This study explores the use of natural, ecological coagulant-flocculants to reduce suspended particles in water. Three compounds were tested, namely diatomaceous earth, calcium lactate and lactic acid. For this purpose, experiments in jar tests were carried out and the best compound was submitted to an optimization in order to evaluate the most significant parameters affecting its use as coagulant-flocculant. First results evidenced that lactic acid remove 71% of the suspended particles during the first five minutes, and up to 83% during the first 15 min. To optimize its use, the range of suspended particles concentration, lactic acid dose and salinity gradient was tested by means of an incomplete 33 factorial design. This technique allows reducing the number of experiments to be carried out through a response surface methodology, which enables to infer the values of the dependent variables in not studied situations, by means of predictive equations. As a result of the experiments carried out, optimal conditions to remove suspended particles were set at a lactic acid concentration of 1.75 g·L-1. As lactic acid may be obtained biotechnologically from organic wastes, this use supposes a promising area by keeping products and materials in use and contributing to a circular economy.Fish schooling implies an awareness of the swimmers for their companions. In flow mediated environments, in addition to visual cues, pressure and shear sensors on the fish body are critical for providing quantitative information that assists the quantification of proximity to other fish. Here we examine the distribution of sensors on the surface of an artificial swimmer so that it can optimally identify a leading group of swimmers. We employ Bayesian experimental design coupled with numerical simulations of the two-dimensional Navier Stokes equations for multiple self-propelled swimmers. The follower tracks the school using information from its own surface pressure and shear stress. https://www.selleckchem.com/products/ccs-1477-cbp-in-1-.html We demonstrate that the optimal sensor distribution of the follower is qualitatively similar to the distribution of neuromasts on fish. Our results show that it is possible to identify accurately the center of mass and the number of the leading swimmers using surface only information.BACKGROUND A nanomaterial-based electronic-skin (E-Skin) wearable sensor has been successfully used for detecting and measuring body movements such as finger movement and foot pressure. The ultrathin and highly sensitive characteristics of E-Skin sensor make it a suitable alternative for continuously out-of-hospital lumbar-pelvic movement (LPM) monitoring. Monitoring these movements can help medical experts better understand individuals' low back pain experience. However, there is a lack of prior studies in this research area. Therefore, this paper explores the potential of E-Skin sensors to detect and measure the anatomical angles of lumbar-pelvic movements by building a linear relationship model to compare its performance to clinically validated inertial measurement unit (IMU)-based sensing system (ViMove). METHODS The paper first presents a review and classification of existing wireless sensing technologies for monitoring of body movements, and then it describes a series of experiments performed with E-Skidividuals with low back pain.In practical applications, how to achieve a perfect balance between high accuracy and computational efficiency can be the main challenge for simultaneous localization and mapping (SLAM). To solve this challenge, we propose SD-VIS, a novel fast and accurate semi-direct visual-inertial SLAM framework, which can estimate camera motion and structure of surrounding sparse scenes. In the initialization procedure, we align the pre-integrated IMU measurements and visual images and calibrate out the metric scale, initial velocity, gravity vector, and gyroscope bias by using multiple view geometry (MVG) theory based on the feature-based method. At the front-end, keyframes are tracked by feature-based method and used for back-end optimization and loop closure detection, while non-keyframes are utilized for fast-tracking by direct method. This strategy makes the system not only have the better real-time performance of direct method, but also have high accuracy and loop closing detection ability based on feature-based method. At the back-end, we propose a sliding window-based tightly-coupled optimization framework, which can get more accurate state estimation by minimizing the visual and IMU measurement errors. In order to limit the computational complexity, we adopt the marginalization strategy to fix the number of keyframes in the sliding window. Experimental evaluation on EuRoC dataset demonstrates the feasibility and superior real-time performance of SD-VIS. Compared with state-of-the-art SLAM systems, we can achieve a better balance between accuracy and speed.This study presents a U-shaped optical fiber developed for a facile application of microRNA detection. It is fabricated by the lamping process and packaged in a quartz tube to eliminate human negligence. In addition, silanization and electrostatic self-assembly are employed to bind gold nanoparticles and miRNA-133a probe onto the silicon dioxide of the fiber surface. For Mahlavu of hepatocellular carcinoma (HCC), detection is determined by the wavelength shift and transmission loss of a U-shaped optical fiber biosensor. The spectral sensitivity of wavelength and their coefficient of determination are found at -218.319 nm/ ng/mL and 0.839, respectively. Concurrently, the sensitivity of transmission loss and their coefficient of determination are found at 162.394 dB/ ng/mL and 0.984, respectively. A method for estimating the limit of detection of Mahlavu is at 0.0133 ng/mL. The results show that the proposed U-shaped biosensor is highly specific to miRNA-133a and possesses good sensitivity to variations in specimen concentration. As such, it could be of substantial value in microRNA detection.Unsupervised change detection approaches, which are relatively straightforward and easy to implement and interpret, and which require no human intervention, are widely used in change detection. Polarimetric synthetic aperture radar (PolSAR), which has an all-weather response capability with increased polarimetric information, is a key tool for change detection. However, for PolSAR data, inadequate evaluation of the difference image (DI) map makes the threshold-based algorithms incompatible with the true distribution model, which causes the change detection results to be ineffective and inaccurate. In this paper, to solve these problems, we focus on the generation of the DI map and the selection of the optimal threshold. An omnibus test statistic is used to generate the DI map from multi-temporal PolSAR images, and an improved Kittler and Illingworth algorithm based on either Weibull or gamma distribution is used to obtain the optimal threshold for generating the change detection map. Multi-temporal PolSAR data obtained by the Radarsat-2 sensor over Wuhan in China are used to verify the efficiency of the proposed method.