mechanical behaviour of wind turbines operating above design conditions

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An Automated Procedure for Assessing Local Reliability Index and Life-Cycle Cost of Alternative Girder Bridge Design Solutions

Stakeholders of civil infrastructures have to usually choose among several design alternatives in order to select a final design representing the best trade-off between safety and economy, in a life-cycle perspective. In this framework, the paper proposes an automated procedure for the estimation of life-cycle repair costs of different bridge design solutions. The procedure provides the levels of safety locally guaranteed by the selected design solution and the related total life-cycle cost. The method is based on the finite element modeling of the bridge and uses design traffic models as suggested by international technical standards. Both the global behavior and the transversal cross section of the bridge are analyzed in order to provide local reliability indexes. Several parameters involved in the design, such as geometry and loads and materials' characteristics, are considered as uncertain. Degradation models are adopted for steel carpentry and rebars. The application of the procedure to a road bridge case study shows its potential in providing local safety levels for different limit states over the entire lifetime of the bridge and the life-cycle cost of the infrastructure, highlighting the importance of the local character of the life-cycle cost analysis

Toxic organic contaminants in airborne particles: levels, potential sources and risk assessment

In the last years, many studies have focused on risk assessment of exposure of workers to airborne particulate matter (PM). Several studies indicate a strong correlation between PM and adverse health outcomes, as a function of particle size. In the last years, the study of atmospheric particulate matter has focused more on particles less than 10 m or 2.5 m in diameter; however, recent studies identify in particles less than 0.1 m the main responsibility for negative cardiovascular effects. The present paper deals with the determination of 66 organic compounds belonging to six different classes of persistent organic pollutants (POPs) in the ultrafine, fine and coarse fractions of PM (PM < 0.1 m; 0.1 < PM < 2.5 mand 2.5 < PM < 10 m) collected in three outdoor workplaces and in an urban outdoor area. Data obtained were analyzed with principal component analysis (PCA), in order to underline possible correlation between sites and classes of pollutants and characteristic emission sources. Emission source studies are, in fact, a valuable tool for both identifying the type of emission source and estimating the strength of each contamination source, as useful indicator of environment healthiness. Moreover, both carcinogenic and non-carcinogenic risks were determined in order to estimate human health risk associated to study sites. Risk analysis was carried out evaluating the contribution of pollutant distribution in PM size fractions for all the sites. The results highlighted significant differences between the sites and specific sources of pollutants related to work activities were identified. In all the sites and for all the size fractions of PM both carcinogenic and non-carcinogenic risk values were below acceptable and safe levels of risks recommended by the regulatory agencies

Cortico-muscular coupling to control a hybrid brain-computer interface for upper limb motor rehabilitation: A pseudo-online study on stroke patients

Brain-Computer Interface (BCI) systems for motor rehabilitation after stroke have proven their efficacy to enhance upper limb motor recovery by reinforcing motor related brain activity. Hybrid BCIs (h-BCIs) exploit both central and peripheral activation and are frequently used in assistive BCIs to improve classification performances. However, in a rehabilitative context, brain and muscular features should be extracted to promote a favorable motor outcome, reinforcing not only the volitional control in the central motor system, but also the effective projection of motor commands to target muscles, i.e., central-to-peripheral communication. For this reason, we considered cortico-muscular coupling (CMC) as a feature for a h-BCI devoted to post-stroke upper limb motor rehabilitation. In this study, we performed a pseudo-online analysis on 13 healthy participants (CTRL) and 12 stroke patients (EXP) during executed (CTRL, EXP unaffected arm) and attempted (EXP affected arm) hand grasping and extension to optimize the translation of CMC computation and CMC-based movement detection from offline to online. Results showed that updating the CMC computation every 125 ms (shift of the sliding window) and accumulating two predictions before a final classification decision were the best trade-off between accuracy and speed in movement classification, independently from the movement type. The pseudo-online analysis on stroke participants revealed that both attempted and executed grasping/extension can be classified through a CMC-based movement detection with high performances in terms of classification speed (mean delay between movement detection and EMG onset around 580 ms) and accuracy (hit rate around 85%). The results obtained by means of this analysis will ground the design of a novel non-invasive h-BCI in which the control feature is derived from a combined EEG and EMG connectivity pattern estimated during upper limb movement attempts

CometAnalyser : A user-friendly, open-source deep-learning microscopy tool for quantitative comet assay analysis

Comet assay provides an easy solution to estimate DNA damage in single cells through microscopy assessment. It is widely used in the analysis of genotoxic damages induced by radiotherapy or chemotherapeutic agents. DNA damage is quantified at the single-cell level by computing the displacement between the genetic material within the nucleus, typically called ``comet head", and the genetic material in the surrounding part of the cell, considered as the ``comet tail". Today, the number of works based on Comet Assay analyses is really impressive. In this work, besides revising the solutions available to obtain reproducible and reliable quantitative data, we developed an easy-to-use tool named CometAnalyser. It is designed for the analysis of both fluorescent and silver-stained wide-field microscopy images and allows to automatically segment and classify the comets, besides extracting Tail Moment and several other intensity/morphological features for performing statistical analysis. CometAnalyser is an open-source deep-learning tool. It works with Windows, Macintosh, and UNIX-based systems. Source code, standalone versions, user manual, sample images, video tutorial and further documentation are freely available at: https://sourceforge.net/p/cometanalyser. (c) 2022 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY license (http://creativecommons. org/licenses/by/4.0/).Peer reviewe

Polychlorinated biphenyl profile in polyhydroxy-alkanoates synthetized from urban organicwastes

Call CIRC-05-2016The microbial synthesis of polyhydroxyalkanoates (PHA) from organic wastes is a valuable process to valorize available renewable resources, such as food wastes and biological sludge. Bioplastics find many applications in various sectors, from medical field to food industry. However, persistent organic pollutants could be transferred from wastes to the final product. The present paper demonstrates that the use of municipal wastes in PHA production is safe for the environment and human health and provides a polychlorinated biphenyl (PCB) profile in both commercial and waste-based PHA samples. PCB analysis in several PHA samples showed very low concentrations of the target analytes. Commercial PHA samples showed a similar PCB level with respect to PHA samples from municipal waste/sludge and higher than PHA samples from fruit waste. For all analyzed PCBs, detected concentrations were consistently lower than the ones reported in regulatory framework or guidelines.publishersversionpublishe

Correlative Fluorescence and Raman Microscopy to Define Mitotic Stages at the Single-Cell Level: Opportunities and Limitations in the AI Era

Nowadays, morphology and molecular analyses at the single-cell level have a fundamental role in understanding biology better. These methods are utilized for cell phenotyping and in-depth studies of cellular processes, such as mitosis. Fluorescence microscopy and optical spectroscopy techniques, including Raman micro-spectroscopy, allow researchers to examine biological samples at the single-cell level in a non-destructive manner. Fluorescence microscopy can give detailed morphological information about the localization of stained molecules, while Raman microscopy can produce label-free images at the subcellular level; thus, it can reveal the spatial distribution of molecular fingerprints, even in live samples. Accordingly, the combination of correlative fluorescence and Raman microscopy (CFRM) offers a unique approach for studying cellular stages at the singlecell level. However, subcellular spectral maps are complex and challenging to interpret. Artificial intelligence (AI) may serve as a valuable solution to characterize the molecular backgrounds of phenotypes and biological processes by finding the characteristic patterns in spectral maps. The major contributions of the manuscript are: (I) it gives a comprehensive review of the literature focusing on AI techniques in Raman-based cellular phenotyping; (II) via the presentation of a case study, a new neural network-based approach is described, and the opportunities and limitations of AI, specifically deep learning, are discussed regarding the analysis of Raman spectroscopy data to classify mitotic cellular stages based on their spectral maps

Hardware calibrated learning to compensate heterogeneity in analog RRAM-based Spiking Neural Networks

Spiking Neural Networks (SNNs) can unleash the full power of analog Resistive Random Access Memories (RRAMs) based circuits for low power signal processing. Their inherent computational sparsity naturally results in energy efficiency benefits. The main challenge implementing robust SNNs is the intrinsic variability (heterogeneity) of both analog CMOS circuits and RRAM technology. In this work, we assessed the performance and variability of RRAM-based neuromorphic circuits that were designed and fabricated using a 130 nm technology node. Based on these results, we propose a Neuromorphic Hardware Calibrated (NHC) SNN, where the learning circuits are calibrated on the measured data. We show that by taking into account the measured heterogeneity characteristics in the off-chip learning phase, the NHC SNN self-corrects its hardware non-idealities and learns to solve benchmark tasks with high accuracy. This work demonstrates how to cope with the heterogeneity of neurons and synapses for increasing classification accuracy in temporal tasks

Toxic organic contaminants in airborne particles responsible for negative health effects

Several studies in literature have highlighted associations between airborne particulate matter and several adverse health outcomes, as a function of particle size. Traditionally, PM studies have focused on particles less than 10 μm in diameter (PM10) or particles less than 2.5 μm in diameter (PM2.5), with each fraction characterized by a distinct source, and different composition and health effects. Since, diffusion in the alveolar region of smaller particles with an aerodynamic diameter less than 0.1 μm (i.e. 100 nm and therefore also defined as nanoparticles) becomes an effective mechanism and the probability of deposition increases. There is a great debate whether ultrafine fraction (PM0.1) is mainly responsible for negative cardiovascular effects caused by its high oxidative and mutagenic potential. In the present study, size-fractionated airborne particulate matter was collected from outdoor urban and working environments and analyzed for 105 organic contaminants of different classes: polycyclic aromatic hydrocarbons (PAH) and their derivatives (nitro-PAH and oxy-PAH); polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs) and Novel Brominated flame-retardants (N-BFRs) selected for their toxicity and persistence in the environment. The selected organic contaminants were subsequently divided into three different macro-groups based on chemical and physical properties (PAH/N-PAH/O-PAH; PCB and PBDE/N-BFR) and subjected to statistical analysis. The monitoring campaigns were carried out in four sites: an urban atmosphere (RM), a wastewater treatment plant (WWTP) where aerosol is generated during the different phases of the processes, an intensive livestock farming activity, characterized by sheds serving as a shelter for cows (COW) and an area where feed is stored (FEED). In each monitoring campaign PM was collected with a multistage low-pressure impactor able to sample 14 size intervals of PM on as many filters, subsequently joined to form three dimensional fractions (coarse, fine and ultrafine). The results obtained from the organic contaminant analyses and from the Principal Component Analysis (PCA) showed a correlation between sites and classes of pollutants, allowing the identification of characteristic emission sources of each monitored site. Emission source studies are in fact a valuable tool for both identifying the type of emission source characteristic of a specific place and estimating the strength of each contamination source in the same place of interest

Healthcare-associated infections and antimicrobial resistance in severe acquired brain injury: a retrospective multicenter study

BackgroundRecent studies underscore that healthcare-associated infections (HAIs) and multidrug-resistant (MDR) HAIs affect rehabilitation outcomes and hospital length of stay (LOS) for severe acquired brain injury (sABI).ObjectiveThis study aimed to estimate HAI incidence in different sABI rehabilitation settings and determine risk factors and HAI impact on neuromotor and cognitive recovery.MethodsWe conducted a retrospective multicenter study in two semi-intensive units (SICUs), two high-specialty post-acute units (PAUs), and one long-term care (LTC) rehabilitation facility. Data extraction was performed by experienced clinicians, using a structured Excel file and they agreed upon criteria for case definitions of healthcare. The main outcome measures were the HAI and MDR HAI incidence and the LOS, the functional recovery was measured using the Level of Cognitive Functioning and Disability Rating Scale.ResultsThere were 134 sABI participants. The calculation of the probability level was adjusted for three pairwise comparisons among settings (0.05/3 = 0.017). The HAI and MDR HAI incidences were significantly higher in SICU (3.7 and 1.3 per 100 person-days) than in other settings (LTC: 1.9, p = 0.034 and 0.5, p = 0.026; PAU: 1.2, p < 0.001 and 0.3, p < 0.001). HAI and MDR HAI risk variables included older age, an increased number of devices, and carbapenemase-producing Enterobacteriaceae (CPE) colonization, while a high prealbumin plasma value seemed to have a protective effect.ConclusionHAIs are related to longer LOS, and colonization is associated with poor prognosis and poor functional outcomes with reduced ability to achieve the cognitive capacity of self-care, employability, and independent living. The need to ensure the protection of non-colonized patients, especially those with severe disabilities on admission, is highlighted