Mycorrhizal Patterns in the Roots of Dominant Festuca rubra in a High-Natural-Value Grassland

Grassland ecosystems occupy significant areas worldwide and represent a reservoir for biodiversity. These areas are characterized by oligotrophic conditions that stimulate mycorrhizal symbiotic partnerships to meet nutritional requirements. In this study, we selected Festuca rubra for its dominance in the studied mountain grassland, based on the fact that grasses more easily accept a symbiotic partner. Quantification of the entire symbiosis process, both the degree of colonization and the presence of a fungal structure, was performed using the root mycorrhizal pattern method. Analysis of data normality indicated colonization frequency as the best parameter for assessing the entire mycorrhizal mechanism, with five equal levels, each of 20%. Most of the root samples showed an intensity of colonization between 0 and 20% and a maximum of arbuscules of about 5%. The colonization degree had an average value of 35%, which indicated a medium permissiveness of roots for mycorrhizal partners. Based on frequency regression models, the intensity of colonization presented high fluctuations at 50% frequency, while the arbuscule development potential was set to a maximum of 5% in mycorrhized areas. Arbuscules were limited due to the unbalanced and unequal root development and their colonizing hyphal networks. The general regression model indicated that only 20% of intra-radicular hyphae have the potential to form arbuscules. The colonization patterns of dominant species in mountain grasslands represent a necessary step for improved understanding of the symbiont strategies that sustain the stability and persistence of these species.

Correlation approach in predictor selection for groundwater level forecasting in areas threatened by water deficits

Reliable long-term groundwater level (GWL) prediction is essential to assess the availability of resources and the risk to drinking water supply in changing climatic and socio-economic conditions, especially in areas with water deficits. The modern approach in this area involves the use of machine learning methods. However, the greatest challenge in these methods lies in the optimization of input selection. The presented research concerns the selection of the best combination of predictors using the Hellwig method. It served as a preprocessing technique before GWL prediction using support vector regression (SVR) and multilayer perceptron (MLP) for three wells in the Greater Poland Province, where the largest water deficits occur, in the period 1975–2014. The results of this method were compared with those of the regression method, general regression model. For the case study under investigation, the Hellwig method found GWL at lags of −1 and −2 months, all precipitation from the current month, and delayed by −1 to −6 months, and past temperature at months −1, −3, −4 and −6 as the most informative input set. Such input led to a model accuracy of 0.003–0.022 for a mean squared error and r2 of >0.8. The results obtained with SVR were slightly better than those with MLP. Moreover, every well required an individual set of predictors, and additional meteorological inputs improved the models’ performance.

Investigation of Magneto-/Radio-Metric Behavior in Order to Identify an Estimator Model Using K-Means Clustering and Artificial Neural Network (ANN) (Iron Ore Deposit, Yazd, IRAN)

The study area is located near Toot village in the Yazd province of Iran, which is considered in terms of its iron mineralization potential. In this area, due to radioactivity, radiometric surveys were performed in a part of the area where magnetometric studies have also been performed. According to geological studies, the presence of magnetic anomalies can have a complex relationship with the intensity of radioactivity of radioactive elements. Using the K-means clustering method, the centers of the clusters were calculated with and without considering the coordinates of radiometric points. Finally, the behavior of the two variables of magnetic field strength and radioactivity of radioactive elements relative to each other was studied, and a mathematical relationship was presented to analyze the behavior of these two variables relative to each other. On the other hand, the increasing and then decreasing behavior of the intensity of the Earth’s magnetic field relative to the intensity of radioactivity of radioactive elements shows that it is possible to generalize the results of magnetometric surveys to radiometry without radiometric re-sampling in this region and neighboring areas. For this purpose, using the general regression neural network and backpropagation neural network (BPNN) methods, radiometric data were estimated with very good accuracy. The general regression neural network (GRNN) method, with more precision in estimation, was used as a model for estimating the radiation intensity of radioactive elements in other neighboring areas.

INTELLIGENT HEALTH STATUS DETECTION METHOD FOR LOCOMOTIVE FUEL CELL BASED ON DATA-DRIVEN TECHNIQUES

Main drawbacks of fuel cell systems, namely, high cost, poor reliability, and short lifespan, limit the large-scale commercial application of fuel cell systems. The health status detection of fuel cell systems for locomotives is of great significance to the safe and stable operation of locomotives. To identify the failure modes of the fuel cell system accurately and quickly, this study proposed an intelligent health status detection method for locomotive fuel cells based on data-driven techniques. In this study, the actual test data of a 150-kW fuel cell system for locomotives was analyzed. The t-distributed stochastic neighbor embedding (t-SNE) algorithm was combined with the general regression neural network (GRNN) to intelligently detect the health status of the fuel cell system for locomotives. Specifically, t-SNE was used to process the high-dimensionality and strong coupling raw data of health status, enabling the dimensional reduction of the raw data to reflect essential features. Then, GRNN was used to identify the feature data to achieve the fast and accurate detection of the health status of the fuel cell system. Results show that the proposed method can effectively detect four health conditions, namely, normal state, high inlet coolant temperature, low air pressure, and low spray pump pressure, with a diagnostic accuracy of 98.75%. This study is applicable to the analysis of the actual measurement data of high-power level fuel cell systems and provides a reference for the health status detection of fuel cell systems for locomotives. Keywords: fuel cell system for locomotive; data-driven; general regression neural network; t-distributed stochastic neighbor embedding; health status detection