While social scientists have long focused on socioeconomic and demographic factors, physical modelers typically study soil loss using physical factors. In the current environment, it is becoming increasingly important to consider both approaches simultaneously for the conservation of soil and water, and the improvement of land use conditions. This study uses physical and socioeconomic factors to find a coefficient that evaluates the combination of these factors. It aims to determine the effect of socioeconomic factors on soil loss and, in turn, to modify the universal soil loss equation (USLE). The methodology employed in this study specifies that soil loss can be calculated and predicted by comparing the degree of soil loss in watersheds, with and without human influence, given the same overall conditions. A coefficient for socioeconomic factors, therefore, has been determined based on adjoining watersheds (WS I and II), employing simulation methods. Combinations of C and P factors were used in the USLE to find the impact of their contributions to soil loss. The results revealed that these combinations provided good estimation of soil loss amounts for the second watershed, i.e., WS II, from the adjoining watersheds studied in this work. This study shows that a coefficient of 0.008 modified the USLE to reflect the socioeconomic factors, such as settlement, influencing the amount of soil loss in the studied watersheds.

Objective: This study aimed to determine serum and salivary levels of neutrophil gelatinase-associated lipocalin (NGAL) and evaluate NGAL correlation with key anti-interleukin 10 (IL-10) and pro-inflammatory (IL-1β) cytokines in different severities of periodontal diseases. We also calculated the systemic inflammation using the periodontal inflamed surface area (PISA) to evaluate its correlation with NGAL in the study groups. Methodology: Eighty systemically healthy and non-smoking individuals were separated into four groups of 20: clinically healthy (Group 1), gingivitis (Group 2), stage I generalized periodontitis (Group 3, Grade A), and stage III generalized periodontitis (Group 4, Grade A). Sociodemographic characteristics and periodontal parameters were recorded, and PISA was calculated. The serum and salivary levels of interleukin (IL)-1β, IL-10, and NGAL were determined using the enzyme-linked immunosorbent assay (ELISA). Results: We observed a significant increase in serum and salivary NGAL levels from healthy to periodontitis groups (p=0.000). Group 2 presented significantly higher serum and salivary IL-10 levels and salivary IL-1β levels than Group 3 (p=0.000). Serum and salivary parameters (IL-1β, IL-10, and NGAL levels) were strongly positively correlated to periodontal parameters and PISA values (p=0.000). Groups 2 and 3 showed overlapping PISA values. Conclusion: The overlapping PISA values found in Groups 2 and 3 suggest that gingivitis might progress to a systemic inflammatory burden somewhat comparable to stage I periodontitis. This finding is supported by the higher serum and salivary cytokines/mediators levels in the gingivitis group than in stage I periodontitis group. Serum and salivary NGAL levels increased proportionally to disease severity and PISA. NGAL seems to play a role in the pathogenesis of periodontal disease, within the limitation of our study.

In this study, anesthetic effects of Ocimum basilicum and Eucalyptus globulus essential oils were investigated on rainbow trout, Oncorhynchus mykiss. Fish (average 10 g) were exposed to 20, 50, 70, 100, 150, 200, 300, 400, 500, and 600 mg L⁻¹ essential oils, anesthesia induction, and recovery times were recorded separately for each fish. Acute toxicities of essential oils (10 min LC₅₀ concentration) on rainbow trout were determined at 70–400 mg L⁻¹. In addition, histopathological effects of the essential oils in fish tissues were determined after deep anesthesia. As a result of this study, O. basilicum at 300 mg L⁻¹ dose and E. globulus essential oils at 400 mg L⁻¹ dose showed ideal anesthetic effects on rainbow trout. Anesthesia induction (stage 4) and recovery times at this concentration for E. globulus were found as 186 and 117.5 s, respectively. Anesthesia induction (stage 4) and recovery times were 220.5 and 61 s for this concentration in O. basilicum essential oil, respectively. The toxic effects of essential oils for rainbow trout were not found. No pathological finding in gill, liver, and kidney was observed at the histopathological examination. Consequently, this study demonstrated that O. basilicum and E. globulus essential oils can be used as an effective and safe anesthetic in rainbow trout.

In this study, we compared the Student's t-test, Welch's t-test, and Mann-Whitney U test, in terms of their type I error rate and statistical power when the assumptions of parametric tests are violated in different situations. Materials used in this study, consisted of random numbers generated using the Numpy library in the Python programming language. All random numbers were generated from a normal distribution with N (0, 1) parameters. Balanced and unbalanced experimental conditions were simulated 50 000 times for each combination. The study revealed that, in comparison to other tests, Welch’s t - test was particularly more conservative in terms of type I error rate. It was discovered that the Student-t test had higher power values than the Mann-Whitney U test, mainly when only a small sample size of observations was used for the analysis. This simulation study indicated that Welch’s t - test is robust for preserving type I error rate when the distribution is normal. Therefore, in practice, the use of Welch t-test is recommended based on the findings of this study. One of the recommendations of this study is that the tests in question should also be evaluated in cases where observations have different distributions.

Due to the development of computing technology and different machine learning models, big data sets have gained importance in animal science as well as in many disciplines. The main objective of this study was to compare different machine learning algorithms to predict daily dry matter intake (DMI) in feedlot cattle. The data consisted of 2660 cattle pens placed on feed between January 1988 and December 1997. Machine learning methods were compared in heifers and steers, with 718 in pens of heifers and 1942 in pens of steers. Initial body weight, days on feed, and average proportion of dietary concentrate were used as independent variables to predict DMI in steers and heifers separately. The multivariate linear regression (LR), random forest (RF), gradient boosting regressor (GBR), and light gradient boosting machine (LGBR) algorithms were compared in terms of several performance metrics (MAE, MAPE, MSE, and RMSE). Results showed that the determination coefficient alone is not a good single criterion. It is recommended that the interpretation of model consistency should also consider MAE, MAPE, MSE, and RMSE values. In the current study, all machine learning algorithms yielded similar and lower performance metrics. However, the LGBR and GBR algorithms, were found to perform slightly better than the other algorithms, especially in heifers. Increasing the number of animals and using different independent variables that are related to the DMI can affect the accuracy of DMI prediction.

In this study, the rotation stage of factor analysis, which is one of the multivariate analysis methods, was examined. All stages of factor analysis have been defined. The material of the study consisted of a data set obtained from barley planted in 20 plots (replication) having 9 variables. In each plot, the average of 6 plants selected from that plot was used. The variables emphasized in the study were plant height, number of leaves, spike length, spike weight, grain yield, flowering period (days), harvest index, yield, and 1000-grain weight. Factors were obtained by principal component analysis, which is a factor extraction method, from the data set that met the prerequisites of the analysis. The criteria used in different factor rotations are given and based on these criteria, the formula that gives the optimum rotation angle for each data set was obtained. As a result, the formulas obtained for orthomax, varimax, quartimax, and equamax were applied to the factors obtained from the data set and the results were interpreted. As a result of factor rotation, when varimax, quartimax, and equamax methods were used, the values of the variables in terms of factor loads differed in each factor. This is a desirable situation for factor analysis results.