Non-linear Autoregressive Neural Networks to Forecast Short-Term Solar Radiation for Photovoltaic Energy Predictions

Nowadays, green energy is considered as a viable solution to hinder CO2 emissions and greenhouse effects. Indeed, it is expected that Renewable Energy Sources (RES) will cover 40% of the total energy request by 2040. This will move forward decentralized and cooperative power distribution systems also called smart grids. Among RES, solar energy will play a crucial role. However, reliable models and tools are needed to forecast and estimate with a good accuracy the renewable energy production in short-term time periods. These tools will unlock new services for smart grid management. In this paper, we propose an innovative methodology for implementing two different non-linear autoregressive neural networks to forecast Global Horizontal Solar Irradiance (GHI) in short-term time periods (i.e. from future 15 to 120min). Both neural networks have been implemented, trained and validated exploiting a dataset consisting of four years of solar radiation values collected by a real weather station. We also present the experimental results discussing and comparing the accuracy of both neural networks. Then, the resulting GHI forecast is given as input to a Photovoltaic simulator to predict energy production in short-term time periods. Finally, we present the results of this Photovoltaic energy estimation discussing also their accuracy

The separation-preconcentration and determination of ultra-trace gold in water and solid samples by dispersive liquid-liquid microextraction using 4-ethyl-1(2-(4-(4-nitrophenyl)piperazin-1-yl)acetyl)thiosemicarbazide) as chelating agent and flame atomic absorption spectrometry

Soylak, Mustafa/0000-0002-1017-0244WOS: 000431928200014A selective separation and preconcentration method for the determination of gold ions in water and ore samples has been developed using dispersive liquid-liquid microextraction, followed by flame atomic absorption spectrometry. 4-Ethyl-1(2-(4-(4-nitrophenyl)piperazin-1-yl)acetyl)thiosemicarbazide) (NPPTSC) has been used for the first time as new chelating reagent. A mixture of ethanol (dispersive solvent) and carbon tetrachloride (extraction solvent) was used. Some parameters affecting the extraction procedure including the type and volume of the extracting and dispersive solvents, HNO3 concentration, the chelating agent amount, volume of sample, and foreign ions have optimized. Also, the complex formation between gold ions and the ligand has been investigated in a methanol-water solution (1:1) using UV-visible spectrometry. The spectrophotometric titration data showed that of Au-NPPTSC complex composition was found to be 3:2. After optimizing the instrumental and experimental parameters, we achieved a detection limit of 1.5 mu g L-1, a preconcentration factor of 50, and a linear dynamic range of 10.0-400.0 mu g L-1. The relative standard deviation obtained 2.1% at 50 mu g L-1 for gold ions (n = 10). The proposed method was successfully performed for the determination of gold in certified reference material, environmental water, and ore samples.Scientific Research Projects of Giresun University [101016-131]The financial support of the unit of the Scientific Research Projects of Giresun University (Project No.: 101016-131) (Giresun Turkey) is gratefully acknowledged and also gratitude to Karadeniz Technical University (Trabzon, Turkey) for laboratory facilities

Stem Cell-Based and Tissue Engineering Approaches for Skeletal Muscle Repair

Skeletal muscle tissue exhibits significant regeneration capacity upon injury or disease. This intrinsic regeneration potential is orchestrated by stem cells termed satellite cells, which undergo activation and differentiation in response to muscle insult, giving rise to fusion-competent myogenic progenitors responsible for tissue rejuvenation. Skeletal muscle diseases such as Duchenne muscular dystro-phy are characterized by progressive loss of muscle mass which precipitates reduced motility, paralysis, and in some occurrences untimely death. A manifold of muscle pathologies involve a failure to efficiently regenerate the muscle tissue, rendering stem cell-based approaches an attractive therapeutic strategy. Here we will present past and contemporary methods to treat skeletal muscle degeneration by stem cell therapy, covering prominent challenges facing this technology and potential means to overcome current hurdles. A primary focus of this chapter is directed toward illustrating innovative ways to utilize stem cells alone or in conjunction with biomaterials and tissue engineering techniques to remedy Duchenne muscular dystrophy or volumetric muscle loss