Accuracy Characterization of a MEMS Accelerometer for Vibration Monitoring in a Rotating Framework

Active and passive vibration control systems are of paramount importance in many engineering applications. If an external load excites a structure’s resonance and the damping is too low, detrimental events, such as crack initiation, growth and, in the worst case, fatigue failure, can be entailed. Damping systems can be commonly found in applications such as industrial machines, vehicles, buildings, turbomachinery blades, and so forth. Active control systems usually achieve higher damping effectiveness than passive ones, but they need a sensor to detect the working conditions that require damping system activation. Recently, the development of such systems in rotating structures has received considerable interest among designers. As a result, the development of vibration monitoring equipment in rotating structures is also a topic of particular interest. In this respect, a reliable, inexpensive and wireless monitoring system is of utmost importance. Typically, optical systems are used to measure vibrations, but they are expensive and require rather complex processing algorithms. In this paper, a wireless system based on a commercial MEMS accelerometer is developed for rotating blade vibration monitoring. The proposed system measurement accuracy was assessed by means of comparison with a reference wired measurement setup based on a mini integrated circuit piezoelectric (ICP) accelerometer adapted for data acquisition in a rotating frame. Both the accelerometers were mounted on the tip of the blade and, in order to test the structure under different conditions, the first four blade resonances were excited by means of piezoelectric actuators, embedded in a novel experimental setup. The frequency and amplitude of acceleration, simultaneously measured by the reference and MEMS sensors, were compared with each other in order to investigate the viability and accuracy of the proposed wireless monitoring system. The rotor angular speed was varied from 0 to 300 rpm, and the data acquisitions were repeated six times for each considered condition. The outcomes reveal that the wireless measurement system may be successfully used for vibration monitoring in rotating blades

Self-Healing Nanocomposites—Advancements and Aerospace Applications

Self-healing polymers and nanocomposites form an important class of responsive materials. These materials have the capability to reversibly heal their damage. For aerospace applications, thermosets and thermoplastic polymers have been reinforced with nanocarbon nanoparticles for self-healing of structural damage. This review comprehends the use of self-healing nanocomposites in the aerospace sector. The self-healing behavior of the nanocomposites depends on factors such as microphase separation, matrix–nanofiller interactions and inter-diffusion of polymer–nanofiller. Moreover, self-healing can be achieved through healing agents such as nanocapsules and nanocarbon nanoparticles. The mechanism of self-healing has been found to operate via physical or chemical interactions. Self-healing nanocomposites have been used to design structural components, panels, laminates, membranes, coatings, etc., to recover the damage to space materials. Future research must emphasize the design of new high-performance self-healing polymeric nanocomposites for aerospace structures

Performance of Smart Materials-Based Instrumentation for Force Measurements in Biomedical Applications: A Methodological Review

The introduction of smart materials will become increasingly relevant as biomedical technologies progress. Smart materials sense and respond to external stimuli (e.g., chemical, electrical, mechanical, or magnetic signals) or environmental circumstances (e.g., temperature, illuminance, acidity, or humidity), and provide versatile platforms for studying various biological processes because of the numerous analogies between smart materials and biological systems. Several applications based on this class of materials are being developed using different sensing principles and fabrication technologies. In the biomedical field, force sensors are used to characterize tissues and cells, as feedback to develop smart surgical instruments in order to carry out minimally invasive surgery. In this regard, the present work provides an overview of the recent scientific literature regarding the developments in force measurement methods for biomedical applications involving smart materials. In particular, performance evaluation of the main methods proposed in the literature is reviewed on the basis of their results and applications, focusing on their metrological characteristics, such as measuring range, linearity, and measurement accuracy. Classification of smart materials-based force measurement methods is proposed according to their potential applications, highlighting advantages and disadvantages

Cutting-Edge Green Polymer/Nanocarbon Nanocomposite for Supercapacitor—State-of-the-Art

Supercapacitors have attained a special stance among energy storage devices such as capacitors, batteries, fuel cell, and so forth. In this state-of-the-art overview on green synthesis approaches and green materials for supercapacitors, the cutting-edge green polymer/nanocarbon nanocomposite systems were explored by focusing on the design and related essential features. In this regard, various polymers were reconnoitered including conjugated polymers, thermosetting matrices, and green-cellulose-based matrices. Nanocarbon nanomaterials have also expanded research thoughtfulness for green-technology-based energy storage devices. Consequently, green polymer/nanocarbon nanocomposites have publicized fine electron conduction pathways to promote the charge storage, specific capacitance, energy density, and other essential features of supercapacitors. Future research directions must focus on the design of novel high performance green nanocomposites for energy storage applications

A New CuSe-TiO2-GO Ternary Nanocomposite: Realizing a High Capacitance and Voltage for an Advanced Hybrid Supercapacitor

A high capacitance and widened voltage frames for an aqueous supercapacitor system are challenging to realize simultaneously in an aqueous medium. The severe water splitting seriously restricts the narrow voltage of the aqueous electrolyte beyond 2 V. To overcome this limitation, herein, we proposed the facile wet-chemical synthesis of a new CuSe-TiO2-GO ternary nanocomposite for hybrid supercapacitors, thus boosting the specific energy up to some maximum extent. The capacitive charge storage mechanism of the CuSe-TiO2-GO ternary nanocomposite electrode was tested in an aqueous solution with 3 M KOH as the electrolyte in a three-cell mode assembly. The voltammogram analysis manifests good reversibility and a remarkable capacitive response at various currents and sweep rates, with a durable rate capability. At the same time, the discharge/charge platforms realize the most significant capacitance and a capacity of 920 F/g (153 mAh/g), supported by the impedance analysis with minimal resistances, ensuring the supply of electrolyte ion diffusion to the active host electrode interface. The built 2 V CuSe-TiO2-GO||AC-GO||KOH hybrid supercapacitor accomplished a significant capacitance of 175 F/g, high specific energy of 36 Wh/kg, superior specific power of 4781 W/kg, and extraordinary stability of 91.3% retention relative to the stable cycling performance. These merits pave a new way to build other ternary nanocomposites to achieve superior performance for energy storage devices

Hippocampal subfield volumetry: differential pattern of atrophy in different forms of genetic frontotemporal dementia

BACKGROUND: Frontotemporal dementia (FTD) is a heterogeneous neurodegenerative disorder, with a strong genetic component. Previous research has shown that medial temporal lobe atrophy is a common feature of FTD. However, no study has so far investigated the differential vulnerability of the hippocampal subfields in FTD. OBJECTIVES: We aimed to investigate hippocampal subfield volumes in genetic FTD. METHODS: We in6/2/2018vestigated hippocampal subfield volumes in a cohort of 75 patients with genetic FTD (age: mean (standard deviation) 59.3 (7.7) years; disease duration: 5.1(3.4) years; 29 with MAPT, 28 with C9orf72, and 18 with GRN mutations) compared with 97 age-matched controls (age: 62.1 (11.1) years). We performed a segmentation of their volumetric T1-weighted MRI scans to extract hippocampal subfields volumes. Left and right volumes were summed and corrected for total intracranial volumes. RESULTS: All three groups had smaller hippocampi than controls. The MAPT group had the most atrophic hippocampi, with the subfields showing the largest difference from controls being CA1-4 (24–27%, p < 0.0005). For C9orf72, the CA4, CA1, and dentate gyrus regions (8–11%, p < 0.0005), and for GRN the presubiculum and subiculum (10–14%, p < 0.0005) showed the largest differences from controls. CONCLUSIONS: The hippocampus was affected in all mutation types but a different pattern of subfield involvement was found in the three genetic groups, consistent with differential cortical-subcortical network vulnerability

Study of an Optimized Micro-Grid’s Operation with Electrical Vehicle-Based Hybridized Sustainable Algorithm

Recently, the expansion of energy communities has been aided by the lowering cost of storage technologies and the appearance of mechanisms for exchanging energy that is driven by economics. An amalgamation of different renewable energy sources, including solar, wind, geothermal, tidal, etc., is necessary to offer sustainable energy for smart cities. Furthermore, considering the induction of large-scale electric vehicles connected to the regional micro-grid, and causes of increase in the randomness and uncertainty of the load in a certain area, a solution that meets the community demands for electricity, heating, cooling, and transportation while using renewable energy is needed. This paper aims to define the impact of large-scale electric vehicles on the operation and management of the microgrid using a hybridized algorithm. First, with the use of the natural attributes of electric vehicles such as flexible loads, a large-scale electric vehicle response dispatch model is constructed. Second, three factors of micro-grid operation, management, and environmental pollution control costs with load fluctuation variance are discussed. Third, a hybrid gravitational search algorithm and random forest regression (GSA-RFR) approach is proposed to confirm the method’s authenticity and reliability. The constructed large-scale electric vehicle response dispatch model significantly improves the load smoothness of the micro-grid after the large-scale electric vehicles are connected and reduces the impact of the entire grid. The proposed hybridized optimization method was solved within 296.7 s, the time taken for electric vehicle users to charge from and discharge to the regional micro-grid, which improves the economy of the micro-grid, and realizes the effective management of the regional load. The weight coefficients λ1 and λ2 were found at 0.589 and 0.421, respectively. This study provides key findings and suggestions that can be useful to scholars and decisionmakers

MRI analysis for Hippocampus segmentation on a distributed infrastructure

Medical image computing raises new challenges due to the scale and the complexity of the required analyses. Medical image databases are currently available to supply clinical diagnosis. For instance, it is possible to provide diagnostic information based on an imaging biomarker comparing a single case to the reference group (controls or patients with disease). At the same time many sophisticated and computationally intensive algorithms have been implemented to extract useful information from medical images. Many applications would take great advantage by using scientific workflow technology due to its design, rapid implementation and reuse. However this technology requires a distributed computing infrastructure (such as Grid or Cloud) to be executed efficiently. One of the most used workflow manager for medical image processing is the LONI pipeline (LP), a graphical workbench developed by the Laboratory of Neuro Imaging (http://pipeline.loni.usc.edu). In this article we present a general approach to submit and monitor workflows on distributed infrastructures using LONI Pipeline, including European Grid Infrastructure (EGI) and Torque-based batch farm. In this paper we implemented a complete segmentation pipeline in brain magnetic resonance imaging (MRI). It requires time-consuming and data-intensive processing and for which reducing the computing time is crucial to meet clinical practice constraints. The developed approach is based on web services and can be used for any medical imaging application

Multiple RF classifier for the hippocampus segmentation: method and validation on EADC-ADNI harmonized hippocampal protocol

AbstractThe hippocampus has a key role in a number of neurodegenerative diseases, such as Alzheimer's Disease. Here we present a novel method for the automated segmentation of the hippocampus from structural magnetic resonance images (MRI), based on a combination of multiple classifiers. The method is validated on a cohort of 50 T1 MRI scans, comprehending healthy control, mild cognitive impairment, and Alzheimer's Disease subjects. The preliminary release of the EADC-ADNI Harmonized Protocol training labels is used as gold standard. The fully automated pipeline consists of a registration using an affine transformation, the extraction of a local bounding box, and the classification of each voxel in two classes (background and hippocampus). The classification is performed slice-by-slice along each of the three orthogonal directions of the 3D-MRI using a Random Forest (RF) classifier, followed by a fusion of the three full segmentations. Dice coefficients obtained by multiple RF (0.87 ± 0.03) are larger than those obtained by a single monolithic RF applied to the entire bounding box, and are comparable to state-of-the-art. A test on an external cohort of 50 T1 MRI scans shows that the presented method is robust and reliable. Additionally, a comparison of local changes in the morphology of the hippocampi between the three subject groups is performed. Our work showed that a multiple classification approach can be implemented for the segmentation for the measurement of volume and shape changes of the hippocampus with diagnostic purposes

On the interpretation of spin-polarized electron energy loss spectra

We study the origin of the structure in the spin-polarized electron energy loss spectroscopy (SPEELS) spectra of ferromagnetic crystals. Our study is based on a 3d tight-binding Fe model, with constant onsite Coulomb repulsion U between electrons of opposite spin. We find it is not the total density of Stoner states as a function of energy loss which determines the response of the system in the Stoner region, as usually thought, but the densities of Stoner states for only a few interband transitions. Which transitions are important depends ultimately on how strongly umklapp processes couple the corresponding bands. This allows us to show, in particular, that the Stoner peak in SPEELS spectra does not necessarily indicate the value of the exchange splitting energy. Thus, the common assumption that this peak allows us to estimate the magnetic moment through its correlation with exchange splitting should be reconsidered, both in bulk and surface studies. Furthermore, we are able to show that the above mechanism is one of the main causes for the typical broadness of experimental spectra. Finally, our model predicts that optical spin waves should be excited in SPEELS experiments.Comment: 11 pages, 7 eps figures, REVTeX fil