In the dielectric strength test, it was found that nanodoping can effectively improve the direct current (DC) and alternating current (AC) breakdown field strength and the stability after the thermal aging. The dielectric constant of nanocomposite can be reduced, and the dielectric loss had no obvious change during the aging process. Moreover, the zeolite/LDPE nanocomposite with the doping concentration of 1 wt % had the best performance, for the nano-zeolite was better dispersed.The widespread use of glass fibre reinforced polymer (GFRP) bars in reinforced concrete (RC) elements has yet been limited due to the anisotropic and non-homogeneous material behaviour of GFRP. https://www.selleckchem.com/products/indy.html The material characteristics of GFRP bars from different manufacturers vary as a function of several factors. Several standards have developed various procedures to investigate the mechanical characteristics of GFRP bars, but universal methods to test different types and diameters of GFRP bars in tension have not been fully developed. Due to the lack of such a standardized test procedure, there are some doubts and gaps in terms of the behaviour of GFRP bars in tension, which has led to lack of reliable information on their tensile properties. The determination of tensile characteristics of GFRP bars, including the tensile strength, modulus of elasticity, and ultimate strain, according to various test standards, is the main subject of the paper. This paper reports test results for tensile characterization obtained on four types of GFRP bars from four manufacturers with six various diameters. Moreover, the study compares various test procedures according to seven standards to characterize the tensile properties of GFRP bars, to examine the proposed test procedures, and to reveal main differences.There has been an increasing interest in the development of antimicrobial peptides (AMPs) and their synthetic mimics as a novel class of antibiotics to overcome the rapid emergence of antibiotic resistance. Recently, phenylglyoxamide-based small molecular AMP mimics have been identified as potential leads to treat bacterial infections. In this study, a new series of biphenylglyoxamide-based small molecular AMP mimics were synthesised from the ring-opening reaction of N-sulfonylisatin bearing a biphenyl backbone with a diamine, followed by the conversion into tertiary ammonium chloride, quaternary ammonium iodide and guanidinium hydrochloride salts. Structure-activity relationship studies of the analogues identified the octanesulfonyl group as being essential for both Gram-positive and Gram-negative antibacterial activity, while the biphenyl backbone was important for Gram-negative antibacterial activity. The most potent analogue was identified to be chloro-substituted quaternary ammonium iodide salt 15c, which possesses antibacterial activity against both Gram-positive (MIC against Staphylococcus aureus = 8 μM) and Gram-negative bacteria (MIC against Escherichia coli = 16 μM, Pseudomonas aeruginosa = 63 μM) and disrupted 35% of pre-established S. aureus biofilms at 32 μM. Cytoplasmic membrane permeability and tethered bilayer lipid membranes (tBLMs) studies suggested that 15c acts as a bacterial membrane disruptor. In addition, in vitro toxicity studies showed that the potent compounds are non-toxic against human cells at therapeutic dosages.(1) Background The influence of ketogenic diet on physical fitness remains controversial. We performed a randomized controlled trial to compare the effect of cyclical ketogenic reduction diet (CKD) vs. nutritionally balanced reduction diet (RD) on body composition, muscle strength, and endurance performance. (2) Methods 25 healthy young males undergoing regular resistance training combined with aerobic training were randomized to CKD (n = 13) or RD (n = 12). Body composition, muscle strength and spiroergometric parameters were measured at baseline and after eight weeks of intervention. (3) Results Both CKD and RD decreased body weight, body fat, and BMI. Lean body mass and body water decreased in CKD and did not significantly change in RD group. Muscle strength parameters were not affected in CKD while in RD group lat pull-down and leg press values increased. Similarly, endurance performance was not changed in CKD group while in RD group peak workload and peak oxygen uptake increased. (4) Conclusions Our data show that in healthy young males undergoing resistance and aerobic training comparable weight reduction were achieved by CKD and RD. In RD group; improved muscle strength and endurance performance was noted relative to neutral effect of CKD that also slightly reduced lean body mass.Potato is the largest non-cereal food crop in the world. Timely estimation of end-of-season tuber production using in-season information can inform sustainable agricultural management decisions that increase productivity while reducing impacts on the environment. Recently, unmanned aerial vehicles (UAVs) have become increasingly popular in precision agriculture due to their flexibility in data acquisition and improved spatial and spectral resolutions. In addition, compared with natural color and multispectral imagery, hyperspectral data can provide higher spectral fidelity which is important for modelling crop traits. In this study, we conducted end-of-season potato tuber yield and tuber set predictions using in-season UAV-based hyperspectral images and machine learning. Specifically, six mainstream machine learning models, i.e., ordinary least square (OLS), ridge regression, partial least square regression (PLSR), support vector regression (SVR), random forest (RF), and adaptive boosting (AdaBoost), were developed and compared across potato research plots with different irrigation rates at the University of Wisconsin Hancock Agricultural Research Station. Our results showed that the tuber set could be better predicted than the tuber yield, and using the multi-temporal hyperspectral data improved the model performance. Ridge achieved the best performance for predicting tuber yield (R2 = 0.63) while Ridge and PLSR had similar performance for predicting tuber set (R2 = 0.69). Our study demonstrated that hyperspectral imagery and machine learning have good potential to help potato growers efficiently manage their irrigation practices.