This study investigates the effectiveness of intra-mammary ozone administration in the dry period and at the time of delivery for preventing against mastitis in herds with contagious mastitis. The cows were divided into five groups with 10 cows in each. Group 1 was administered an ozone-containing foam preparation via the teat canal into four udder quarters for 5 seconds at the beginning of the dry period; Group 2 was administered ozone at the beginning of the dry period and at the time of delivery; Group 3 was administered ozone at the time of delivery; Group 4 was administered a dry period udder preparation at the beginning of the dry period; and Group 5 was administered only teat seal at the beginning of the dry period. No statistically significant difference was found between the cows with regard to the SCC values at the beginning of the dry period and at the time of delivery (in cows without clinical mastitis, n=25). The SCC values were reported to decrease when the values at the beginning of the dry period and at the time of delivery were compared. All cows except two in Group 1 were detected to have clinical mastitis according to the frequency of microbial isolation in milk at the time of delivery. In conclusion, intra-mammary ozone administration did not prevent mastitis in the dry period or at the time of delivery in herds with contagious mastitis; moreover, it was determined to increase the rate of clinical mastitis in the postpartum period.

This study is aimed to investigate culturable airborne bacteria concentrations and the composition of methicillin-resistant staphylococci in eleven different locations on the basis of specific activities conducted within different parts of the European side of Istanbul. The highest bacterial levels were observed at the Bakirkoy station (1 100 CFU/m3) while the second highest levels were found at the Bahcelievler station (1 040 CFU/m3) in October; the lowest levels (10 CFU/m3) were measured at other different stations (Atakoy, Yesilkoy). Fifteen methicillin-resistant isolates [Staphylococcus hominis (n=11), S. cohnii spp. cohnii (n=2), S. sciuri (n=1), S. capitis spp. capitis (n=1)] were identified. The disc diffusion method was used to identify the antimicrobial resistance of these isolates, it was observed that the most common resistance was to penicillin (P) (n=11), doxycycline (DO) (n=4) and tetracycline (T) (n=5). None of the isolates was resistant to imipenem, amoxicillin/clavulanic acid, vancomycin (IPM, AMC, VA). However, multiple antimicrobial resistance was found to be 26.7%. The results of this study revealed the importance of isolated methicillin-resistant staphylococci in the stations with densely active human population and traffic, for public health. As a result, the importance of resting along known shorelines, where culturable airborne bacteria concentrations are much lower, and its importance for human health have been emphasized.

The demand for energy on a global scale increases day by day. Unlike renewable energy sources, fossil fuels have limited reserves and meet most of the world’s energy needs despite their adverse environmental effects. This study presents a new forecast strategy, including an optimization-based S-curve approach for coal consumption in Turkey. For this approach, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Whale Optimization Algorithm (WOA) are among the meta-heuristic optimization techniques used to determine the optimum parameters of the S-curve. In addition, these algorithms and Artificial Neural Network (ANN) have also been used to estimate coal consumption. In evaluating coal consumption with ANN, energy and economic parameters such as installed capacity, gross generation, net electric consumption, import, export, and population energy are used for input parameters. In ANN modeling, the Feed Forward Multilayer Perceptron Network structure was used, and Levenberg-Marquardt Back Propagation has used to perform network training. S-curves have been calculated using optimization, and their performance in predicting coal consumption has been evaluated statistically. The findings reveal that the optimization-based S-curve approach gives higher accuracy than ANN in solving the presented problem. The statistical results calculated by the GWO have higher accuracy than the PSO, WOA, and GA with R ² = 0.9881, RE = 0.011, RMSE = 1.079, MAE = 1.3584, and STD = 1.5187. The novelty of this study, the presented methodology does not need more input parameters for analysis. Therefore, it can be easily used with high accuracy to estimate coal consumption within other countries with an increasing trend in coal consumption, such as Turkey.