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Skeletal muscle mass histidine-containing dipeptide items are increased in water turtles (Chemical. picta bellii) using cold-acclimation.
Identifying structural damage is an essential task for ensuring the safety and functionality of civil, mechanical, and aerospace structures. In this study, the structural damage identification scheme is formulated as an optimization problem, and a new meta-heuristic optimization algorithm, called visible particle series search (VPSS), is proposed to tackle that. The proposed VPSS algorithm is inspired by the visibility graph technique, which is a technique used basically to convert a time series into a graph network. In the proposed VPSS algorithm, the population of candidate solutions is regarded as a particle series and is further mapped into a visibility graph network to obtain visible particles. AEBSF The information captured from the visible particles is then utilized by the algorithm to seek the optimum solution over the search space. The general performance of the proposed VPSS algorithm is first verified on a set of mathematical benchmark functions, and, afterward, its ability to identify structural damage is assessed by conducting various numerical simulations. The results demonstrate the high accuracy, reliability, and computational efficiency of the VPSS algorithm for identifying the location and the extent of damage in structures.Digital image correlation techniques are well known for motion extraction from video images. Following a two-stage approach, the pixel-level displacement is first estimated by maximizing the cross-correlation between two images, then the estimation is refined in the vicinity of the cross-correlation peak. Among existing subpixel refinement methods, quadratic surface fitting (QSF) provides good performances in terms of accuracy and computational burden. It estimates subpixel displacement by interpolating cross-correlation values with a quadratic surface. The purpose of this paper is to analytically investigate the QSF method. By means of counterexamples, it is first shown in this paper that, contrary to a widespread intuition, the quadratic surface fitted to the pixel-level cross-correlation values in the neighborhood of the cross-correlation peak does not always have a maximum. The main contribution of this paper then consists in establishing the mathematical conditions ensuring the existence of a maximum of this fitted quadratic surface, based on a rigorous analysis. Algorithm modifications for handling the failure cases of the QSF method are also proposed in this paper, in order to consolidate it for subpixel motion extraction. Experimental results based on two typical types of images are also reported.Microfluidic systems are of paramount importance in various fields such as medicine, biology, and pharmacy. Despite the plethora of methods, accurate dosing and mixing of small doses of liquid reagents remain challenges for microfluidics. In this paper, we present a microfluidic device that uses two micro pumps and an alternating drive pattern to fill a microchannel. With a capacitive sensor system, we monitored the fluid process and controlled the micro pumps. In a first experiment, the system was set up to generate a 11 mixture between two fluids while using a range of fluid packet sizes from 0.25 to 2 µL and pumping frequencies from 50 to 100 Hz. In this parameter range, a dosing accuracy of 50.3 ± 0.9% was reached, validated by a gravimetric measurement. Other biased mixing ratios were tested as well and showed a deviation of 0.3 ± 0.3% from the targeted mixing ratio. In a second experiment, Trypan blue was used to study the mixing behavior of the system. Within one to two dosed packet sets, the two reagents were reliably mixed. The results are encouraging for future use of micro pumps and capacitive sensing in demanding microfluidic applications.We present a device based on low-cost electrochemical and optical sensors, designed to be attached to bicycle handlebars, with the aim of monitoring the air quality in urban environments. The system has three electrochemical sensors for measuring NO2 and O3 and an optical particle-matter (PM) sensor for PM2.5 and PM10 concentrations. The electronic instrumentation was home-developed for this application. To ensure a constant air flow, the input fan of the particle sensor is used as an air supply pump to the rest of the sensors. Eight identical devices were built; two were collocated in parallel with a reference urban-air-quality-monitoring station and calibrated using a neural network (R2 > 0.83). Several bicycle routes were carried out throughout the city of Badajoz (Spain) to allow the device to be tested in real field conditions. An air-quality index was calculated to facilitate the user's understanding. The results show that this index provides data on the spatiotemporal variability of pollutants between the central and peripheral areas, including changes between weekdays and weekends and between different times of the day, thus providing valuable information for citizens through a dedicated cloud-based data platform.In recent times, electronic portfolios (e-portfolios) are being increasingly used by students and lifelong learners as digital online multimedia résumés that showcase their skill sets and achievements. E-portfolios require secure, reliable, and privacy-preserving credential issuance and verification mechanisms to prove learning achievements. However, existing systems provide private institution-wide centralized solutions that primarily rely on trusted third parties to issue and verify credentials. Furthermore, they do not enable learners to own, control, and share their e-portfolio information across organizations, which increases the risk of forged and fraudulent credentials. Therefore, we propose a consortium blockchain-based e-portfolio management scheme that is decentralized, secure, and trustworthy. Smart contracts are leveraged to enable learners to completely own, publish, and manage their e-portfolios, and also enable potential employers to verify e-portfolio credentials and artifacts without relying on trusted third parties. Blockchain is used as an immutable distributed ledger that records all transactions and logs for tamper-proof trusted data provenance, accountability, and traceability. This system guarantees the authenticity and integrity of user credentials and e-portfolio data. Decentralized identifiers and verifiable credentials are used for user profile identification, authentication, and authorization, whereas verifiable claims are used for e-portfolio credential proof authentication and verification. We have designed and implemented a prototype of the proposed scheme using a Quorum consortium blockchain network. Based on the evaluations, our solution is feasible, secure, and privacy-preserving. It offers excellent performance.The 'intention' classification of a user question is an important element of a task-engine driven chatbot. The essence of a user question's intention understanding is the text classification. The transfer learning, such as BERT (Bidirectional Encoder Representations from Transformers) and ERNIE (Enhanced Representation through Knowledge Integration), has put the text classification task into a new level, but the BERT and ERNIE model are difficult to support high QPS (queries per second) intelligent dialogue systems due to computational performance issues. In reality, the simple classification model usually shows a high computational performance, but they are limited by low accuracy. In this paper, we use knowledge of the ERNIE model to distill the FastText model; the ERNIE model works as a teacher model to predict the massive online unlabeled data for data enhancement, and then guides the training of the student model of FastText with better computational efficiency. The FastText model is distilled by the ERNIE model in chatbot intention classification. This not only guarantees the superiority of its original computational performance, but also the intention classification accuracy has been significantly improved.Acoustic manipulation of microparticles and cells has attracted growing interest in biomedical applications. In particular, the use of acoustic waves to concentrate particles plays an important role in enhancing the detection process by biosensors. Here, we demonstrated microparticle concentration within sessile droplets placed on the hydrophobic surface using the flexural wave. The design benefits from streaming flow induced by the Lamb wave propagated in the glass waveguide to manipulate particles in the droplets. Microparticles will be concentrated at the central area of the droplet adhesion plane based on the balance among the streaming drag force, gravity, and buoyancy at the operating frequency. We experimentally demonstrated the concentration of particles of various sizes and tumor cells. Using numerical simulation, we predicted the acoustic pressure and streaming flow pattern within the droplet and characterized the underlying physical mechanisms for particle motion. The design is more suitable for micron-sized particle preparation, and it can be valuable for various biological, chemical, and medical applications.This paper presents a fiber optic, liquid level sensor system based on a pair of fiber Bragg gratings (FBGs), embedded in a circular silicone (PDMS-polydimethylsiloxane) rubber diaphragm. The measurement principles of this sensor, whose diaphragm structure is about 2.2 mm thick with 45 mm in diameter, are introduced. To analyze the linearity and sensitivity of the sensor, the diaphragm was subjected to compression tests as well as to liquid level loading and unloading. The force and liquid level increase tests showed that inserting two FBGs (0.99453 for force and 0.99163 for liquid level) in the diaphragm resulted in a system with greater linearity than that with individual FBGs. This occurred where FBG1 showed 0.97684 for force and 0.98848 for liquid level and FBG2 presented 0.89461 for force and 0.93408 for liquid level. However, the compression and water level decrease tests showed that the system (R2 = 0.97142) had greater linearity with FBG2 (0.94123) and lower linearity with FBG1 (0.98271). Temperature characterization was also performed, and we found that sensitivity to FBG1 temperature variation was 11.73 pm/°C and for FGB2 it was 10.29 pm/°C. Temperature sensitivity was improved for both FBGs when compared with uncoated FBGs with typical values of 9.75 pm/°C. Therefore, the proposed FBG-based sensor system is capable of simultaneous measurement of force and temperature in a compact diaphragm-embedded system.This study proposes the use of a non-destructive testing technique, based on piezoelectric bender element tests, to determine the initial and final setting times of metakaolin geopolymer pastes. (1) Background Metakaolin geopolymer is a new eco-friendly building material that develops strength rapidly and is high in compressive strength. (2) Methods The initial and the final setting times were investigated via bender element and Vicat needle tests. Metakaolin powder was prepared by treating kaolin at 0, 200, 800, 1000, and 1200 °C. All metakaolin powder samples were then mixed with geopolymer solution at different mixing ratios of 0.81.0, 1.01.0, 1.21.0, and 1.51.0. The geopolymer solution was prepared by adding 10 normal concentrations of sodium hydroxide (10 N NaOH) to sodium silicate (Na2SiO3) at various solution ratios of 1.01.0, 1.01.2, 1.01.5, 1.02.0, 1.21.0, 1.51.0 and 2.01.0. (3) Results The optimum temperature for treating metakaolin is established at 1000 °C, with a mixing ratio between the metakaolin powder and the geopolymer solution of 1.
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