Nanoscience and nanotechnology currently represent one of the most rapidly developing fields of science and technology; therefore, the fundamental principles of nanoscience and nanotechnology should be understood by college and even high school students as well as by members of scientific communities. Silver, as the pioneer material in these fields, can be considered the appropriate guide on the voyage from the macro- to the nanoworld revealing the changes in fundamental properties of matter. We suggest a set of experiments that offer the preparation of silver mirror (macroscopic silver), submicroscopic “black” colloidal particles (<465 nm), and also nanoscopic “orange” particles (40 nm). Interestingly, all of these forms of silver can be prepared via the well-established Tollens reaction using just the variation in the initial concentrations of the reaction components. The macroscopic silver particles can be detected as a result of their typical metal shine. The colloidal and the nanoscopic silver particles, prepared in aqueous dispersions, can be simply detected with a laser pointer because of the Tyndall effect. However, more sophisticated methods like UV−vis absorption spectroscopy, dynamic light scattering, or transmission electron microscopy can be employed for the appropriate characterization of all of the prepared silver particles.
As energy distribution systems evolve from a traditional hierarchical load structure towards distributed smart grids, flexibility is increasingly investigated as both a key measure and core challenge of grid balancing. This paper contributes to the theoretical framework for quantifying network flexibility potential by introducing a machine learning based node characterization. In particular, artificial neural networks are considered for classification of historic demand data from several network substations. Performance of the resulting classifiers is evaluated with respect to clustering analysis and parameter space of the models considered, while the bootstrapping based statistical evaluation is reported in terms of mean confusion matrices. The resulting meta-models of individual nodes can be further utilized on a network level to mitigate the difficulties associated with identifying, implementing and actuating many small sources of energy flexibility, compared to the few large ones traditionally acknowledged.
This paper aims to analyse possibilities of train type identification in railway switches and crossings (S&C) based on accelerometer data by using contemporary machine learning methods such as neural networks. That is a unique approach since trains have been only identified in a straight track. Accelerometer sensors placed around the S&C structure were the source of input data for subsequent models. Data from four S&C at different locations were considered and various neural network architectures evaluated. The research indicated the feasibility to identify trains in S&C using neural networks from accelerometer data. Models trained at one location are generally transferable to another location despite differences in geometrical parameters, substructure, and direction of passing trains. Other challenges include small dataset and speed variation of the trains that must be considered for accurate identification. Results are obtained using statistical bootstrapping and are presented in a form of confusion matrices.
The presented paper concerns the development of condition monitoring system for railroad switches and crossings that utilizes vibration data. Successful utilization of such system requires a robust and efficient train type identification. Given the complex and unique dynamical response of any vehicle track interaction, the machine learning was chosen as a suitable tool. For design and validation of the system, real on-site acceleration data were used. The resulting theoretical and practical challenges are discussed.
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