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Rapid detection of nucleic acids is essential for clinical diagnosis of a wide range of infectious and non-infectious diseases. CRISPR-based diagnostic platforms are well-established for rapid and specific detection of nucleic acids but suffer from a low detection sensitivity without a target pre-amplification step. Our recently developed detection system, called CRISPR-ENHANCE, employs engineered crRNAs and optimized conditions to achieve a significantly higher sensitivity and enable femtomolar levels of nucleic acid detection even without target pre-amplification. Using the CRISPR-ENHANCE platform and following the methodology detailed in this paper, nucleic acid detection for low copy numbers can be achieved in less than an hour through either a fluorescence-based detection or a lateral flow assay. The step-by-step instructions provided, in addition to describing how to perform both assays, incorporate details on a LAMP/RT-LAMP-based target amplification step to enable detection of RNA, ssDNA and dsDNA. Furthermore, a protocol for in-house expression and purification of LbCas12a using CL7/lm7-based affinity chromatography, which has been used to achieve a high yield and purity of the enzyme in a single-step, is provided. [ABSTRACT FROM AUTHOR]

The present work critically analyzes the probabilistic definition of dynamic state-space models subject to Bayesian filters used for monitoring and predicting monotonic degradation processes. The study focuses on the selection of the random process, often called process noise , which is a key perturbation source in the evolution equation of particle filtering. Despite the large number of applications of particle filtering predicting structural degradation, the adequacy of the picked process noise has not been investigated. This paper reviews existing process noise models that are typically embedded in particle filters dedicated to monitoring and predicting structural damage caused by fatigue, which is monotonic in nature. The analysis emphasizes that existing formulations of the process noise can jeopardize the performance of the filter in terms of state estimation and remaining life prediction (i.e., damage prognosis). This paper subsequently proposes an optimal and unbiased process noise model and a list of requirements that the stochastic model must satisfy to guarantee high prognostic performance. These requirements are useful for future and further implementations of particle filtering for monotonic system dynamics. The validity of the new process noise formulation is assessed against experimental fatigue crack growth data from a full-scale aeronautical structure using dedicated performance metrics. [ABSTRACT FROM AUTHOR]

This paper deals with the state-of-the-art strategies and techniques based on vibro-acoustic signals that can monitor and diagnose malfunctions in Internal Combustion Engines (ICEs) under both test bench and vehicle operating conditions. Over recent years, several authors have summarized what is known in critical reviews mainly focused on reciprocating machines in general or on specific signal processing techniques: no attempts to deal with IC engine condition monitoring have been made. This paper first gives a brief summary of the generation of sound and vibration in ICEs in order to place further discussion on fault vibro-acoustic diagnosis in context. An overview of the monitoring and diagnostic techniques described in literature using both vibration and acoustic signals is also provided. Different faulty conditions are described which affect combustion, mechanics and the aerodynamics of ICEs. The importance of measuring acoustic signals, as opposed to vibration signals, is due since the former seem to be more suitable for implementation on on-board monitoring systems in view of their non-intrusive behaviour, capability in simultaneously capturing signatures from several mechanical components and because of the possibility of detecting faults affecting airborne transmission paths. In view of the recent needs of the industry to (−) optimize component structural durability adopting long-life cycles, (−) verify the engine final status at the end of the assembly line and (−) reduce the maintenance costs monitoring the ICE life during vehicle operations, monitoring and diagnosing system requests are continuously growing up. The present review can be considered a useful guideline for test engineers in understanding which types of fault can be diagnosed by using vibro-acoustic signals in sufficient time in both test bench and operating conditions and which transducer and signal processing technique (of which the essential background theory is here reported) could be considered the most reliable and informative to be implemented for the fault in question. [ABSTRACT FROM AUTHOR]

• A new method of condition monitoring based on locating defects via strain gauge analysis is suggested. • Strain gauge analysis in real-time is used as original information for control. • Exact location of defect (excitation sources) is defined. • Mathematical model for calculating exact location of defect via measurements of dynamics forces is developed. • Experimental study of the developed method is provided. This paper is devoted to a new approach for condition monitoring. The main feature is an application of strain gauge analysis for geometrical locating defects. Information about exact location of a defect, intensity of excitation and its frequency provides accurate diagnostics. In this paper a mathematical model is developed, that allow to calculate location of excitation sources using real-time data about dynamic forces in supports of a machine. An electronic board and computer program for implementation the proposed method are developed. The electronics measure strain gauge data in real time and transmit it to a computer program. Then coordinates, frequency and intensity of excitation sources are calculated. Also a 3D plot of source location is available. Such an approach gives new information for diagnostic and provides new opportunities for effective defect detecting and condition monitoring of various machines and equipment. [ABSTRACT FROM AUTHOR]

Highlights • The condition of the gear can be inferred under fluctuating operating conditions. • The models are used to detect, locate and trend localised gear damage. • The uncertainty in the diagnostic parameters is quantified with Bayesian techniques. • No historical data are required for optimising the models. Abstract Performing condition monitoring on rotating machines which operate under fluctuating conditions remains a challenge, with robust diagnostic techniques being required for the condition inference task. Some of the developed diagnostic techniques rely on the availability of historical fault data, which is impractical or even impossible to obtain in many circumstances and therefore novelty detection techniques such as discrepancy analysis are used. However, discrepancy analysis assumes that the condition of the machine is the same throughout the signal in the model optimisation process i.e. no localised damage is present, which can pose problems if the training data unwittingly contain a component with localised damage. In this paper, an automatic procedure is proposed for diagnosing localised gear damage in the presence of fluctuating operating conditions, with no historical data being required to model the data used in the condition inference process. The continuous wavelet transform, principal component analysis and information theory are used to obtain divergence data of the gear under consideration. The divergence data are used with Bayesian data analysis techniques to automatically infer the presence of localised anomalies due to localised gear damage. The proposed technique is validated in two experimental investigations, with promising results being obtained. [ABSTRACT FROM AUTHOR]

The number of timely diagnoses based on condition monitoring data is increasing with the growing usage of monitoring systems. In most of the methods used in these systems, a pre-established fault detection threshold is needed, while there are no specific limit values or thresholds in many cases, especially when the machine is unique. Also, in most actual applications, due to the kind of process and harsh environment, the noise inherent in the observed process exhibits non-Gaussian characteristics, making it a challenging task for diagnostics based on condition monitoring (CM) data. Therefore, this paper introduced a robust methodology based on the switching maximum correntropy Kalman filter (SMCKF) to address the mentioned problems (threshold and online diagnostics in the presence of non-Gaussian noise by using CM data). This approach uses multiple dynamic system models to explain different degradation stages, utilizing robust Bayesian estimation. As this approach is based on dynamic behavior, a threshold for diagnostics is no longer needed. Ultimately, the proposed approach is applied to the online diagnosis of simulated and actual data sets. The results of both simulated and real data sets prove the method's efficacy. [ABSTRACT FROM AUTHOR]

• Free and forced response sensitivity to symmetry-breaking imperfections. • Imperfections lead to distinct mode distortions with non-unison oscillations. • No degenerate absorber mode splitting due to absorber mass and spacing error. • An improved parametric tuning strategy against imperfections. • Diagnostics of unique response changes due to spacing and mass deviations. This paper details an analytical investigation on free and forced vibration response sensitivity of centrifugal pendulum vibration absorbers (CPVA) to symmetry-breaking absorber imperfections. Minor variations in the absorbers' design due to manufacturing tolerances, errors as well as operational wear and tear tend to break cyclic-symmetry and lead to asynchronous absorber oscillations, unbalanced rotor translations and thus significant vibration amplification. Therefore, an in-depth understanding on the sensitivity of CPVA's vibration response to absorber imperfections is needed to devise an improved absorber tuning strategy for robust vibration and noise reduction from realistic vehicle transmissions and drivelines. In this paper, free and forced vibration response sensitivity of generalized CPVA systems are analytically derived for absorber spacing and mass variations using the perturbation-based eigensensitivity technique in conjunction with modal superposition. The methodology applied in this study is realized through a linearized planar dynamic model, which includes both torsional and translational motions of the rotor along with oscillations of N equally-spaced baseline identical absorbers. Distinct mode distortions and degenerate mode splitting from the known baseline modal structure (with translational, rotational and absorber modes) are investigated for systems with asymmetric absorber spacing and absorber mass variations. The baseline modal structure is modified differently due to having unequal absorber masses or absorber spacing errors, and these differences are analytically derived and demonstrated. Moreover, it was identified that CPVA systems do not exhibit mode splitting with the absorber mass and spacing deviations. Response alterations and localized response patterns due to asymmetric absorber spacing and mass variations are identified and visualized through a case study. Results define a new parametric lower limit for the overtuning of absorbers to avoid localized absorber response and vibration amplification due to absorber imperfections. Additionally, the outcomes are used to locate unique excitation orders, which would help distinguish the alterations in system response due to absorber spacing and mass from each other and serve as a diagnostic tool. [ABSTRACT FROM AUTHOR]

Several isothermal techniques for the detection of plant pathogens have been developed with the advent of molecular techniques. Among them, Recombinase Polymerase Amplification (RPA) is becoming an important technique for the rapid, sensitive and cost-effective detection of plant viruses. The RPA technology has the advantage to be implemented in field-based scenarios because the method requires a minimal sample preparation, and is performed at constant low temperature (37–42 °C). The RPA technique is rapidly becoming a promising tool for use in rapid detection and further diagnostics in plant clinics and monitoring quarantine services. This paper presents a review of studies conducted using RPA for detection/diagnosis of plant viruses with either DNA genomes ( Banana bunchy top virus , Bean golden yellow mosaic virus , Tomato mottle virus , Tomato yellow leaf curl virus ) or RNA genomes ( Little Cherry virus 2 , Plum pox virus and Rose rosette virus ). [ABSTRACT FROM AUTHOR]

In this paper, a fault diagnostic methodology is developed which is able to detect, locate and trend gear faults under fluctuating operating conditions when only vibration data from a single transducer, measured on a healthy gearbox are available. A two-phase feature extraction and modelling process is proposed to infer the operating condition and based on the operating condition, to detect changes in the machine condition. Information from optimised machine and operating condition hidden Markov models are statistically combined to generate a discrepancy signal which is post-processed to infer the condition of the gearbox. The discrepancy signal is processed and combined with statistical methods for automatic fault detection and localisation and to perform fault trending over time. The proposed methodology is validated on experimental data and a tacholess order tracking methodology is used to enhance the cost-effectiveness of the diagnostic methodology. [ABSTRACT FROM AUTHOR]

Abstract: Much research has been conducted in prognostics and health management (PHM), an emerging field in mechanical engineering that is gaining interest from both academia and industry. Most of these efforts have been in the area of machinery PHM, resulting in the development of many algorithms for this particular application. The majority of these algorithms concentrate on applications involving common rotary machinery components, such as bearings and gears. Knowledge of this prior work is a necessity for any future research efforts to be conducted; however, there has not been a comprehensive overview that details previous and on-going efforts in PHM. In addition, a systematic method for developing and deploying a PHM system has yet to be established. Such a method would enable rapid customization and integration of PHM systems for diverse applications. To address these gaps, this paper provides a comprehensive review of the PHM field, followed by an introduction of a systematic PHM design methodology, 5S methodology, for converting data to prognostics information. This methodology includes procedures for identifying critical components, as well as tools for selecting the most appropriate algorithms for specific applications. Visualization tools are presented for displaying prognostics information in an appropriate fashion for quick and accurate decision making. Industrial case studies are included in this paper to show how this methodology can help in the design of an effective PHM system. [Copyright &y& Elsevier]

Estimating deterministic single-valued damage parameters when evaluating the actual health state of a material has a limited meaning if one considers not only the existence of measurement errors, but also that the model chosen to represent the damage behavior is just an idealization of reality. This paper proposes a multilevel Bayesian inverse problem framework to deal with these sources of uncertainty in the context of ultrasound-based damage identification. Although the methodology has a broad spectrum of applicability, here it is oriented to model-based damage assessment in layered composite materials using through-transmission ultrasonic measurements. The overall procedure is first validated on synthetically generated signals and then evaluated on real signals obtained from a post-impact fatigue damage experiment in a cross-ply carbon-epoxy laminate. The evidence of the hypothesized model of damage is revealed as a suitable measure of the overall ability of that candidate hypothesis to represent the actual damage state observed by the ultrasound, thus avoiding the extremes of over-fitting or under-fitting the ultrasonic signal. [ABSTRACT FROM AUTHOR]

Time-varying transmission paths and inaccessibility can increase the difficulty in both acquiring and processing vibration signals for the purpose of monitoring epicyclic gearboxes. Recent work has shown that the synchronous signal averaging approach may be applied to measured motor currents in order to diagnose tooth faults in parallel shaft gearboxes. In this paper we further develop the approach, so that it may also be applied to monitor tooth faults in epicyclic gearboxes. A low-degree-of-freedom model of an epicyclic gearbox which incorporates the possibility of simulating tooth faults, as well as any subsequent tooth contact loss due to these faults, is introduced. By combining this model with a simple space-phasor model of an induction motor it is possible to show that, in theory, tooth faults in epicyclic gearboxes may be identified from motor currents. Applying the synchronous averaging approach to experimentally recorded motor currents and angular displacements recorded from a shaft mounted encoder, validate this finding. Comparison between experiments and theory highlight the influence of operating conditions, backlash and shaft couplings on the transient response excited in the currents by the tooth fault. The results obtained suggest that the method may be a viable alternative or complement to more traditional methods for monitoring gearboxes. However, general observations also indicate that further investigations into the sensitivity and robustness of the method would be beneficial. [ABSTRACT FROM AUTHOR]

Generally, in planetary gearbox diagnostics, vibration transducers are placed on the gearbox case near the ring gear. The relative angular position of the planet gears with respect to the transducer is a useful information for the evaluation of vibration signals related to planet/sun gears. This angular position is usually unknown, or it is known with a large tolerance causing serious difficulties in both gears and bearing diagnostics. In fact, noise and spurious component from healthy planets could overhang the informative content about incipient faults. The present work seeks to propose two alternative methods for the identification of the angular position of the planet gears with respect to the transducer. The first one is based on the study of how the power flows inside the Time Synchronous Average of the ring gear, whilst the second method is based on a modified statistical parameter such as the Crest Factor. The effectiveness of these methods is assessed on the basis of actual vibration signals acquired from a faulty planetary gearbox. The knowledge of the exact angular position of the planet gears allows the diagnostics of both gears and bearings, as proven by extensive experimental activities reported in the paper. [ABSTRACT FROM AUTHOR]

Abstract: The paper shows that for condition monitoring of planetary gearboxes it is important to identify the external varying load condition. In the paper, systematic consideration has been taken of the influence of many factors on the vibration signals generated by a system in which a planetary gearbox is included. These considerations give the basis for vibration signal interpretation, development of the means of condition monitoring, and for the scenario of the degradation of the planetary gearbox. Real measured vibration signals obtained in the industrial environment are processed. The signals are recorded during normal operation of the diagnosed objects, namely planetary gearboxes, which are a part of the driving system used in a bucket wheel excavator, used in lignite mines. It is found that a planetary gearbox in bad condition is more susceptible to load than a gearbox in good condition. The estimated load time traces obtained by a demodulation process of the vibration acceleration signal for a planetary gearbox in good and bad conditions are given. It has been found that the most important factor of the proper planetary gearbox condition is connected with perturbation of arm rotation, where an arm rotation gives rise to a specific vibration signal whose properties are depicted by a short-time Fourier transform (STFT) and Wigner-Ville distribution presented as a time–frequency map. The paper gives evidence that there are two dominant low-frequency causes that influence vibration signal modulation, i.e. the varying load, which comes from the nature of the bucket wheel digging process, and the arm/carrier rotation. These two causes determine the condition of the planetary gearboxes considered. Typical local faults such as cracking or breakage of a gear tooth, or local faults in rolling element bearings, have not been found in the cases considered. In real practice, local faults of planetary gearboxes have not occurred, but heavy destruction of planetary gearboxes have been noticed, which are caused by a prolonged run of a planetary gearbox at the condition of the arm run perturbation. It may be stated that the paper gives a new approach to the condition monitoring of planetary gearboxes. It has been shown that only a root cause analysis based on factors having an influence on the vibration solves the problem of planetary gearbox condition monitoring. [Copyright &y& Elsevier]

One of the most common problems in rotor dynamics is the identification of faults and model-based methods are often used for this purpose. In some applications, the least-squares (LS) estimate is used to find out the position and the severity of impending faults on the basis of experimental vibration data of rotating machinery. Anyhow LS are not very robust with respect to possible outliers (noise and gross errors) in the experimental data and to inaccuracies in the model. The introduction of weights in the LS algorithm has proven to be effective in increasing the robustness and successful experimental cases, both on test rigs and on real machines, are reported in literature. However, the arbitrary choice of the weights is normally based on operators’ experience. In this paper, an improvement is presented by introducing a method that is robust in itself, the M-estimate, which allows defining automatically the weights. This method is general and can be applied in every problem of regression or estimation, not necessarily related to rotor dynamics. The fundamental theoretical aspects are introduced in the first part, while several experimental test cases are presented by means of fault identification on a test rig and on a gas turbo generator in the second part of the paper. The obtained results highlight the increasing of the accuracy allowed by M-estimate. [Copyright &y& Elsevier]

Abstract: Condition-based maintenance (CBM) is a maintenance program that recommends maintenance decisions based on the information collected through condition monitoring. It consists of three main steps: data acquisition, data processing and maintenance decision-making. Diagnostics and prognostics are two important aspects of a CBM program. Research in the CBM area grows rapidly. Hundreds of papers in this area, including theory and practical applications, appear every year in academic journals, conference proceedings and technical reports. This paper attempts to summarise and review the recent research and developments in diagnostics and prognostics of mechanical systems implementing CBM with emphasis on models, algorithms and technologies for data processing and maintenance decision-making. Realising the increasing trend of using multiple sensors in condition monitoring, the authors also discuss different techniques for multiple sensor data fusion. The paper concludes with a brief discussion on current practices and possible future trends of CBM. [Copyright &y& Elsevier]

On June 5 and 6, 2014, the Epilepsy Foundation held its 4th Biennial Epilepsy Pipeline Update Conference, an initiative of the Epilepsy Therapy Project, which showcased the most promising epilepsy innovations from health-care companies and academic laboratories dedicated to pioneering and advancing drugs, biologics, technologies, devices, and diagnostics for epilepsy. Speakers and attendees included emerging biotech and medical technology companies, major pharmaceutical and device companies, as well as investigators and innovators at the cutting-edge of epilepsy. The program included panel discussions on collaboration between small and large companies, how to get products in need of funding to the marketplace, who is currently funding epilepsy and CNS innovation, and how the NIH facilitates early-stage drug development. Finally, the conference featured the third annual “Shark Tank” competition. The presentations are summarized in this paper, which is followed by a compilation of the meeting poster abstracts. [ABSTRACT FROM AUTHOR]

Abstract: Cyclostationary models for the diagnostic signals measured on faulty rotating machineries have proved to be successful in many laboratory tests and industrial applications. The squared envelope spectrum has been pointed out as the most efficient indicator for the assessment of second order cyclostationary symptoms of damages, which are typical, for instance, of rolling element bearing faults. In an attempt to foster the spread of rotating machinery diagnostics, the current trend in the field is to reach higher levels of automation of the condition monitoring systems. For this purpose, statistical tests for the presence of cyclostationarity have been proposed during the last years. The statistical thresholds proposed in the past for the identification of cyclostationary components have been obtained under the hypothesis of having a white noise signal when the component is healthy. This need, coupled with the non-white nature of the real signals implies the necessity of pre-whitening or filtering the signal in optimal narrow-bands, increasing the complexity of the algorithm and the risk of losing diagnostic information or introducing biases on the result. In this paper, the authors introduce an original analytical derivation of the statistical tests for cyclostationarity in the squared envelope spectrum, dropping the hypothesis of white noise from the beginning. The effect of first order and second order cyclostationary components on the distribution of the squared envelope spectrum will be quantified and the effectiveness of the newly proposed threshold verified, providing a sound theoretical basis and a practical starting point for efficient automated diagnostics of machine components such as rolling element bearings. The analytical results will be verified by means of numerical simulations and by using experimental vibration data of rolling element bearings. [Copyright &y& Elsevier]

Abstract: Nowadays, manufacturing companies are making great efforts to implement an effective machinery maintenance program, which provides incipient fault detection. The machine problem and its irregularity can be detected at an early stage by employing a suitable condition monitoring accompanied with powerful signal processing technique. Among various defects occurred in machines, rotor faults are of significant importance as they cause secondary failures that lead to a serious motor malfunction. Diagnosis of rotor failures has long been an important but complicated task in the area of motor faults detection. This paper intends to review and summarize the recent researches and developments performed in condition monitoring of the induction machine with the purpose of rotor faults detection. The aim of this article is to provide a broad outlook on rotor fault monitoring techniques for the researchers and engineers. [Copyright &y& Elsevier]

Abstract: The frequency spectra of the outputs of transducers fixed to the ring gears of planetary-gear systems contain useful information pertaining to the health of such systems. The effects on such frequency spectra arising from non-uniform loading carried by the M planets in such systems was derived and illustrated in “Stationary transducer response to planetary-gear vibration excitation with non-uniform planet loading,” by Mark and Hines [W.D. Mark, J.A. Hines, Stationary transducer response to planetary-gear vibration excitation with non-uniform planet loading, Mechanical Systems and Signal Processing 23 (2009) 1366–1381]. The present paper extends this earlier analysis by predicting the added effects on such frequency spectra caused by planet-carrier torque modulations. It is shown that such torque modulations produce “sideband” harmonics on the harmonic contributions arising from uniform loading carried by the planets, and additional “sideband” harmonics on the harmonic contributions arising from the lack of uniformity of loading carried by the planets. An example is discussed for torque modulations with a period of 1/B times the planet-carrier rotation period. This example illustrates the effects of torque modulations on the transducer output spectra of a UH-60A rotorcraft with a B=4 blade rotor and M=5 planets. [Copyright &y& Elsevier]

• The 2020 Pipeline Conference presented emerging epilepsy therapies. • This article summarizes these advances and most recent available data. • This article underscores the areas of epilepsy requiring further investigation. From August 27–28, 2020 the Epilepsy Foundation hosted the Pipeline Conference, exploring emerging issues related to antiepileptic drug and device development. The conference featured epilepsy therapeutic companies and academic laboratories developing drugs for focal epilepsies, innovations for rare and ultra-rare diseases, and devices both in clinical trials and approved for use. In this paper, we outline the virtual presentations by the authors, including novel data from their development pipeline. [ABSTRACT FROM AUTHOR]

Diagnostics and prognostics are two important aspects in a condition-based maintenance (CBM) program. However, these two tasks are often separately performed. For example, data might be collected and analysed separately for diagnosis and prognosis. This practice increases the cost and reduces the efficiency of CBM and may affect the accuracy of the diagnostic and prognostic results. In this paper, a statistical modelling methodology for performing both diagnosis and prognosis in a unified framework is presented. The methodology is developed based on segmental hidden semi-Markov models (HSMMs). An HSMM is a hidden Markov model (HMM) with temporal structures. Unlike HMM, an HSMM does not follow the unrealistic Markov chain assumption and therefore provides more powerful modelling and analysis capability for real problems. In addition, an HSMM allows modelling the time duration of the hidden states and therefore is capable of prognosis. To facilitate the computation in the proposed HSMM-based diagnostics and prognostics, new forward–backward variables are defined and a modified forward–backward algorithm is developed. The existing state duration estimation methods are inefficient because they require a huge storage and computational load. Therefore, a new approach is proposed for training HSMMs in which state duration probabilities are estimated on the lattice (or trellis) of observations and states. The model parameters are estimated through the modified forward–backward training algorithm. The estimated state duration probability distributions combined with state-changing point detection can be used to predict the useful remaining life of a system. The evaluation of the proposed methodology was carried out through a real world application: health monitoring of hydraulic pumps. In the tests, the recognition rates for all states are greater than 96%. For each individual pump, the recognition rate is increased by 29.3% in comparison with HMMs. Because of the temporal structures, the same HSMMs can be used to predict the remaining-useful-life (RUL) of the pumps. [Copyright &y& Elsevier]

Abstract: To assess gear health and detect gear-tooth damage, the vibratory response from meshing gear-pair excitations is commonly monitored by accelerometers. In an earlier paper, strong evidence was presented suggesting that, in the case of tooth bending-fatigue damage, the principal source of detectable damage is whole-tooth plastic deformation; i.e. yielding, rather than changes in tooth stiffness caused by tooth-root cracks. Such plastic deformations are geometric deviation contributions to the “static-transmission-error” (STE) vibratory excitation caused by meshing gear pairs. The STE contributions caused by two likely occurring forms of such plastic deformations on a single tooth are derived, and displayed in the time domain as a function of involute “roll distance.” Example calculations are provided for transverse contact ratios of and 1.8, for spur gears and for helical-gear axial contact ratios ranging from to . Low-pass- and band-pass-filtered versions of these same STE contributions also are computed and displayed in the time domain. Several calculations, consisting of superposition of the computed STE tooth-meshing fundamental harmonic contribution and the band-pass STE contribution caused by a plastically deformed tooth, exhibit the amplitude and frequency or phase modulation character commonly observed in accelerometer-response waveforms caused by damaged teeth. General formulas are provided that enable computation of these STE vibratory-excitation contributions for any form of plastic deformation on any number of teeth for spur and helical gears with any contact ratios. [Copyright &y& Elsevier]

Abstract: Fault identification by means of model-based techniques, both in frequency and time domain, is often employed in diagnostics of rotating machines, when the main task is to locate and to evaluate the severity of the malfunction. The model of the fully assembled machine is composed by the submodels of the rotor, of the bearings and of the foundation, while the effect of the faults is modelled by means of equivalent force systems. Some identification techniques, such as the least squares identification in frequency domain, proposed by the authors, have proven to be quite robust even if the submodels are not fine-tuned. Anyhow, the use of a reliable model can increase the accuracy of the identification. Normally a supporting structure is represented by means of rigid foundation or by pedestals, i.e. 2 d.o.f. mass–spring–damper systems, but these kind of models are often not able to reproduce correctly the influence of the dynamical behaviour of the supporting structure on the shaft, especially in large machines where coupled modes are present. Therefore, peculiar aspect of this paper is the use of a modal foundation to model the supporting structure of the machine and the method is discussed in detail in this first part. The modal representation of the foundation is then introduced in the least squares identification technique in frequency domain. [Copyright &y& Elsevier]

Abstract: In this paper it is studied how observations in the training sample affect the misclassification probability of a quadratic discriminant rule. An approach based on partial influence functions is followed. It allows to quantify the effect of observations in the training sample on the performance of the associated classification rule. Focus is on the effect of outliers on the misclassification rate, merely than on the estimates of the parameters of the quadratic discriminant rule. The expression for the partial influence function is then used to construct a diagnostic tool for detecting influential observations. Applications on real data sets are provided. [Copyright &y& Elsevier]

On May 22-24, 2019, the 15th Antiepileptic Drug and Device (AEDD) Trials Conference was held, which focused on current issues related to AEDD development from preclinical models to clinical prognostication. The conference featured regulatory agencies, academic laboratories, and healthcare companies involved in emerging epilepsy therapies and research. The program included discussions around funding and support for investigations in epilepsy and neurologic research, clinical trial design and integrated outcome measures for people with epilepsy, and drug development and upcoming disease-modifying therapies. Finally, the conference included updates from the preclinical, clinical, and device pipeline. Summaries of the talks are provided in this paper, with the various pipeline therapeutics in the listed tables to be outlined in a subsequent publication. • 15th Antiepileptic Drug and Device Trials (AEDD) Meeting took place on May 22–24, 2019. • Topics focused on issues to advance therapeutic development for epilepsy from preclinical to clinical evaluation. • Presenters were representatives from regulatory agencies, academic labs, healthcare companies, or nonprofit organizations. [ABSTRACT FROM AUTHOR]