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In this note, it is shown that Example 3.2 and Theorem 4.4 in the paper ‘Wiener index of a fuzzy graph and application to illegal immigration networks’, Fuzzy Sets Syst. 2019, article in press are not correct. Accordingly, the updated results are established.

To realize the distributed generation and to make the partnership between the dispatchable units and variable renewable resources work efficiently, accurate and flexible monitoring needs to be implemented. Due to digital transformation in the energy industry, a large amount of data is and will be captured every day, but the inability to process them in real time challenges the conventional monitoring and maintenance practices. Access to automated and reliable data-filtering tools seems to be crucial for the monitoring of many distributed generation units, avoiding false warnings and improving the reliability. This study aims to evaluate a machine-learning-based methodology for autodetecting outliers from real data, exploring an interdisciplinary solution to replace the conventional manual approach that was very time-consuming and error-prone. The raw data used in this study was collected from experiments on a 100-kW micro gas turbine test rig in Norway. The proposed method uses Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to detect and filter out the outliers. The filtered datasets are used to develop artificial neural networks (ANNs) as a baseline to predict the normal performance of the system for monitoring applications. Results show that the filtering method presented is reliable and fast, minimizing time and resources for data processing. It was also shown that the proposed method has the potential to enhance the performance of the predictive models and ANN-based monitoring.

Development of materials for selective recovery of uranium (U) from contaminated water and sea water is necessary to solve the issues related to sources of U(VI) for nuclear energy and alleviating the environmental pollution. Phosphorous-doped Graphitic (P-Gc) material, synthesized under microwave irradiation (450 W, 40 s) has been utilized as potential adsorbent material for U removal in ppm level from contaminated water selectively at room temperature (RT). The influence of various environmental conditions (e.g. pH, contact time, ionic strength, and temperature) on U(VI) adsorption was investigated by batch method. Maximum Adsorption capacity (QM) was found 1186 mg g−1 from Langmuir fit and this could be due to high specific surface area of P-Gc (1635 m2/g) and the presence of phosphate functionalities (3.18 atomic wt% P according to XPS) on the adsorbent surface. The results of kinetic and thermodynamic studies revealed that the adsorption was a spontaneous and exothermic chemical process and the adsorption process could be better fitted by a pseudo-second order model, suggesting a chemisorption over physisorption. The adsorption of U(VI) on P-Gc was a multilayer uptake in lower concentration range of U(VI) and monolayer in higher concentration range of U(VI). The material has been reused for five cycles with almost no change in the efficiency which is very important for the wastewater treatment. 90 % of the adsorbed uranium was regenerated from P-Gc by simple acid treatment. Hence, P-Gc has a great promise in the quest to development of metal free adsorbent for uranium removal at RT.