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The process of implementing a damage identification strategy for aerospace, civil and mechanical engineering infrastructure is referred to as structural health monitoring (SHM). Here, damage is defined as changes to the material and/or geometric properties of these systems, including changes to the boundary conditions and system connectivity, which adversely affect the system's performance. A wide variety of highly effective local non-destructive evaluation tools are available for such monitoring. However, the majority of SHM research conducted over the last 30 years has attempted to identify damage in structures on a more global basis. The past 10 years have seen a rapid increase in the amount of research related to SHM as quantified by the significant escalation in papers published on this subject. The increased interest in SHM and its associated potential for significant life-safety and economic benefits has motivated the need for this theme issue. This introduction begins with a brief history of SHM technology development. Recent research has begun to recognize that the SHM problem is fundamentally one of the statistical pattern recognition (SPR) and a paradigm to address such a problem is described in detail herein as it forms the basis for organization of this theme issue. In the process of providing the historical overview and summarizing the SPR paradigm, the subsequent articles in this theme issue are cited in an effort to show how they fit into this overview of SHM. In conclusion, technical challenges that must be addressed if SHM is to gain wider application are discussed in a general manner.

Ceramic matrix composites are the emerging material of choice for structures that will see temperatures above ~1,500 °C in hostile environments, as for example in next-generation gas turbines and hypersonic-flight applications. The safe operation of applications depends on how small cracks forming inside the material are restrained by its microstructure. As with natural tissue such as bone and seashells, the tailored microstructural complexity of ceramic matrix composites imparts them with mechanical toughness, which is essential to avoiding failure. Yet gathering three-dimensional observations of damage evolution in extreme environments has been a challenge. Using synchrotron X-ray computed microtomography, we have fully resolved sequences of microcrack damage as cracks grow under load at temperatures up to 1,750 °C. Our observations are key ingredients for the high-fidelity simulations used to compute failure risks under extreme operating conditions. Gathering information on the evolution of small cracks in ceramic matrix composites used in hostile environments such as in gas turbines and hypersonic flights has been a challenge. It is now shown that sequences of microcrack damage in ceramic composites under load at temperatures up to 1,750 °C can be fully resolved with the use of in situ synchrotron X-ray computed microtomography.

The traditional requirement for clean rooms and specialized skills has inhibited many biologists from pursuing new microfluidic innovations. Makerspaces provide a growing alternative to clean rooms: they provide low-cost access to fabrication equipment such as laser cutters, plotter cutters, and 3D printers; use commercially available materials; and attract a diverse community of product designers. This Opinion discusses the materials, tools, and building methodologies particularly suited for developing novel microfluidic devices in these spaces, with insight into biological applications and leveraging the maker community. The lower barrier to access of makerspaces ameliorates the otherwise poor accessibility and scalability of microfluidic prototyping. The use of simple tools and materials to manufacture microfluidic devices provides an opportunity for makerspaces to serve as a hotbed for microfluidic device development. Materials such as plastic, adhesive, and paper, along with tools such as plotter/laser cutters and 3D printers, enable building integrated microfluidic systems that are more easily translated to large-scale manufacturing. Makerspaces provide low-cost access to prototyping tools and access to technically diverse human capital, and they enable those without advanced skills to participate in microfluidic device development.

Purpose Riata and Riata ST defibrillator leads (St. Jude Medical, Sylmar, CA, USA) have been recalled due to increased risk of insulation failure leading to externalized cables. As this mechanical failure does not necessarily correlate with electrical failure, it can be difficult to diagnose. Fluoroscopic screening can identify insulation failure. Studies have suggested that insulation failure is predominantly seen in 8-Fr, single-coil models. Our patients have exclusively dual-coil leads and a high proportion of 7-Fr leads. Methods Fluoroscopic screening was performed in 48 patients with recalled Riata leads. Twenty-three patients had 8-Fr Riata leads and 25 patients had 7-Fr Riata ST leads. Images were recorded in at least three projections and studies were reviewed by seven attending electrophysiologists. Results Externalized cables were seen in ten patients (21 %), and another five patients (10 %) had abnormal cable spacing. All device interrogations showed normal parameters. Patients with abnormal leads had more leads in situ (2.5 ± 0.7 vs. 1.6 ± 0.8 leads; P  = 0.002) and a higher rate of nonischemic cardiomyopathy (80 vs. 24 %; P  = 0.03). There were no differences between the groups with regards to patient age, body mass index, lead age, lead parameters, or vascular access site. There was no difference with regard to lead size ( P  = 0.76). Conclusions The Riata family of leads has a high incidence of mechanical failure, as demonstrated on fluoroscopic screening. In this study, the 7-Fr models were just as likely to mechanically fail as the 8-Fr models. Increasing lead burden and a diagnosis of nonischemic cardiomyopathy correlated with insulation failure.

Rotating machinery is one of the most typical types of mechanical equipment and plays a significant role in industrial applications. Condition monitoring and fault diagnosis of rotating machinery has gained wide attention for its significance in preventing catastrophic accident and guaranteeing sufficient maintenance. With the development of science and technology, fault diagnosis methods based on multi-disciplines are becoming the focus in the field of fault diagnosis of rotating machinery. This paper presents a multi-discipline method based on image-processing for fault diagnosis of rotating machinery. Different from traditional analysis method in one-dimensional space, this study employs computing method in the field of image processing to realize automatic feature extraction and fault diagnosis in a two-dimensional space. The proposed method mainly includes the following steps. First, the vibration signal is transformed into a bi-spectrum contour map utilizing bi-spectrum technology, which provides a basis for the following image-based feature extraction. Then, an emerging approach in the field of image processing for feature extraction, speeded-up robust features, is employed to automatically exact fault features from the transformed bi-spectrum contour map and finally form a high-dimensional feature vector. To reduce the dimensionality of the feature vector, thus highlighting main fault features and reducing subsequent computing resources, t-Distributed Stochastic Neighbor Embedding is adopt to reduce the dimensionality of the feature vector. At last, probabilistic neural network is introduced for fault identification. Two typical rotating machinery, axial piston hydraulic pump and self-priming centrifugal pumps, are selected to demonstrate the effectiveness of the proposed method. Results show that the proposed method based on image-processing achieves a high accuracy, thus providing a highly effective means to fault diagnosis for rotating machinery.

Objective We examined whether additional helmet flow obtained by a single-circuit and a modified plateau valve applied at the helmet expiratory port (open-circuit ventilators) improves CO 2 wash-out by increasing helmet airflow. Design and setting Randomized physiological study in a university research laboratory. Participants Ten healthy volunteers. Interventions Helmet continuous positive airway pressure and pressure support ventilation delivered by an ICU ventilator (closed-circuit ventilator) and two open-circuit ventilators equipped with a plateau valve placed either at the inspiratory or at the helmet expiratory port. Measurements and results We measured helmet air leaks, breathing pattern, helmet minute ventilation ( Eh ), minute ventilation washing the helmet ( Ewh ), CO 2 wash-out, and ventilator inspiratory assistance. Air leaks were small and similar in all conditions. Breathing pattern was similar among the different ventilators. Inspiratory and end-tidal CO 2 were lower, while Eh and Ewh were higher only using open-circuit ventilators with the plateau valve placed at the helmet expiratory port. This occurred notwithstanding these ventilators delivered a lower inspiratory assistance. Conclusions Additional helmet flow provided by open-circuit ventilators can lower helmet CO 2 rebreathing. However, inspiratory pressure assistance significantly decreases using open-circuit ventilators, still casting doubts on the choice of the optimal helmet ventilation setup.

Background Implantable cardioverter defibrillators (ICDs) have become an important part of the management of patients with congestive heart failure. At the time of ICD implantation, ventricular fibrillation (VF) is induced to assess adequate energy required for defibrillation. There are multiple parameters which influence the defibrillation safety margin (DSM); however, these factors are not well-established when ICDs are implanted for the primary prevention of sudden cardiac death (SCD) in patients with severe systolic dysfunction. We evaluated multiple clinical and echocardiographic parameters as predictors of adequate DSM in patients referred for ICD implantation for the primary prevention of SCD. Methods We prospectively enrolled 41 patients for ICD implantation with clinical indications for the primary prevention of SCD. Two blinded independent readers evaluated the prespecified echocardiographic parameters. These included left ventricular (LV) mass, indices of right ventricular and LV systolic and diastolic functions, and LV geometric dimensions. Basic clinical demographics, including age, gender, comorbidities, and etiology of cardiomyopathy, were also evaluated. DSM was established using our standard protocol for defibrillation testing which includes VF with successful first shock terminating VF at a value at least 10 J below the maximum output of the implanted device. High defibrillation thresholds (DFT) were defined as >21 J. Results The mean age is 61.8 ± 14.7 years, with men comprising the majority of the patients (73 %). The only clinical variables which predicted the high DFT were age (in years) (54.5 ± 17.5 vs. 65.7 ± 11.3, p  = 0.044), QRS duration (in milliseconds) (116.0 ± 29.5 vs. 110.5 ± 21.8, p  = 0.03), LV mass (in grams) (241.0 ± 77.9 vs. 181.9 ± 52.3, p  = 0.006), and LV mass index (in grams per square meter) (111.1 ± 38.2 vs. 86.4 ± 21.1, p  = 0.02). On multivariate logistic regression analysis, LV mass was the only independent predictor of low DFT (≤22 J) in patients with ICD implanted for the primary prevention of SCD. Conclusion LV mass may help predict an adequate DSM in patients who are referred for ICD implantation for the primary prevention of SCD. These results may help distinguish the patients who may require high-energy devices prior to the implantation procedure. These results may help distinguish patients requiring high-energy devices, coils, or advanced programming prior to implantation and appropriate referral to electrophysiologists.

Structural health monitoring (SHM) is a term increasingly used in the last decade to describe a range of systems implemented on full-scale civil infrastructures and whose purposes are to assist and inform operators about continued 'fitness for purpose' of structures under gradual or sudden changes to their state, to learn about either or both of the load and response mechanisms. Arguably, various forms of SHM have been employed in civil infrastructure for at least half a century, but it is only in the last decade or two that computer-based systems are being designed for the purpose of assisting owners/operators of ageing infrastructure with timely information for their continued safe and economic operation. This paper describes the motivations for and recent history of SHM applications to various forms of civil infrastructure and provides case studies on specific types of structure. It ends with a discussion of the present state-of-the-art and future developments in terms of instrumentation, data acquisition, communication systems and data mining and presentation procedures for diagnosis of infrastructural 'health'.

The recent discovery of graphene 1 , 2 , 3 has led to many advances in two-dimensional physics and devices 4 , 5 . The graphene devices fabricated so far have relied on SiO 2 back gating 1 , 2 , 3 . Electrochemical top gating is widely used for polymer transistors 6 , 7 , and has also been successfully applied to carbon nanotubes 8 , 9 . Here we demonstrate a top-gated graphene transistor that is able to reach doping levels of up to 5×10 13  cm −2 , which is much higher than those previously reported. Such high doping levels are possible because the nanometre-thick Debye layer 8 , 10 in the solid polymer electrolyte gate provides a much higher gate capacitance than the commonly used SiO 2 back gate, which is usually about 300 nm thick 11 . In situ Raman measurements monitor the doping. The G peak stiffens and sharpens for both electron and hole doping, but the 2D peak shows a different response to holes and electrons. The ratio of the intensities of the G and 2D peaks shows a strong dependence on doping, making it a sensitive parameter to monitor the doping.

Stated in its most basic form, the objective of structural health monitoring is to ascertain if damage is present or not based on measured dynamic or static characteristics of a system to be monitored. In reality, structures are subject to changing environmental and operational conditions that affect measured signals, and these ambient variations of the system can often mask subtle changes in the system's vibration signal caused by damage. Data normalization is a procedure to normalize datasets, so that signal changes caused by operational and environmental variations of the system can be separated from structural changes of interest, such as structural deterioration or degradation. This paper first reviews the effects of environmental and operational variations on real structures as reported in the literature. Then, this paper presents research progresses that have been made in the area of data normalization.