The non-selective property of conventional polyurethane (PU) foam tends to lower its oil absorption efficiency. To address this issue, we modified the surface properties of PU foam using a rapid solvent-free surface functionalization approach based on the chemical vapor deposition (CVD) method to establish an extremely thin yet uniform coating layer to improve foam performance. The PU foam was respectively functionalized using different monomers, i.e., perfluorodecyl acrylate (PFDA), 2,2,3,4,4,4-hexafluorobutyl acrylate (HFBA), and hexamethyldisiloxane (HMDSO), and the effect of deposition times (1, 5 and 10 min) on the properties of foam was investigated. The results showed that all the modified foams demonstrated a much higher water contact angle (i.e., greater hydrophobicity) and greater absorption capacities compared to the control PU foam. This is due to the presence of specific functional groups, e.g., fluorine (F) and silane (Si) in the modified PU foams. Of all, the PU/PHFBAi foam exhibited the highest absorption capacities, recording 66.68, 58.15, 53.70, and 58.38 g/g for chloroform, acetone, cyclohexane, and edible oil, respectively. These values were 39.19-119.31% higher than that of control foam. The promising performance of the PU/PHFBAi foam is due to the improved surface hydrophobicity attributed to the original perfluoroalkyl moieties of the HFBA monomer. The PU/PHFBAi foam also demonstrated a much more stable absorption performance compared to the control foam when both samples were reused for up to 10 cycles. This clearly indicates the positive impact of the proposed functionalization method in improving PU properties for oil absorption processes.This paper proposes a high-speed low-cost VLSI system capable of on-chip online learning for classifying address-event representation (AER) streams from dynamic vision sensor (DVS) retina chips. The proposed system executes a lightweight statistic algorithm based on simple binary features extracted from AER streams and a Random Ferns classifier to classify these features. The proposed system's characteristics of multi-level pipelines and parallel processing circuits achieves a high throughput up to 1 spike event per clock cycle for AER data processing. Thanks to the nature of the lightweight algorithm, our hardware system is realized in a low-cost memory-centric paradigm. In addition, the system is capable of on-chip online learning to flexibly adapt to different in-situ application scenarios. The extra overheads for on-chip learning in terms of time and resource consumption are quite low, as the training procedure of the Random Ferns is quite simple, requiring few auxiliary learning circuits. An FPGA prototype of the proposed VLSI system was implemented with 9.5~96.7% memory consumption and less then 11% computational and logic resources on a Xilinx Zynq-7045 chip platform. It was running at a clock frequency of 100 MHz and achieved a peak processing throughput up to 100 Meps (Mega events per second), with an estimated power consumption of 690 mW leading to a high energy efficiency of 145 Meps/W or 145 event/μJ. We tested the prototype system on MNIST-DVS, Poker-DVS, and Posture-DVS datasets, and obtained classification accuracies of 77.9%, 99.4% and 99.3%, respectively. Compared to prior works, our VLSI system achieves higher processing speeds, higher computing efficiency, comparable accuracy, and lower resource costs.The gut microbiota, which consists of all bacteria, viruses, fungus, and protozoa living in the intestine, and the immune system have co-evolved in a symbiotic relationship since the origin of the immune system. The bacterial community forming the microbiota plays an important role in the regulation of multiple aspects of the immune system. This regulation depends, among other things, on the production of a variety of metabolites by the microbiota. These metabolites range from small molecules to large macro-molecules. https://www.selleckchem.com/products/idasanutlin-rg-7388.html All types of immune cells from the host interact with these metabolites resulting in the activation of different pathways, which result in either positive or negative responses. The understanding of these pathways and their modulations will help establish the microbiota as a therapeutic target in the prevention and treatment of a variety of immune-related diseases.Considering the variation of the received signal strength indicator (RSSI) in wireless networks, the objective of this study is to investigate and propose a method of indoor localization in order to improve the accuracy of localization that is compromised by RSSI variation. For this, quartile analysis is used for data pre-processing and the k-nearest neighbors (kNN) classifier is used for localization. In addition to the tests in a real environment, simulations were performed, varying many parameters related to the proposed method and the environment. In the real environment with reference points of 1.284 density per unit area (RPs/m2), the method presents zero-mean error in the localization in test points (TPs) coinciding with the RPs. In the simulated environment with a density of 0.327 RPs/m2, a mean error of 0.490 m for the localization of random TPs was achieved. These results are important contributions and allow us to conclude that the method is promising for locating objects in indoor environments.Recent studies have shown the ability of electrochemical methods to sense and determine, even at very low concentrations, the presence and quantity of molecules or analytes including pharmaceutical samples. Furthermore, analytical methods, such as high-pressure liquid chromatography (HPLC), can also detect the presence and quantity of peptides at very low concentrations, in a simple, fast, and efficient way, which allows the monitoring of conjugation reactions and its completion. Graphite/SiO2 film electrodes and HPLC methods were previously shown by our group to be efficient to detect drug molecules, such as losartan. We now use these methods to detect the conjugation efficiency of a peptide from the immunogenic region of myelin oligodendrocyte to a carrier, mannan. The HPLC method furthermore confirms the stability of the peptide with time in a simple one pot procedure. Our study provides a general method to monitor, sense and detect the presence of peptides by effectively confirming the conjugation efficiency.