Protective effect of (-)-epigallocatechin gallate on ultraviolet b-induced skin damage in hairless mice

Purpose: To investigate the protective effect of green tea (-)-epigallocatechin gallate (EGCg) on ultraviolet B (UV-B)-induced skin damages in hairless mice in order to develop a natural sunscreen compound for use in skin care products and cosmetics. Methods: EGCg was dissolved in acetone at concentrations of 1.0, 10.0 and 50.0 mg/mL, and topically applied to the skin of hairless mice at doses of 0.2 mL/cm2, with acetone as control. The mice were then irradiatd m2 UV-B for 30 min daily. EGCg treatment and UV-B irradiation were carried out daily for 28 consecutive days. The mice were then sacrificed and their dorsal skin examined by transmission electron microscopy (TEM) on the 28th day. Results: UV-B irradiation induced severe macroscopic skin damage including chapping, cracking and abnormal desquamation in the treated hairless mice. EGCg showed dose-dependent protective effects against UV-B induced damage on the skin. Treatments with 10.0 and 50.0 mg/mL EGCg alleviated UVB-induced skin damage by suppressing both keratinocyte apoptosis and mitochondrial dysfunction, along with inhibiting the production of melanin pigment. Conclusion: Topical application of green tea EGCg shows dose-dependent protective effect against UV-B-induced damage on hairless mouse skin. Thus, the plant compound can potentially be used as an alternative agent for photoprotection against UV-B exposure.

Training of interval type-2 fuzzy logic system using extreme learning machine for load forecasting

Extreme learning machine (ELM) is originally proposed for single- hidden layer feed-forward neural networks (SLFN). From the functional equivalence of fuzzy logic systems and SLFN, the fuzzy logic systems can be interpreted as a special case of SLFN under some mild conditions. Hence the fuzzy logic systems can be trained using SLFN's learning algorithms. Considering the same equivalence, ELM is utilized here to train interval type-2 fuzzy logic systems (IT2FLSs). Based on the working principle of the ELM, the parameters of the antecedent of IT2FLSs are randomly generated while the consequent part of IT2FLSs is optimized using Moore-Penrose generalized inverse of ELM. Application of the developed model to electricity load forecasting is another novelty of the research work. Experimental results shows better forecasting performance of the proposed model over the two frequently used forecasting models.

Miscibility, UV resistance, thermal degradation, and mechanical properties of PMMA/SAN blends and their composites with MWCNTs

Poly(methyl methacrylate)/poly(styrene-co-acrylonitrile) (PMMA/SAN) blends, with varying concentrations, were prepared by melt-mixing technique. The miscibility is ensured by fixing the acrylonitrile (AN) content of styrene acrylonitrile (SAN) as 25% by weight. The blends were transparent as well. The Fourier transform infrared spectroscopic (FTIR) studies did not reveal any specific interactions, supporting the well accepted 'copolymer repulsion effect' as the driving mechanism for miscibility. Addition of SAN increased the stability of PMMA towards ultraviolet (UV) radiations and thermal degradation. Incorporation of even 0.05% by weight of multi-walled carbon nanotubes (MWCNTs) significantly improved the UV absorbance and thermal stability. Moreover, the composites exhibited good strength and modulus. However, at higher concentrations of MWCNTs (0.5 and 1% by weight) the thermo-mechanical properties experienced deterioration, mainly due to the agglomeration of MWCNTs. It was observed that composites with 0.05% by weight of finely dispersed and well distributed MWCNTs provided excellent protection in most extreme climatic conditions. Thus, PMMA/SAN/MWCNTs composites can act as excellent light screens and may be useful, as cost-effective UV absorbers, in the outdoor applications.

Rock slope stability analyses using extreme learning neural network and terminal steepest descent algorithm

The analysis of rock slope stability is a classical problem for geotechnical engineers. However, for practicing engineers, proper software is not usually user friendly, and additional resources capable of providing information useful for decision-making are required. This study developed a convenient tool that can provide a prompt assessment of rock slope stability. A nonlinear input-output mapping of the rock slope system was constructed using a neural network trained by an extreme learning algorithm. The training data was obtained by using finite element upper and lower bound limit analysis methods. The newly developed techniques in this study can either estimate the factor of safety for a rock slope or obtain the implicit parameters through back analyses. Back analysis parameter identification was performed using a terminal steepest descent algorithm based on the finite-time stability theory. This algorithm not only guarantees finite-time error convergence but also achieves exact zero convergence, unlike the conventional steepest descent algorithm in which the training error never reaches zero.

Long-term Trends in Magnitude and Frequency of Extreme Rainfall Events in Florida

This study computed trends in extreme precipitation events of Florida for 1950-2010. Hourly aggregated rainfall data from 24 stations of the National Climatic Data Centre were analyzed to derive time-series of extreme rainfalls for 12 durations, ranging from 1 hour to 7 day. Non-parametric Mann-Kendall test and Theil-Sen Approach were applied to detect the significance of trends in annual maximum rainfalls, number of above threshold events and average magnitude of above threshold events for four common analysis periods. Trend Free Pre-Whitening (TFPW) approach was applied to remove the serial correlations and bootstrap resampling approach was used to detect the field significance of trends. The results for annual maximum rainfall revealed dominant increasing trends at the statistical significance level of 0.10, especially for hourly events in longer period and daily events in recent period. The number of above threshold events exhibited strong decreasing trends for hourly durations in all time periods.

Effects in ultrafast laser micromachining PMMA-based optical fibre grating

Ultrafast laser owns extreme small beam size and high pulse intensity which enable spatial localised modification either on the surface or in the bulk of materials. Therefore, ultrafast laser has been widely used to micromachine optical fibres to alter optical structures. In order to do the precise control of the micromachining process to achieve the desired structure and modification, investigations on laser parameters control should be carried out to make better understanding of the effects in the laser micromachining process. These responses are important to laser machining, most of which are usually unknown during the process. In this work, we report the real time monitored results of the reflection of PMMA based optical fibre Bragg gratings (POFBGs) during excimer ultraviolet laser micromachining process. Photochemical and thermal effects have been observed during the process. The UV radiation was absorbed by the PMMA material, which consequently induced the modifications in both spatial structure and material properties of the POFBG. The POFBG showed a significant wavelength blue shift during laser micromachining. Part of it attributed to UV absorption converted thermal energy whilst the other did not disappear after POFBG cooling off, which attributed to UV induced photodegradation in POF.

Estimation of Convective Extreme Events over Germany based on Large Scale Parameters

Strong convective events can produce extreme precipitation, hail, lightning or gusts, potentially inducing severe socio-economic impacts. These events have a relatively small spatial extension and, in most cases, a short lifetime. In this study, a model is developed for estimating convective extreme events based on large scale conditions. It is shown that strong convective events can be characterized by a Weibull distribution of radar-based rainfall with a low shape and high scale parameter value. A radius of 90km around a station reporting a convective situation turned out to be suitable. A methodology is developed to estimate the Weibull parameters and thus the occurrence probability of convective events from large scale atmospheric instability and enhanced near-surface humidity, which are usually found on a larger scale than the convective event itself. Here, the probability for the occurrence of extreme convective events is estimated from the KO-index indicating the stability, and relative humidity at 1000hPa. Both variables are computed from ERA-Interim reanalysis. In a first version of the methodology, these two variables are applied to estimate the spatial rainfall distribution and to estimate the occurrence of a convective event. The developed method shows significant skill in estimating the occurrence of convective events as observed at synoptic stations, lightning measurements, and severe weather reports. In order to take frontal influences into account, a scheme for the detection of atmospheric fronts is implemented. While generally higher instability is found in the vicinity of fronts, the skill of this approach is largely unchanged. Additional improvements were achieved by a bias-correction and the use of ERA-Interim precipitation. The resulting estimation method is applied to the ERA-Interim period (1979-2014) to establish a ranking of estimated convective extreme events. Two strong estimated events that reveal a frontal influence are analysed in detail. As a second application, the method is applied to GCM-based decadal predictions in the period 1979-2014, which were initialized every year. It is shown that decadal predictive skill for convective event frequencies over Germany is found for the first 3-4 years after the initialization.

Solar and thermal radiative effects during the 2011 extreme desert dust episode over Portugal

This paper analyses the influence of the extreme Saharan desert dust (DD) event on shortwave (SW) and longwave (LW) radiation at the EARLINET/AERONET Évora station (Southern Portugal) from 4 up to 7 April 2011. There was also some cloud occurrence in the period. In this context, it is essential to quantify the effect of cloud presence on aerosol radiative forcing. A radiative transfer model was initialized with aerosol optical properties, cloud vertical properties and meteorological atmospheric vertical profiles. The intercomparison between the instantaneous TOA shortwave and longwave fluxes derived using CERES and those calculated using SBDART, which was fed with aerosol extinction coefficients derived from the CALIPSO and lidar-PAOLI observations, varying OPAC dataset parameters, was reasonably acceptable within the standard deviations. The dust aerosol type that yields the best fit was found to be the mineral accumulation mode. Therefore, SBDART model constrained with the CERES observations can be used to reliably determine aerosol radiative forcing and heating rates. Aerosol radiative forcings and heating rates were derived in the SW (ARFSw, AHRSw) and LW (ARFLw, AHRLw) spectral ranges, considering a cloud-aerosol free reference atmosphere. We found that AOD at 440 nm increased by a factor of 5 on 6 April with respect to the lower dust load on 4 April. It was responsible by a strong cooling radiative effect pointed out by the ARFSw value (−99 W/m2 for a solar zenith angle of 60°) offset by a warming radiative effect according to ARFLw value (+21.9 W/m2) at the surface. Overall, about 24% and 12% of the dust solar radiative cooling effect is compensated by its longwave warming effect at the surface and at the top of the atmosphere, respectively. Hence, larger aerosol loads could enhance the response between the absorption and re-emission processes increasing the ARFLw with respect to those associated with moderate and low aerosol loads. The unprecedented results derived from this work complement the findings in other regions on the modifications of radiative energy budget by the dust aerosols, which could have relevant influences on the regional climate and will be topics for future investigations.