Abstract To meet consumer requirements and expectations, innovative approaches to combining whey with other ingredients are being explored. The demand for health-promoting drinks containing vitamins, probiotics, prebiotics, minerals, and bioactive components (antioxidants) has risen, propelling market expansion. The purpose of this study was to develop a synbiotic functional whey beverage supplemented with Bifidobacterium animalis, D-allulose, and β-glucan and evaluate its microbiological, physicochemical, and influence on several health indicators in a Wistar rat model. The beverage supplemented with D-allulose had the highest average viable counts of B. animalis (9.20 log10CFU/g) and was the second most preferred in terms of of taste, texture and general acceptability compared to β-glucan-containing beverage. The highest TAS and lowest TOS values were determined in the serum samples of rats belonging to group WA, WG and WAG, respectively. This study might lead to additional studies focusing on specific variables and the relevance of utilizing D-allulose in dairy product processing.

Wind energy is one of the most significant and rapidly developing renewable energy sources in the world and it provides a clean energy resource, which is a promising alternative in the short term in Turkey. The wind energy potential in various parts of Turkey is becoming economical due to reductions in wind turbine costs, and in fossil fuel atmospheric pollution. This paper is to present, in brief, wind potential in Turkey and to perform an investigation on the wind energy potential of the Kutahya region. A wind measurement station was established at Dumlupinar University Main Campus in order to □gure out the wind energy potential in the province. This study analyses the electricity generation capacity of the Kutahya region, Turkey, which uses the wind power system. In the study, the wind data collected from wind measurement stations between July 2001 and June 2004 (36 months) were evaluated to determine the energy potential of the region. Using this energy potential value, the power generation capacity of Kutahya was investigated for 17 different wind turbines. In this analysis, an ANN-based model and Weibull and Rayleigh distribution models were used to determine the power generation. In the ANN model, different feed-forward back propagation learning algorithms, namely Pola-Ribiere Conjugate Gradient, Levenberg-Marquardt and Scaled Conjugate Gradient were applied. The best appropriate model was determined as Levenberg-Marquardt with 15 neurons in a single hidden layer. Using the best ANN topology, it was determined that all the turbines were profitable except turbine type 1. The system with the turbine type 3 was decisively the most profitable case as determined at the end of the study according to Net Present Value concept.