The flow cytometer is an instrument that can measure the characteristics of cells such as the number of cells, the degree of internal composition of the cells, the size of the cells, and the cell cycle etc. This equipment has been used to study leukemia, DNA and RNA analysis, protein expression, cell death, and immune response. However, flow cytometer is an expensive equipment and requires an operator with expertise for use and maintenance. When only simple data are needed, such as measuring the number of cells or quantitative analysis of cell growth and inhibition, the use of a flow cytometer is not suitable in terms of cost and requires unnecessary measurement time consumption. In this study, a compact optical measurement system using commercially available LEDs, photodiodes, and digital multimeters was developed, and the body structure was printed and utilized by a 3D printer. Cervical cancer cells, known as one of the major cancers of women, were fluorescently treated with fluorescent dyes such as Calcein AM and DiD, and performance of the system was verified. The side scattering measured using various filters with different transmission wavelengths of light showed high linearity in proportion to the number of cells. By measuring the side scattering of the untreated cervical cancer cells, fluorescence scattering could be confirmed from the difference in the side scattering intensity according to the fluorescence treatment.

Flexible sensors are fundamental devices for human body monitoring in application areas ranging from health care to soft robotics. During the last decade the possibility to couple sensing of mechanical strain and physiological parameters have attracted ever increasing interest to design novel, robust and low-cost wearable sensing units. Stretchable and pH sensible piezoelectric strain sensors made by blending intrinsically conductive polymers and polymeric electrolyte can serve this purpose. In this work, we specifically investigate a Crosslinked Nanofibers (NFs) Flex Sensor able to detect mechanical flection and pH change. The optimized sensitive element of the NFs Flex Sensor is based on crosslinked electrospun NFs mats made of a blend of (polyethylene oxide) PEO as the polymeric electrolyte, and poly(3,4-ethylenedioxythiophene) doped with poly(styrene sulfonate) (PEDOT:PSS) as the intrinsically conductive polymer. The NFs Flex Sensor has been obtained by directly collecting the nanomaterial on a flexible and biocompatible polydimethylsiloxane (PDMS) slab and thermally treating it to promote electrical conductivity of the PEO/PEDOT:PSS NFs. The thermal treatment was optimized to crosslink PEO and PSS while preserving the nanostructuration, to optimize the mechanical coupling with PDMS substrate and to improve water resistance. In this work, we demonstrate excellent mechanical sensing of the NFs Flex Sensor coupled to electrochemical pH detection. Change of pH was detected by Electrochemical Impedance Spectroscopy (EIS), obtaining a linear dependence of the capacitance with the pH value. The piezo-resistive caracterization of the Crosslinked NFs Flex Sensors demonstrated the ability of nanomaterials to recover their initial configuration after release of the mechanical strain in both compression and traction mode. The Gauge Factors (GFs) values were 45.84 in traction and 208.55 in compression mode, reflecting the extraordinary piezoresistive behavior of our nanostructurated PEO/PEDOT:PSS NFs.

REFERENCES

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The aging, deterioration and failure of civil structures are nowadays challenges of paramount importance, increasingly motivating the search of advanced Structural Health Monitoring (SHM) tools. In this work, we propose a SHM strategy for real time structural damage detection and localization, combining Deep Learning (DL) and Model-Order Reduction (MOR). The developed data-based procedure is driven by the analysis of vibration and temperature recordings, shaped as multivariate time series and collected on the fly through pervasive sensor networks. Damage detection and localization are treated as a supervised classification task considering a finite number of predefined damage scenarios. During a preliminary offline phase, for each damage scenario, a collection of synthetic structural responses and temperature distributions is numerically generated through a physics-based model. Several loading and thermal conditions are considered thanks to a suitable parametrization of the problem, which controls the dependency of the model on operational and environmental conditions. Because of the huge amount of model evaluations, required by the construction of a dataset such as to guarantee a good enough exploration of the parametric space, MOR techniques are employed to relieve the computational burden of the procedure. Finally, a DL network, featuring a stack of convolutional layers, is trained by assimilating both the vibrational and thermal data. During the online phase, the trained DL network processes new experimental recordings to classify the actual state of the structure, and thus providing information about the presence and the localization of the damage, if any. Numerical performances of the proposed approach are assessed through a numerical example involving the monitoring of a two-storey frame under low intensity seismic excitation.

Resolution is an important characteristic to determine the nature and features of the image. Enhancing the resolution strengthens the features hidden within the image, and make the image sharper and more informative. The image quality is improved when noise is removed/suppressed from it. The proposed model provides a technique to enhance the resolution of different types of images, obtained from imaging devices, using a convolutional autoencoder. A convolutional neural network (CNN) architecture is developed by adding different layers to the neural network. An autoencoder capable of encoding and decoding the structure of the images is proposed to enhance their resolution. The model tends to learn the lower-dimensional features of unclear images and provide a high resolution to them by predicting and enhancing their dimensions. The model is trained on low-resolution images and the corresponding high-resolution images, and a convolutional auto-encoder is implemented to denoise the image to introduce high-resolution in the blurred or corrupted images. The model overcomes the limitations of the existing denoising filter techniques and provides a higher level of image quality enhancement.

Concrete based civil infrastructure such as bridges, tunnels and dams undergo structural deterioration due to weathering, corrosion, thermal cycles, and carbonation. Cracks on concrete surfaces are often identified as an early indication of possible future structural failures which could be catastrophic if unattended. Therefore, it is of utmost importance to inspect concrete structures frequently for cracks to initiate any proactive measures to avoid further damage. Visual inspection of larger civil structures using remotely controlled drones has become popular in recent years. The recorded video footages from these inspection rounds are manually watched to detect any cracks. This is a highly time-consuming process and largely depends on surveyor’s experience and the knowledge which adds an extra subjective bias to the final qualitative analysis.

In this paper, we demonstrate that deep transfer learning can be used to train an object detection model to automatically identify cracks with segmentation masks to localize cracks on images collected from video inspections. We specifically looked at YOLACT: a real-time instant segmentation algorithm which outperformed other existing algorithms in speed and accuracy in the COCO object detection dataset and used it to train deep learning model on a small dataset of concrete crack images. Instance segmentation allowed us to detect localized multiple cracks on the same image which may provide extra information to predict the propagation of cracks. Real-time detection is vital as this will enable active inspection by autonomously steering the drone along the cracks. Also, the drone can be navigated to closely look at the detected cracks. To train the crack detection model, we built a dataset by collecting images from a publicly available dataset and manually annotating segmentation mask for each crack. The transfer learning approach helped us to train the network on a smaller dataset with the high-level features extracted from the COCO dataset. The test on the trained model achieved a precision value of more than 90% and a recall value of more than 75%.

The major challenges of operating data-intensive of Distributed Ledger Technology (DLT) are 1) To reach consensus on the main chain is a set of validators cast public votes to decide on which blocks to finalize and 2) scalability on how to increasing the number of chains which will be running in parallel.
In this paper, we introduce a new proximal algorithm that scales DLT in large scale IoT devices network. We discuss how the algorithm benefits the integrating DLT in IoT by using edge computing technology, taking the scalability and heterogeneous capability of IoT devices into consideration. IoT devices are clustered dynamically into groups based on various proximity context information. A cluster head is used to bridge the IoT devices with the DLT network where the smart contract is deployed. In this way, the security of the IoT is improved and the scalability and latency are solved. We elaborate our mechanism and discuss issues that should be considered and implemented when using the proposed algorithm even we show its behaves when varying parameters like latency or when clustering.

Satellite-based navigation techniques have revolutionized the modern-day surveying with unprecedented accuracies along with the traditional and terrestrial-based navigation techniques. However, the satellite-based techniques gain popularity due to its ease and availability. The position and attitude sensors mounted on satellites, aerial, and ground-based platforms as well as different types of equipment play a vital role in remote sensing providing navigation and data. The presented review in this paper describes the terrestrial (LORAN-C, Omega, Alpha, Chayka) and satellite-based systems with their major features and peculiar applications. The regional and global navigation satellite systems (GNSS) can provide the position of a static object or a moving object i.e. in Kinematic mode. The GNSS systems include NAVigation Satellite Timing And Ranging Global Positioning System (NAVSTAR GPS), of United States of America (USA); Globalnaya navigatsionnaya sputnikovaya sistema (GLObal NAvigation Satellite System, GLONASS), of RUSSIA; BEIDOU, of China; and GALILEO, of the European Union (EU). Among the initial satellite-based regional navigation systems included the TRANSIT of the US and TSYKLON of the USSR which became operational in the 1960s. Regional systems developed in the last decade include the Quasi-Zenith Satellite System (QZSS) and the Indian Regional Navigation Satellite System (IRNSS). Currently, these global and regional satellite-based systems provide their services with accuracies of the order of 10-20m using the trilateration method of surveying for civil use. The terrestrial and satellite-based augmented systems (SBAS) were further developed along with different surveying techniques to improve the accuracies up to centimeters or millimeter levels for precise applications.

The goal of the article is to compare two methods used for suppression of an effect of terrain unevenness on accuracy of height measurement in UAV with integrated ultrasound altimeter during landing. Secure landing is one of the main requirements of current UAV operation. When the UAV is controlled by an operator on direct visibility, the landing process is possible to control manually or alternatively to use auxiliary system that informs about current height. However recently, the development is focused on regimes without direct visibility between UAV and its operator or the autonomous and semiautonomous modes of UAV flight. The course of the whole flight itself is possible to divide into three phases, take off, cruising and landing. Just autonomous landing is usually the most safety critical of these phases. For this reason it is crucial to continually measure the UAV height as accurately as possible and to monitor the landing area for potential obstacles. The obstacle detection can be performed in various ways, today most often with on-board camera. For height determination it is beneficial to use ultrasound. In this case it is necessary to compare requested measurement accuracy with actually achievable. Measurement accuracy is dependent on several factors caused by the altimeter itself and by outside conditions. Here it is possible to consider two essential effects. These are transmission environment properties and the character of the terrain from which the ultrasound signal is reflected during the height measurement. This article is focused on the ways to suppress the impact of terrain unevenness on measured height. Two of the basic methods are compared here. The first method is a continuous averaging of the measured values. The second method is the Kalman filter. The use of the both methods presumes that any obstacles have been eliminated from the landing side detection process in another way, so the area of the application of these methods is obstacle free only with different degree and character of terrain unevenness. In the article, several model cases of the terrain unevenness are used with their simulated ultrasonic height measurements. These values are then applied to the both different processing methods and their results are mutually compared.

Gold nanoparticles (AuNPs) are nanodevices that can have many uses in biomedical applications but sometime they show nanotoxic effects on biological system. Among these effects, the activation of the innate immune system (inflammatory response) is considered a central issue for assessing health risks of Au NPs. Although the origin of this nanotoxicity is not well known, the cause could be associated to the presence of contaminants on nanoparticles’ surface, such as bacterial endotoxin. Bacterial Endotoxin, also known as Lipopolysaccharide (LPS), is the main component on cell walls of gram-negative bacteria and it is considered one of the major contaminant in the environment. The main goal of this study is to identify and analyse the activation of the inflammatory response associated to AuNPs and/or to the presence of LPS on the nanoparticles’ surface. To this aim, the interaction of AuNPs with LPS is analysed, the presence of LPS molecules on NPs is quantified, and the interaction of AuNPs with human primary macrophages is investigated, in order to distinguish the intrinsic NPs biological effects from those induced by LPS.

LPS dose-dependent adsorption on 50 nm AuNPs was studied by DLS and by SERS technique in order to understand the amount of LPS that binds to NPs surface and quantify it. Internalization of bare and LPS coated 50 nm AuNPs was studied in macrophages by TEM and Raman imaging and their inflammatory effect was studied by in vitro stimulation through evaluation of inflammatory cytokine production (TNF-α).

DLS results indicate that a uniform LPS corona (8712 molecules) is formed around all NPs (2 µg) when incubated with doses greater than 500 ng, while analysis of SERS signals show a Limit of Detection (LOD) for LPS amount of the order of fg. These promising results show how SERS technique can be a reliable LPS-Sensor, while NPs imaging studies showed that NPs are localized in cytoplasmic vesicles inside macrophages. Moreover, bare NPs do not induce the production of TNF-α cytokine in treated macrophages.

Vinegar is produced from the alcoholic and subsequent acetous fermentations of carbohydrate sources under highly aerobic conditions wherein the ethanol is oxidized to acetic acid. Depending on the source materials, many different types of vinegar are produced, as the former do not only provide different acidity and sour taste to the latter, but also play a key role in vinegar flavour as well as in its chemical composition.
As for many other food products, several frauds have been perpetrated in the production and commercialization of vinegar given the huge variety of vinegars available in the market in terms of quality, types and prices. Unfortunately there is not a specific methodology that allows the detection of such adulterations with traditional methods, but current approaches rely on the quantification of certain physical properties or chemical compounds which have been reported as genuineness indicators.
In this direction, the application of a voltammetric electronic tongue (ET) towards the classification and authentication of vinegar is presented herein. Vinegar samples of different varieties were analysed with a three-sensor array, without performing any sample pre-treatment, but only an electrochemical cleaning stage between sample measurements to avoid fouling onto the electrode surfaces. Next, the use of discrete cosine transform (DCT) for the compression and reduction of signal complexity in voltammetric measurements was explored, and the number of coefficients was optimized through its inverse transform. Finally, the obtained coefficients were analysed by principal component analysis (PCA) to attempt the discrimination of the different vinegars and by linear discriminant analysis (LDA) to build a model that allows its categorization. Satisfactory results were obtained overall, with a classification rate of 100% for the external test subset (n = 15).