Review of Energy Transition Policies in Singapore, London, and California

The paper contains the online supplementary materials for "Data-Driven Prediction and Evaluation on Future Impact of Energy Transition Policies in Smart Regions". We review the renewable energy development and policies in the three metropolitan cities/regions over recent decades. Depending on the geographic variations in the types and quantities of renewable energy resources and the levels of policymakers' commitment to carbon neutrality, we classify Singapore, London, and California as case studies at the primary, intermediate, and advanced stages of the renewable energy transition, respectively

Railway axle box bearing fault identification using LCD-MPE and ELM-AdaBoost

. In this study, a new method for bearing fault diagnosis using local characteristic-scale decomposition multi-scale permutation entropy (LCD-MPE) and extreme learning machine AdaBoost (ELM-AdaBoost) algorithms is proposed. Vibration signals of railway axle box rolling bearings under 4 conditions (normal, outer race fault, inner race fault, and rolling element fault) were used as our research objects. The signals were de-noised using wavelet de-noising (WD) as a pre-filter, then the LCD was used to decompose the signal into a number of intrinsic scale components (ISCs). Then, the multi-scale permutation entropy (MPE) was extracted as the feature parameters. Finally, the extracted features were used as ELM-AdaBoost to achieve the automated fault diagnosis. Our results prove that our method is effective for an accurate diagnosis of railway axle box bearing faults. Furthermore, our fault diagnosis method is highly applicable in practical engineering

Dual-emission single sensing element-assembled fluorescent sensor arrays for the rapid discrimination of multiple surfactants in environments

Surfactants are considered as typical emerging pollutants, their extensive use of in disinfectants has hugely threatened the ecosystem and human health, particularly during the pandemic of coronavirus disease-19 (COVID-19), whereas the rapid discrimination of multiple surfactants in environments is still a great challenge. Herein, we designed a fluorescent sensor array based on luminescent metal–organic frameworks (UiO-66-NH2@Au NCs) for the specific discrimination of six surfactants (AOS, SDS, SDSO, MES, SDBS, and Tween-20). Wherein, UiO-66-NH2@Au NCs were fabricated by integrating UiO-66-NH2 (2-aminoterephthalic acid-anchored-MOFs based on zirconium ions) with gold nanoclusters (Au NCs), which exhibited a dual-emission features, showing good luminescence. Interestingly, due to the interactions of surfactants and UiO-66-NH2@Au NCs, the surfactants can differentially regulate the fluorescence property of UiO-66-NH2@Au NCs, producing diverse fluorescent “fingerprints”, which were further identified by pattern recognition methods. The proposed fluorescence sensor array achieved 100% accuracy in identifying various surfactants and multicomponent mixtures, with the detection limit in the range of 0.0032 to 0.0315 mM for six pollutants, which was successfully employed in the discrimination of surfactants in real environmental waters. More importantly, our findings provided a new avenue in rapid detection of surfactants, rendering a promising technique for environmental monitoring against trace multicontaminants

Practical Link Duration Prediction Model in Vehicular Ad Hoc Networks

Link duration prediction is one of the most fundamental problems in vehicular ad hoc networks (VANETs) as it determines the network performance of many vehicular applications. Existing analytical analysis about link duration in both mobile ad hoc network (MANETs) and VANETs is too complicated to be applied in a practical setting. Assuming vehicle's velocity follows the normal distribution, we propose a practical model which considers the distribution of relative velocity, intervehicle distance, and impact of traffic lights to estimate the expected link duration between any pair of connected vehicles. Such model is implemented on each vehicle along with (1) a relative velocity estimation approach and (2) an exponential moving average- (EMA-) based data processing procedure. Furthermore, the proposed model assumes that the events of two consecutive vehicles encountering traffic lights combination are dependent, which make the model more practical. Simulation results show that the link duration model predicts link duration with the average accuracy of 10% and 20% in highway and city scenarios, respectively