stress input and observing the resulting deviations in the apoptotic signature from the normally predicted pathway. Such an approach could potentially form the basis for designing more effective combination therapies against complex diseases such as cancer.
In this paper, we have developed a Boolean network model for the oxidative stress response. This model was developed based on pathway information from the current literature pertaining to oxidative stress. Where applicable, the behaviour predicted by the model is in agreement with experimental observations from the published literature. We have also linked the oxidative stress response to the phenomenon of apoptosis via the PI3k/Akt pathway.
It is our hope that some of the additional predictions here, such as those pertaining to the oscillatory behaviour of certain genes in the presence of oxidative stress, will be experimentally validated in the near future. Of course, it should be pointed out that the theoretical procedure presented here for pinpointing fault locations in a biological network with feedback will need to be further simplified before it can be even considered for practical biological validation.
Combining a simplified on-board turbo-shaft model with sensor fault diagnostic logic, a model-based sensor fault diagnosis method is proposed. The existing fault diagnosis method for turbo-shaft engine key sensors is mainly based on a double redundancies technique, and this can't be satisfied in some occasions as lack of judgment. The simplified on-board model provides the analytical third channel against which the dual channel measurements are compared, while the hardware redundancy will increase the structure complexity and weight. The simplified turbo-shaft model contains the gas generator model and the power turbine model with loads, this is built up via dynamic parameters method. Sensor fault detection, diagnosis (FDD) logic is designed, and two types of sensor failures, such as the step faults and the drift faults, are simulated. When the discrepancy among the triplex channels exceeds a tolerance level, the fault diagnosis logic determines the cause of the difference. Through this approach, the sensor fault diagnosis system achieves the objectives of anomaly detection, sensor fault diagnosis and redundancy recovery. Finally, experiments on this method are carried out on a turbo-shaft engine, and two types of faults under different channel combinations are presented. The experimental results show that the proposed method for sensor fault diagnostics is efficient.
turbo-shaft engine; fault diagnosis; gas turbine sensor; simplified on-board model; dynamic parameters
In this paper, a fiber optic based sensor capable of fault detection in both radial and network overhead transmission power line systems is investigated. Bragg wavelength shift is used to measure the fault current and detect fault in power systems. Magnetic fields generated by currents in the overhead transmission lines cause a strain in magnetostrictive material which is then detected by Fiber Bragg Grating (FBG). The Fiber Bragg interrogator senses the reflected FBG signals, and the Bragg wavelength shift is calculated and the signals are processed. A broadband light source in the control room scans the shift in the reflected signal. Any surge in the magnetic field relates to an increased fault current at a certain location. Also, fault location can be precisely defined with an artificial neural network (ANN) algorithm. This algorithm can be easily coordinated with other protective devices. It is shown that the faults in the overhead transmission line cause a detectable wavelength shift on the reflected signal of FBG and can be used to detect and classify different kind of faults. The proposed method has been extensively tested by simulation and results confirm that the proposed scheme is able to detect different kinds of fault in both radial and network system.
current measurement; current transformers; optical fiber; magnetostrictive devices; power system protection
Wireless Sensor Networks (WSN) currently represent the best candidate to be adopted as the communication solution for the last mile connection in process control and monitoring applications in industrial environments. Most of these applications have stringent dependability (reliability and availability) requirements, as a system failure may result in economic losses, put people in danger or lead to environmental damages. Among the different type of faults that can lead to a system failure, permanent faults on network devices have a major impact. They can hamper communications over long periods of time and consequently disturb, or even disable, control algorithms. The lack of a structured approach enabling the evaluation of permanent faults, prevents system designers to optimize decisions that minimize these occurrences. In this work we propose a methodology based on an automatic generation of a fault tree to evaluate the reliability and availability of Wireless Sensor Networks, when permanent faults occur on network devices. The proposal supports any topology, different levels of redundancy, network reconfigurations, criticality of devices and arbitrary failure conditions. The proposed methodology is particularly suitable for the design and validation of Wireless Sensor Networks when trying to optimize its reliability and availability requirements.
dependability evaluation; wireless sensor networks; fault tree analysis; WirelessHART; ISA 100.11a
The condition of locomotive bearings, which are essential components in trains, is crucial to train safety. The Doppler effect significantly distorts acoustic signals during high movement speeds, substantially increasing the difficulty of monitoring locomotive bearings online. In this study, a new Doppler transient model based on the acoustic theory and the Laplace wavelet is presented for the identification of fault-related impact intervals embedded in acoustic signals. An envelope spectrum correlation assessment is conducted between the transient model and the real fault signal in the frequency domain to optimize the model parameters. The proposed method can identify the parameters used for simulated transients (periods in simulated transients) from acoustic signals. Thus, localized bearing faults can be detected successfully based on identified parameters, particularly period intervals. The performance of the proposed method is tested on a simulated signal suffering from the Doppler effect. Besides, the proposed method is used to analyze real acoustic signals of locomotive bearings with inner race and outer race faults, respectively. The results confirm that the periods between the transients, which represent locomotive bearing fault characteristics, can be detected successfully.
Doppler transient model; locomotive bearings; spectrum correlation assessment; Laplace wavelet; fault diagnosis
Given the problems in intelligent gearbox diagnosis methods, it is difficult to obtain the desired information and a large enough sample size to study; therefore, we propose the application of various methods for gearbox fault diagnosis, including wavelet lifting, a support vector machine (SVM) and rule-based reasoning (RBR). In a complex field environment, it is less likely for machines to have the same fault; moreover, the fault features can also vary. Therefore, a SVM could be used for the initial diagnosis. First, gearbox vibration signals were processed with wavelet packet decomposition, and the signal energy coefficients of each frequency band were extracted and used as input feature vectors in SVM for normal and faulty pattern recognition. Second, precision analysis using wavelet lifting could successfully filter out the noisy signals while maintaining the impulse characteristics of the fault; thus effectively extracting the fault frequency of the machine. Lastly, the knowledge base was built based on the field rules summarized by experts to identify the detailed fault type. Results have shown that SVM is a powerful tool to accomplish gearbox fault pattern recognition when the sample size is small, whereas the wavelet lifting scheme can effectively extract fault features, and rule-based reasoning can be used to identify the detailed fault type. Therefore, a method that combines SVM, wavelet lifting and rule-based reasoning ensures effective gearbox fault diagnosis.
gearbox; support vector machines (SVM); wavelet lifting; rule-based reasoning (RBR); intelligent diagnosis
Sensor fault diagnosis is necessary to ensure the normal operation of a gas turbine system. However, the existing methods require too many resources and this need can’t be satisfied in some occasions. Since the sensor readings are directly affected by sensor state, sensor fault diagnosis can be performed by extracting features of the measured signals. This paper proposes a novel fault diagnosis method for sensors based on wavelet entropy. Based on the wavelet theory, wavelet decomposition is utilized to decompose the signal in different scales. Then the instantaneous wavelet energy entropy (IWEE) and instantaneous wavelet singular entropy (IWSE) are defined based on the previous wavelet entropy theory. Subsequently, a fault diagnosis method for gas turbine sensors is proposed based on the results of a numerically simulated example. Then, experiments on this method are carried out on a real micro gas turbine engine. In the experiment, four types of faults with different magnitudes are presented. The experimental results show that the proposed method for sensor fault diagnosis is efficient.
fault diagnosis; wavelet entropy; wavelet decomposition; gas turbine sensor
Analysis of transient fluid pressure signals has been investigated as an alternative method of fault detection in pipeline systems and has shown promise in both laboratory and field trials. The advantage of the method is that it can potentially provide a fast and cost effective means of locating faults such as leaks, blockages and pipeline wall degradation within a pipeline while the system remains fully operational. The only requirement is that high speed pressure sensors are placed in contact with the fluid. Further development of the method requires detailed numerical models and enhanced understanding of transient flow within a pipeline where variations in pipeline condition and geometry occur. One such variation commonly encountered is the degradation or thinning of pipe walls, which can increase the susceptible of a pipeline to leak development. This paper aims to improve transient-based fault detection methods by investigating how changes in pipe wall thickness will affect the transient behaviour of a system; this is done through the analysis of laboratory experiments. The laboratory experiments are carried out on a stainless steel pipeline of constant outside diameter, into which a pipe section of variable wall thickness is inserted. In order to detect the location and severity of these changes in wall conditions within the laboratory system an inverse transient analysis procedure is employed which considers independent variations in wavespeed and diameter. Inverse transient analyses are carried out using a genetic algorithm optimisation routine to match the response from a one-dimensional method of characteristics transient model to the experimental time domain pressure responses. The accuracy of the detection technique is evaluated and benefits associated with various simplifying assumptions and simulation run times are investigated. It is found that for the case investigated, changes in the wavespeed and nominal diameter of the pipeline are both important to the accuracy of the inverse analysis procedure and can be used to differentiate the observed transient behaviour caused by changes in wall thickness from that caused by other known faults such as leaks. Further application of the method to real pipelines is discussed.
transient; pipelines; water hammer; wall thickness; wavespeed; deterioration
This paper investigates the use of wireless sensor networks for multiple event source localization using binary information from the sensor nodes. The events could continually emit signals whose strength is attenuated inversely proportional to the distance from the source. In this context, faults occur due to various reasons and are manifested when a node reports a wrong decision. In order to reduce the impact of node faults on the accuracy of multiple event localization, we introduce a trust index model to evaluate the fidelity of information which the nodes report and use in the event detection process, and propose the Trust Index based Subtract on Negative Add on Positive (TISNAP) localization algorithm, which reduces the impact of faulty nodes on the event localization by decreasing their trust index, to improve the accuracy of event localization and performance of fault tolerance for multiple event source localization. The algorithm includes three phases: first, the sink identifies the cluster nodes to determine the number of events occurred in the entire region by analyzing the binary data reported by all nodes; then, it constructs the likelihood matrix related to the cluster nodes and estimates the location of all events according to the alarmed status and trust index of the nodes around the cluster nodes. Finally, the sink updates the trust index of all nodes according to the fidelity of their information in the previous reporting cycle. The algorithm improves the accuracy of localization and performance of fault tolerance in multiple event source localization. The experiment results show that when the probability of node fault is close to 50%, the algorithm can still accurately determine the number of the events and have better accuracy of localization compared with other algorithms.
trust index; binary data; multiple event localization; fault tolerance; maximum likelihood estimation; wireless sensor networks
This review article on the beneficial uses of Allium antioxidants tries to give some answers to the recent doubts raised by Singh et al. (Ind J Clin Biochem 25(3):225–243, 2010) against the claim of some researchers that Antioxidants (AOs) are miraculous molecules. Many people still believe that vitamins like A, C and E are the only true AOs that play important role in the corrections of metabolic derangements in life style diseases and hence all their faults are attributed to the failures of AOs as a class. This is quite unfair as there are many other natural AOs that do equal or even better AO action than the vitamins. Such is the case with the Allium S-alkyl sulfoxide aminoacids and their breakdown products viz, the various poly sulfides and their oxides e.g. allicin and ajoene type compounds which trap electrons mainly. It is true that antioxidant vitamins and β-carotene a precursor of Vitamin A bring about problems as prooxidant or as agents that block some metabolic pathways and gene expression. Again the argument that AOs cannot improve the level of antioxidant enzymes like SOD, catalase and glutathione Px is also not universal. Actually allium AOs can even spare the use of antioxidant vitamins in the body and enhance the action of antioxidant enzymes and supply of ATP and other nutrients to the tissues as the former are good vasodialators and promoters of membrane permeability. The use of AOs should be selective and moderate. Allium AOs satisfy the role of ideal AOs based on many of their invivo and invitro actions reported by the author and others. Their metabolits can regenerate them and recycle them for a sufficient time in the body. They have non antioxidant effects also such as antiplatelet, fibrinolytic, antiinflammatory, immunomodulatory, antiageing actions etc. Plant derived AOs may be more beneficial and better tolerated in their partially purified forms rather than in their absolutely purified forms as the accompanying principles have some protective and regulatory effects in general. This and other aspects of allium AOs are discussed in the paper.
Ideal antioxidants; Allium disulfides and their oxides; Vitamins C & E; β-Carotene; Non antioxidant effects; ROS
The diesel engine is the main power source for most agricultural vehicles. The control of diesel engine emissions is an important global issue. Fuel injection control systems directly affect fuel efficiency and emissions of diesel engines. Deterioration faults, such as rack deformation, solenoid valve failure, and rack-travel sensor malfunction, are possibly in the fuel injection module of electronic diesel control (EDC) systems. Among these faults, solenoid valve failure is most likely to occur for in-use diesel engines. According to the previous studies, this failure is a result of the wear of the plunger and sleeve, based on a long period of usage, lubricant degradation, or engine overheating. Due to the difficulty in identifying solenoid valve deterioration, this study focuses on developing a sensor identification algorithm that can clearly classify the usability of the solenoid valve, without disassembling the fuel pump of an EDC system for in-use agricultural vehicles. A diagnostic algorithm is proposed, including a feedback controller, a parameter identifier, a linear variable differential transformer (LVDT) sensor, and a neural network classifier. Experimental results show that the proposed algorithm can accurately identify the usability of solenoid valves.
solenoid valve; diesel engine; fault detection; LVDT sensor
A least square method based on data fitting is proposed to construct a new lifting wavelet, together with the nonlinear idea and redundant algorithm, the adaptive redundant lifting transform based on fitting is firstly stated in this paper. By variable combination selections of basis function, sample number and dimension of basis function, a total of nine wavelets with different characteristics are constructed, which are respectively adopted to perform redundant lifting wavelet transforms on low-frequency approximate signals at each layer. Then the normalized lP norms of the new node-signal obtained through decomposition are calculated to adaptively determine the optimal wavelet for the decomposed approximate signal. Next, the original signal is taken for subsection power spectrum analysis to choose the node-signal for single branch reconstruction and demodulation. Experiment signals and engineering signals are respectively used to verify the above method and the results show that bearing faults can be diagnosed more effectively by the method presented here than by both spectrum analysis and demodulation analysis. Meanwhile, compared with the symmetrical wavelets constructed with Lagrange interpolation algorithm, the asymmetrical wavelets constructed based on data fitting are more suitable in feature extraction of fault signal of roller bearings.
data fitting; lifting wavelet construction; adaptive; roller bearings; feature extraction
The p53 tumor suppressor plays a pivotal role by controlling virtually all processes in the cell. The functions of p53 determine modes of behavior of cells in multicellular organisms and ensure priorities of interests of the organism as a whole above the interests of an individual cell. Multiple signaling pathways of the cell report signals modifying the activities of p53 through numerous connections, ensuring highly selective and gradual regulation of functions that depend on the ongoing events in the cell. The task of p53 is to control the integrity and correctness of all processes in each individual cell and in the organism as a whole. The changes in the activity of p53 depend on the degree of errors or faults, and the effect is directed either toward correction of an imbalance or damage, or, in case of severe damages, leads to the prevention of multiplication of abnormal cells or their death. The strategy of p53 ensures genetic identity of cells and prevents the selection of cells having growth or other advantages. By accomplishing these strategic tasks, p53 may use a wide spectrum of activities. The majority of the activities are due to the ability of p53 to function as a transcription factor, by inducing or repressing different genes. However, p53 can also function as an enzyme, acting as an exonuclease during DNA reparation, or as an adaptor or a regulatory protein, intervening into functions of numerous signaling pathways. It can also act as direct inducer of apoptosis by translocation into mitochondria. Loss of function of the p53 gene occurs in virtually every case of cancer, and deficiency in p53 is an unavoidable prerequisite to the development of malignancies. The functions of p53 play substantial roles in many other pathologies as well as in the aging process. This review is focused on strategies of the p53 gene, demonstrating individual mechanisms underlying its functions.
The p53 tumor suppressor plays a pivotal role in multicellular organism by enforcing benefits of the organism over those of an individual cell. The task of p53 is to control the integrity and correctness of all processes in each individual cell and in the organism as a whole. Information about the state of ongoing events in the cell is gathered through multiple signaling pathways that convey signals modifying activities of p53. Changes in the activities depend on the character of damages or deviations from optimum in processes, and the activity of p53 changes depending on the degree of the aberration, which results in either stimulation of repair processes and protective mechanisms, or the cessation of further cell divisions and the induction of programmed cell death. The strategy of p53 ensures genetic identity of cells and prevents the selection of abnormal cells. By accomplishing these strategic tasks, p53 may use a wide spectrum of activities, such as its ability to function as a transcription factor, by inducing or repressing different genes, or as an enzyme, by acting as an exonuclease during DNA reparation, or as an adaptor or a regulatory protein, intervening into functions of numerous signaling pathways. Loss of function of the p53 gene occurs in virtually every case of cancer, and deficiency in p53 is an unavoidable prerequisite to the development of malignancies. The functions of p53 play substantial roles in many other pathologies as well as in the aging process. This review is focused on strategies of the p53 gene, demonstrating individual mechanisms underlying its functions.
genetic stability; tumor suppressors; apoptosis; carcinogenesis; aging; cell growth regulation
Bearings are not only the most important element but also a common source of failures in rotary machinery. Bearing fault prognosis technology has been receiving more and more attention recently, in particular because it plays an increasingly important role in avoiding the occurrence of accidents. Therein, fault feature extraction (FFE) of bearing accelerometer sensor signals is essential to highlight representative features of bearing conditions for machinery fault diagnosis and prognosis. This paper proposes a spectral regression (SR)-based approach for fault feature extraction from original features including time, frequency and time-frequency domain features of bearing accelerometer sensor signals. SR is a novel regression framework for efficient regularized subspace learning and feature extraction technology, and it uses the least squares method to obtain the best projection direction, rather than computing the density matrix of features, so it also has the advantage in dimensionality reduction. The effectiveness of the SR-based method is validated experimentally by applying the acquired vibration signals data to bearings. The experimental results indicate that SR can reduce the computation cost and preserve more structure information about different bearing faults and severities, and it is demonstrated that the proposed feature extraction scheme has an advantage over other similar approaches.
feature extraction; spectral regression; bearing accelerometer sensor; fault diagnosis; fault prognosis
Induction motors fed through variable speed drives (VSD) are widely used in different industrial processes. Nowadays, the industry demands the integration of smart sensors to improve the fault detection in order to reduce cost, maintenance and power consumption. Induction motors can develop one or more faults at the same time that can be produce severe damages. The combined fault identification in induction motors is a demanding task, but it has been rarely considered in spite of being a common situation, because it is difficult to identify two or more faults simultaneously. This work presents a smart sensor for online detection of simple and multiple-combined faults in induction motors fed through a VSD in a wide frequency range covering low frequencies from 3 Hz and high frequencies up to 60 Hz based on a primary sensor being a commercially available current clamp or a hall-effect sensor. The proposed smart sensor implements a methodology based on the fast Fourier transform (FFT), RMS calculation and artificial neural networks (ANN), which are processed online using digital hardware signal processing based on field programmable gate array (FPGA).
smart sensor; induction motors; multiple-combined faults; VSD; FPGA
Structural faults, such as unbalance, misalignment and looseness, etc., often occur in the shafts of rotating machinery. These faults may cause serious machine accidents and lead to great production losses. This paper proposes an intelligent method for diagnosing structural faults of rotating machinery using ant colony optimization (ACO) and relative ratio symptom parameters (RRSPs) in order to detect faults and distinguish fault types at an early stage. New symptom parameters called “relative ratio symptom parameters” are defined for reflecting the features of vibration signals measured in each state. Synthetic detection index (SDI) using statistical theory has also been defined to evaluate the applicability of the RRSPs. The SDI can be used to indicate the fitness of a RRSP for ACO. Lastly, this paper also compares the proposed method with the conventional neural networks (NN) method. Practical examples of fault diagnosis for a centrifugal fan are provided to verify the effectiveness of the proposed method. The verification results show that the structural faults often occurring in the centrifugal fan, such as unbalance, misalignment and looseness states are effectively identified by the proposed method, while these faults are difficult to detect using conventional neural networks.
rotating machinery; structural fault; relative ratio symptom parameter; ant colony optimization
A Similarity Ratio Analysis (SRA) method is proposed for early-stage Fault Detection (FD) in plasma etching processes using real-time Optical Emission Spectrometer (OES) data as input. The SRA method can help to realise a highly precise control system by detecting abnormal etch-rate faults in real-time during an etching process. The method processes spectrum scans at successive time points and uses a windowing mechanism over the time series to alleviate problems with timing uncertainties due to process shift from one process run to another. A SRA library is first built to capture features of a healthy etching process. By comparing with the SRA library, a Similarity Ratio (SR) statistic is then calculated for each spectrum scan as the monitored process progresses. A fault detection mechanism, named 3-Warning-1-Alarm (3W1A), takes the SR values as inputs and triggers a system alarm when certain conditions are satisfied. This design reduces the chance of false alarm, and provides a reliable fault reporting service. The SRA method is demonstrated on a real semiconductor manufacturing dataset. The effectiveness of SRA-based fault detection is evaluated using a time-series SR test and also using a post-process SR test. The time-series SR provides an early-stage fault detection service, so less energy and materials will be wasted by faulty processing. The post-process SR provides a fault detection service with higher reliability than the time-series SR, but with fault testing conducted only after each process run completes.
Effective debugging of ontologies is an important prerequisite for their broad application, especially in areas that rely on everyday users to create and maintain knowledge bases, such as the Semantic Web. In such systems ontologies capture formalized vocabularies of terms shared by its users. However in many cases users have different local views of the domain, i.e. of the context in which a given term is used. Inappropriate usage of terms together with natural complications when formulating and understanding logical descriptions may result in faulty ontologies. Recent ontology debugging approaches use diagnosis methods to identify causes of the faults. In most debugging scenarios these methods return many alternative diagnoses, thus placing the burden of fault localization on the user. This paper demonstrates how the target diagnosis can be identified by performing a sequence of observations, that is, by querying an oracle about entailments of the target ontology. To identify the best query we propose two query selection strategies: a simple “split-in-half” strategy and an entropy-based strategy. The latter allows knowledge about typical user errors to be exploited to minimize the number of queries. Our evaluation showed that the entropy-based method significantly reduces the number of required queries compared to the “split-in-half” approach. We experimented with different probability distributions of user errors and different qualities of the a priori probabilities. Our measurements demonstrated the superiority of entropy-based query selection even in cases where all fault probabilities are equal, i.e. where no information about typical user errors is available.
Ontology debugging; Query selection; Model-based diagnosis; Description logic
Purpose: Distributed archives in a picture archiving and communication system (PACS) environment can provide added fault tolerance and fail-over capability, as well as increased load capacity at a more economical price than traditional “high-availability” systems. Systems can be configured with varying levels of fault tolerance, depending on the amountof redundancy desired. There is, however, a direct correlation between the level of hardware redundancy and cost to implement. This presentation details the system design for fault-tolerant distributed archives as well as several options for redundancy, referencing implementation of a fault-tolerant archivesystem at the University of Utah.Methods: The distributed archive system described here is based on Image Devices’ image archive software, which can be implemented on multiple individual archive servers in order to distribute archive functionality and operational load. The configuration and implementation of the individual servers together make up the distributed archive system and does not impact the ability of the system to be scaled to meet future requirements. Several implementation and configuration options exist, including the ability for servers to maintain replicated databases containing pateintand image information. Thus, each archive can be aware of all information and the location of this information within the distributed archive system.Results: The goal is to produce systems that will still be operational in the event of any single point of failure, ie, a network connection failure between facilities or the failure of asingle archive server within the distributed system. During normal operation, workload forimage acquisition, image routing and image query requests will be distributed between the archive servers. If the system is deployed in a multifacility environment, each archive server can be configured to be responsible for the acquisition and image distribution management within that server’s localfacility. If the system is deployed in a single facility environment, load can be distributed evenly between the archive servers based on an understanding of the workload requirements generated be each acquisition and display device in the system. In the event that an archive server fails, other archive servers within the system will have the ability to provide redundancy employed. Three levels of fault-tolerant design can be achieved with this system architecture: (1) duplicate work capability only; (2) duplicate work capability and short-term image cache; (3) duplicate work capability,short-term image cache, and long-term image archival. Using the basic fault-tolerant design above, we have implemented a multifacility distributedarchive system at the University of Utah. This system was implemented at a fraction of the cost of true “high-availability” archive architectures yet provides constant up time for the PACS system. If the network connection between thetwo locations goes down, each siteis still fully functional for soft-copy read, as well as image acquisition and distribution. If either of the archiveservers goes down, the image sources are redirected to the other archive server. The operational server then handles image distribution for both locations. Access to images in the short-term image cache is available to both archive servers and is not affected by loss of the network connection or remoteserver. Because there is ony one long-term archivedevice, the ability to retrieve images from long-term storage is theonly function compromised by a network or server failure.Conclusion: By implementing distributed archives in a PACS environment, it is possible to achieve a highly fault-tolerant system without the expense of high-availability hardware and software. The design concepts outlined here can be applied to any PACS system that supports distributed archive functionality.
Both seismological and geodynamic research emphasize that the Aegean Region, which comprises the Hellenic Arc, the Greek mainland and Western Turkey is the most seismically active region in Western Eurasia. The convergence of the Eurasian and African lithospheric plates forces a westward motion on the Anatolian plate relative to the Eurasian one. Western Anatolia is a valuable laboratory for Earth Science research because of its complex geological structure. Izmir is a large city in Turkey with a population of about 2.5 million that is at great risk from big earthquakes. Unfortunately, previous geodynamics studies performed in this region are insufficient or cover large areas instead of specific faults. The Tuzla Fault, which is aligned trending NE–SW between the town of Menderes and Cape Doganbey, is an important fault in terms of seismic activity and its proximity to the city of Izmir. This study aims to perform a large scale investigation focusing on the Tuzla Fault and its vicinity for better understanding of the region's tectonics. In order to investigate the crustal deformation along the Tuzla Fault and Izmir Bay, a geodetic network has been designed and optimizations were performed. This paper suggests a schedule for a crustal deformation monitoring study which includes research on the tectonics of the region, network design and optimization strategies, theory and practice of processing. The study is also open for extension in terms of monitoring different types of fault characteristics. A one-dimensional fault model with two parameters – standard strike-slip model of dislocation theory in an elastic half-space – is formulated in order to determine which sites are suitable for the campaign based geodetic GPS measurements. Geodetic results can be used as a background data for disaster management systems.
Crustal Deformation; Tuzla Fault; Network Design and Optimization; Seismic Hazard; GPS sensors
The popularity of continuous subcutaneous insulin infusion (CSII), or insulin pump therapy, as a way to deliver insulin more physiologically and achieve better glycemic control in diabetes patients has increased. Despite the substantiated therapeutic advantages of using CSII, its use has also been associated with an increased risk of technical malfunctioning of the device, which leads to an increased risk of acute metabolic complications, such as diabetic ketoacidosis. Current insulin pumps already incorporate systems to detect some types of faults, such as obstructions in the infusion set, but are not able to detect other types of fault such as the disconnection or leakage of the infusion set.
In this article, we propose utilizing a validated robust model-based fault detection technique, based on interval analysis, for detecting disconnections of the insulin infusion set. For this purpose, a previously validated metabolic model of glucose regulation in type 1 diabetes mellitus (T1DM) and a continuous glucose monitoring device were used. As a first step to assess the performance of the presented fault detection system, a Food and Drug Administration-accepted T1DM simulator was employed.
Of the 100 in silico tests (10 scenarios on 10 subjects), only two false negatives and one false positive occurred. All faults were detected before plasma glucose concentration reached 300 mg/dl, with a mean plasma glucose detection value of 163 mg/dl and a mean detection time of 200 min.
Interval model-based fault detection has been proven (in silico) to be an effective tool for detecting disconnection faults in sensor-augmented CSII systems. Proper quantification of the uncertainty associated with the employed model has been observed to be crucial for the good performance of the proposed approach.
diabetes fault detection; insulin pump therapy; interval analysis; model-based; robustness
Genetic information should be accurately transmitted from cell to cell; conversely, the adaptation in evolution and disease is fueled by mutations. In the case of cancer development, multiple genetic changes happen in somatic diploid cells. Most classic studies of the molecular mechanisms of mutagenesis have been performed in haploids. We demonstrate that the parameters of the mutation process are different in diploid cell populations. The genomes of drug-resistant mutants induced in yeast diploids by base analog 6-hydroxylaminopurine (HAP) or AID/APOBEC cytosine deaminase PmCDA1 from lamprey carried a stunning load of thousands of unselected mutations. Haploid mutants contained almost an order of magnitude fewer mutations. To explain this, we propose that the distribution of induced mutation rates in the cell population is uneven. The mutants in diploids with coincidental mutations in the two copies of the reporter gene arise from a fraction of cells that are transiently hypersensitive to the mutagenic action of a given mutagen. The progeny of such cells were never recovered in haploids due to the lethality caused by the inactivation of single-copy essential genes in cells with too many induced mutations. In diploid cells, the progeny of hypersensitive cells survived, but their genomes were saturated by heterozygous mutations. The reason for the hypermutability of cells could be transient faults of the mutation prevention pathways, like sanitization of nucleotide pools for HAP or an elevated expression of the PmCDA1 gene or the temporary inability of the destruction of the deaminase. The hypothesis on spikes of mutability may explain the sudden acquisition of multiple mutational changes during evolution and carcinogenesis.
Evolution and carcinogenesis are driven by mutations. Cells maintain constant mutation rates and can afford only transient mutagenesis bursts for adaptation. The nature of the mutational avalanches is not very clear. We sequenced the whole genomes of mutants induced in haploid and diploid yeast by nucleobase analog HAP and by DNA editing cytosine deaminase. Mutants selected in diploids are saturated with passenger mutations. Far fewer mutations are found in haploid mutants. Treatment with a mutagen without selection results in intermediate mutagenesis. The observed transient hypermutability of diploids under mutagenic insult helps to explain the wellspring of mutations that arise during evolution and carcinogenesis.
Installation of a Wireless and Powerless Sensing Node (WPSN) inside a spindle enables the direct transmission of monitoring signals through a metal case of a certain thickness instead of the traditional method of using connecting cables. Thus, the node can be conveniently installed inside motors to measure various operational parameters. This study extends this earlier finding by applying this advantage to the monitoring of spindle systems. After over 2 years of system observation and optimization, the system has been verified to be superior to traditional methods. The innovation of fault diagnosis in this study includes the unmatched assembly dimensions of the spindle system, the unbalanced system, and bearing damage. The results of the experiment demonstrate that the WPSN provides a desirable signal-to-noise ratio (SNR) in all three of the simulated faults, with the difference of SNR reaching a maximum of 8.6 dB. Following multiple repetitions of the three experiment types, 80% of the faults were diagnosed when the spindle revolved at 4,000 rpm, significantly higher than the 30% fault recognition rate of traditional methods. The experimental results of monitoring of the spindle production line indicated that monitoring using the WPSN encounters less interference from noise compared to that of traditional methods. Therefore, this study has successfully developed a prototype concept into a well-developed monitoring system, and the monitoring can be implemented in a spindle production line or real-time monitoring of machine tools.
monitoring system; wireless and powerless sensing node; signal-to-noise ratio; spindle
Despite intensive efforts using linkage and candidate gene approaches, the genetic etiology for the majority of families with a multi-generational breast cancer predisposition is unknown. In this study, we used whole-exome sequencing of thirty-three individuals from 15 breast cancer families to identify potential predisposing genes. Our analysis identified families with heterozygous, deleterious mutations in the DNA repair genes FANCC and BLM, which are responsible for the autosomal recessive disorders Fanconi Anemia and Bloom syndrome. In total, screening of all exons in these genes in 438 breast cancer families identified three with truncating mutations in FANCC and two with truncating mutations in BLM. Additional screening of FANCC mutation hotspot exons identified one pathogenic mutation among an additional 957 breast cancer families. Importantly, none of the deleterious mutations were identified among 464 healthy controls and are not reported in the 1,000 Genomes data. Given the rarity of Fanconi Anemia and Bloom syndrome disorders among Caucasian populations, the finding of multiple deleterious mutations in these critical DNA repair genes among high-risk breast cancer families is intriguing and suggestive of a predisposing role. Our data demonstrate the utility of intra-family exome-sequencing approaches to uncover cancer predisposition genes, but highlight the major challenge of definitively validating candidates where the incidence of sporadic disease is high, germline mutations are not fully penetrant, and individual predisposition genes may only account for a tiny proportion of breast cancer families.
Currently, we know that a woman who inherits a fault in one of two genes, BRCA1 or BRCA2, has a high risk of developing both breast and ovarian cancer. However, such faults account for only half of all families with a strong family history of breast cancer. In this study, we planned to identify new genes that may be associated with an increased risk of developing breast cancer by looking for faults in every gene in the blood DNA of multiple women with breast cancer from large families with a strong family history of the condition over multiple generations. We can then track which gene fault is present in all the women with breast cancer in that family and in other families, but is not found in the women who did not develop breast cancer or have no family history. Using this approach, we identified faults in two genes, Fanconi C and Bloom helicase, in six families. Faults in these genes appear to increase the risk of developing breast cancer. Both these genes work in a similar way as BRCA1 and BRCA2, and this highlights the importance of these functions in preventing breast cancer. Further studies need to be done to confirm our results.
Fault Tolerant Control (FTC) systems are crucial in industry to ensure safe and reliable operation, especially of motor drives. This paper proposes the use of multiple controllers for a FTC system of an induction motor drive, selected based on a switching mechanism. The system switches between sensor vector control, sensorless vector control, closed-loop voltage by frequency (V/f) control and open loop V/f control. Vector control offers high performance, while V/f is a simple, low cost strategy with high speed and satisfactory performance. The faults dealt with are speed sensor failures, stator winding open circuits, shorts and minimum voltage faults. In the event of compound faults, a protection unit halts motor operation. The faults are detected using a wavelet index. For the sensorless vector control, a novel Boosted Model Reference Adaptive System (BMRAS) to estimate the motor speed is presented, which reduces tuning time. Both simulation results and experimental results with an induction motor drive show the scheme to be a fast and effective one for fault detection, while the control methods transition smoothly and ensure the effectiveness of the FTC system. The system is also shown to be flexible, reverting rapidly back to the dominant controller if the motor returns to a healthy state.
fault tolerant control; V/f; vector control; BMRAS; wavelet; induction motor; stator winding shorts; stator winding open; speed sensor