Fax +41 61 306 12 34 E-Mail karger@karger

ing throughout infancy, but FUF are widely used and thus the outdated current FUF standard should be revised. Like IF, FUF serve as breast milk substitutes; hence their marketing should respect appropriate standards. The compositional requirements for FUF for infants from 6 months onwards presented here were unanimously agreed upon. For some nutrients, the compositional requirements for FUF differ from those of IF due to differing needs with infant maturation as well as a rising contribution of an increasingly diversified diet with advancing age. FUF should be fed with adequate complementary feeding that is also appropriate for partially breastfed infants. FUF could be fed also after the age of 1 year without safety concerns, but different compositional requirements should be applied for optimal, age-adapted milk-based formulations for young children used only after the age of 1 year. This has not been considered as part of this review and should be the subject of further consideration

High Quality Measurements of the Solar Spectrum for Simulation of Multi-junction Photovoltaic Cell Yields

There is increasing interest in understanding how high-efficiency multi-junction photovoltaic cells perform in the field under varying spectral distributions of solar irradiation. The aim of this work is to produce a spectral irradiance dataset for Nicosia, Cyprus for a typical meteorological year that can be used to analyse cell performances. This is being done through a combination of spectral and meteorological measurements and simulation using the SMARTS atmospheric radiative transfer model. To date, 6 months of data have been collected and a variety of derived parameters are now being compared against the model. Results so far have indicated that a maritime aerosol model is most appropriate for this location. However, initial analyses have highlighted a persistently higher blue content in the measured spectra, which may indicate a measurement artefact. This demonstrates how SMARTS can also be used as a tool to verify spectral measurements of solar irradiation.JRC.F.7-Renewables and Energy Efficienc

Comparative Analysis of Machine Learning Models for Day-Ahead Photovoltaic Power Production Forecasting

A main challenge for integrating the intermittent photovoltaic (PV) power generation remains the accuracy of day-ahead forecasts and the establishment of robust performing methods. The purpose of this work is to address these technological challenges by evaluating the day-ahead PV production forecasting performance of different machine learning models under different supervised learning regimes and minimal input features. Specifically, the day-ahead forecasting capability of Bayesian neural network (BNN), support vector regression (SVR), and regression tree (RT) models was investigated by employing the same dataset for training and performance verification, thus enabling a valid comparison. The training regime analysis demonstrated that the performance of the investigated models was strongly dependent on the timeframe of the train set, training data sequence, and application of irradiance condition filters. Furthermore, accurate results were obtained utilizing only the measured power output and other calculated parameters for training. Consequently, useful information is provided for establishing a robust day-ahead forecasting methodology that utilizes calculated input parameters and an optimal supervised learning approach. Finally, the obtained results demonstrated that the optimally constructed BNN outperformed all other machine learning models achieving forecasting accuracies lower than 5%

Direct Short-Term Net Load Forecasting Based on Machine Learning Principles for Solar-Integrated Microgrids

Accurate net load forecasting is a cost-effective technique, crucial for the planning, stability, reliability, and integration of variable solar photovoltaic (PV) systems in modern power systems. This work presents a direct short-term net load forecasting (STNLF) methodology for solar-integrated microgrids by leveraging machine learning (ML) principles. The proposed data-driven method comprises of an initial input feature engineering and filtering step, construction of forecasting model using Bayesian neural networks, and an optimization stage. The performance of the proposed model was validated on historical net load data obtained from a university campus solar-powered microgrid. The results demonstrated the effectiveness of the model for providing accurate and robust STNLF. Specifically, the optimally constructed model yielded a normalized root mean square error of 3.98% when benchmarked using a 1-year historical microgrid data. The $k$ -fold cross-validation method was then used and proved the stability of the forecasting model. Finally, the obtained ML-based forecasts demonstrated improvements of 17.77% when compared against forecasts of a baseline naïve persistence model. To this end, this work provides insights on how to construct high-performance STNLF models for solar-integrated microgrids. Such insights on the development of accurate STNLF architectures can have positive implications in actual microgrid decision-making by utilities/operators

Sequential Role of SOXB2 Factors in GABAergic Neuron Specification of the Dorsal Midbrain

Studies proposed a model for embryonic neurogenesis where the expression levels of the SOXB2 and SOXB1 factors regulate the differentiation status of the neural stem cells. However, the precise role of the SOXB2 genes remains controversial. Therefore, this study aims to investigate the effects of individual deletions of the SOX21 and SOX14 genes during the development of the dorsal midbrain. We show that SOX21 and SOX14 function distinctly during the commitment of the GABAergic lineage. More explicitly, deletion of SOX21 reduced the expression of the GABAergic precursor marker GATA3 and BHLHB5 while the expression of GAD6, which marks GABAergic terminal differentiation, was not affected. In contrast deletion of SOX14 alone was sufficient to inhibit terminal differentiation of the dorsal midbrain GABAergic neurons. Furthermore, we demonstrate through gain-of-function experiments, that despite the homology of SOX21 and SOX14, they have unique gene targets and cannot compensate for the loss of each other. Taken together, these data do not support a pan-neurogenic function for SOXB2 genes in the dorsal midbrain, but instead they influence, sequentially, the specification of GABAergic neurons

Potential of photovoltaic systems in countries with high solar irradiation

Renewable energy sources derived principally from solar energy have been gaining ground over the last few years and are now beginning to contribute to the global energy mix. Solar energy in the form of direct electricity conversion (photovoltaics) is already very popular in countries such as the United States, Germany and Japan. The enormous potential of photovoltaic (PV) technology is also obvious and favourable in countries with high irradiation such as the Mediterranean region. The objective of this paper is to review the different up and coming PV technologies, to explore the potential of different PV systems in countries with high solar irradiation and to compare their performance through the assessment of thirteen different types of PV systems that have been installed side by side in Nicosia, Cyprus and Stuttgart, Germany. Finally useful insight into the performance of the PV systems as a function of the meteorological conditions and location will be highlighted.Photovoltaics Mono-crystalline silicon Multi-crystalline silicon Concentrator Thin film photovoltaic potential