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Penetrating injuries of the thoracic aorta are usually rapidly lethal. Few patients survive for long enough to undergo surgical treatment. When penetrating injuries of the thoracic aorta are complicated by arteriovenous fistula a correct preoperative diagnosis is important for adequate planning of the surgical repair, and so selective angiography is essential. The best approach is through a median sternotomy with the use of total cardiopulmonary bypass with or without deep hypothermia and circulatory arrest. Fistulae between aorta and innominate vein invariably lead to congestive cardiac failure. A review of the literature suggests that signs of cardiac failure rarely appear early. Congestive failure developed within 30 days of the initial trauma in only two of the 12 reported cases. In our case, the early onset of cardac failure refractory to therapy and the appearance of an expanding pulsatile mass at the base of the neck, threatening rupture, necessitated emergency surgical treatment. [ABSTRACT FROM PUBLISHER]

Introduction: Many people experiencing mental illness remain in hospital or are readmitted because appropriate home care in the community is not readily available. This project will attempt to establish the use of smart technology in assisting individuals with mental illness. Objectives: The objective of this study is to evaluate the use of smart technologies for individuals with severe mental illness residing in the community and in transitional hospital apartments. Methods: The study is currently recruiting up to 20 participants for the transitional hospital apartments, and 13 participants in community homes (aged 18-85). Recruited participants chose screen devices such as smartphones, tablets, and/or touch-screen monitors. These enabled video-conferencing, prompts/reminders, and transmission of formal and informal questionnaires to their care-providers. Health-monitoring devices such as weigh-scales, blood pressure monitors, glucometers, and smartwatches were provided as needed. Automated medication dispensers were offered to community-based participants. Participants in the transitional hospital apartments completed a semi-structured interview upon discharge and at 6-months post-discharge. Communitybased participants completed interviews at baseline and at 6-month and 12-month follow-ups. Data analysis will include quantitative analyses pertaining to effectiveness (i.e. community integration, housing history and health), ethics, policy and costeffectiveness, and an ethnographic qualitative approach. Results: Participants have been positive towards the technology so far. It is anticipated that participants will demonstrate greater levels of community integration, housing stability and self-care for mental health and chronic illnesses. Conclusions: It is envisaged that the results will inform care-providers and decision-makers of housing support programs, leading to enhancements supporting self-care and potentially preventing hospital readmissions. [ABSTRACT FROM AUTHOR]

The performance evaluation of wind turbines operating in real-world environments typically relies on analyzing the power curve, which shows the relationship between wind speed and power output. However, conventional univariate models that consider only wind speed as an input variable often fail to fully explain the observed performance of wind turbines, as power output depends on multiple variables, including working parameters and ambient conditions. To overcome this limitation, the use of multivariate power curves that consider multiple input variables needs to be explored. Therefore, this study advocates for the application of explainable artificial intelligence (XAI) methods in constructing data-driven power curve models that incorporate multiple input variables for condition monitoring purposes. The proposed workflow aims to establish a reproducible method for identifying the most appropriate input variables from a more comprehensive set than is usually considered in the literature. Initially, a sequential feature selection approach is employed to minimize the root-mean-square error between measurements and model estimates. Subsequently, Shapley coefficients are computed for the selected input variables to estimate their contribution towards explaining the average error. Two real-world data sets, representing wind turbines with different technologies, are discussed to illustrate the application of the proposed method. The experimental results of this study validate the effectiveness of the proposed methodology in detecting hidden anomalies. The methodology successfully identifies a new set of highly explanatory variables linked to the mechanical or electrical control of the rotor and blade pitch, which have not been previously explored in the literature. These findings highlight the novel insights provided by the methodology in uncovering crucial variables that significantly contribute to anomaly detection.

An increasing amount of wind turbines, especially in Europe, are reaching the end of their expected lifetimes; therefore, long data sets describing their operation are available for scholars to analyze the performance trends. On these grounds, the present work is devoted to test case studies for the evaluation and the interpretation of wind turbine performance decline with age. Two wind farms were studied, featuring widely employed wind turbine models: the former is composed of 6 Senvion MM92 and the latter of 11 Vestas V52 wind turbines, owned by the ENGIE Italia company. SCADA data spanning, respectively, 10 and 7 years were analyzed for the two test cases. The effect of aging on the performance of the test case wind turbines was studied by constructing a data-driven model of appropriate operation curves, selected depending on the working region. For the Senvion MM92, we found that it is questionable to talk about performance aging because there is no evident trend in time: the performance variation year by year is in the order of a few kW and is therefore irrelevant for practical applications. For the Vestas V52 wind turbines, a much wider variability is observed: two wind turbines are affected by a remarkable performance drop, after which the behavior is stable and under-performing with respect to the rest of the wind farm. Particular attention is devoted to the interpretation of the results: the comparative discussion of the two test cases indicates that the observed operation curves are compatible with the hypothesis that the worsening with age of the two under-performing Vestas V52 can be ascribed to the behavior of the hydraulic blade pitch. Furthermore, for both test cases, it is estimated that the gearbox-aging contributes negligibly to the performance decline in time.

The measurement of the rotational speed of rotating machinery is typically performed based on mechanical adherence; for example, in encoders. Nevertheless, it can be of interest in various types of applications to develop contactless vision-based methodologies to measure the speed of rotating machinery. In particular, contactless rotor speed measurement methods have several potential applications for wind turbine technology, in the context of non-intrusive condition monitoring approaches. The present study is devoted exactly to this problem: a ground level video-tachometer measurement technique and an image analysis algorithm for wind turbine rotor speed estimation are proposed. The methodology is based on the comparison between a reference frame and each frame of the video through the covariance matrix: a covariance time series is thus obtained, from which the rotational speed is estimated by passing to the frequency domain through the spectrogram. This procedure guarantees the robustness of the rotational speed estimation, despite the intrinsic non-stationarity of the system and the possible signal disturbances. The method is tested and discussed based on two experimental environments with different characteristics: the former is a small wind turbine model (with a 0.45 m rotor diameter) in the wind tunnel facility of the University of Perugia, whose critical aspect is the high rotational speed (up to the order of 1500 RPM). The latter test case is a wind turbine with a 44 m rotor diameter which is part of an industrial wind farm: in this case, the critical point regards the fact that measurements are acquired in uncontrolled conditions. It is shown that the method is robust enough to overcome the critical aspects of both test cases and to provide reliable rotational speed estimates.

The order Actinomycetales includes phylogenetically diverse but morphologically similar aerobic and anaerobic organisms, exhibiting filamentous branching structures which fragment into rods or coccoid forms. Lung pathogens of the order comprise Mycobacterium, Nocardia, Corynebacterium, Actinomyces, Kytococcus, Rothia, Williamsia, as well as Gordonia, Tsukamurella and Rhodococcus. Particularly, members of the last three genera are uncommon aerobic agents of lung cavitations and tuberculosis(TB)-like syndromes, that should be carefully considered in the aetiology of parenchymal lesions. Correct identification of such organisms is hard to obtain, but is crucial to provide patients with adequate diagnose and treatment. Then, this review aims to unearth their airway tropism, as well as their clinical impact as agents of lung disease., (Copyright © 2011 Institut Pasteur. Published by Elsevier Masson SAS. All rights reserved.)

Rhodococcus equi is an uncommon Gram positive, variably acid-fast pathogen, that appears as hard to treat mostly owing to the establishment of intracellular niches. Lack of interpretive criteria for susceptibility testing may lead to under-reporting or overestimation of resistances, whereas knowledge about this pathogen's clinical impact may be affected by erroneous phenotype-based characterization at a genus and species level.We present the case of a bacteraemia with a concomitant lung mass in a lymphoma patient, that further highlights the emergence of rhodococcal diseases as a matter for concern in the fields of infectious diseases and haematology.

Organisms in the genus Kocuria are Gram-positive, coagulase-negative, coccoid actinobacteria belonging to the family Micrococcaceae, suborder Micrococcineae, order Actinomycetales. Sporadic reports in the literature have dealt with infections by Kocuria species, mostly in compromised hosts with serious underlying conditions. Nonetheless, the number of infectious processes caused by such bacteria may be higher than currently believed, given that misidentification by phenotypic assays has presumably affected estimates of the prevalence over the years. As a further cause for concern, guidelines for therapy of illnesses involving Kocuria species are lacking, mostly due to the absence of established criteria for evaluating Kocuria replication or growth inhibition in the presence of antibiotics. Therefore, breakpoints for staphylococci have been widely used throughout the literature to try to understand this pathogen's behaviour under drug exposure; unfortunately, this has sometimes created confusion, thus higlighting the urgent need for specific interpretive criteria, along with a deeper investigation into the resistance determinants within this genus. We therefore review the published data on cultural, genotypic and clinical aspects of the genus Kocuria, aiming to shed some light on these emerging nosocomial pathogens.