Abstract: Shared micro-mobility services are rapidly expanding yet little is known about travel behaviour. Understanding mode choice, in particular, is quintessential for incorporating micro-mobility into transport simulations in order to enable effective transport planning. We contribute by collecting a large dataset with matching GPS tracks, booking data and survey data for more than 500 travellers, and by estimating a first choice model between eight transport modes, including shared e-scooters, shared e-bikes, personal e-scooters and personal e-bikes. We find that trip distance, precipitation and access distance are fundamental to micro-mobility mode choice. Substitution patterns reveal that personal e-scooters and e-bikes emit less CO2 than the transport modes they replace, while shared e-scooters and e-bikes emit more CO2 than the transport modes they replace. Our results enable researchers and planners to test the effectiveness of policy interventions through transport simulations. Service providers can use our findings on access distances to optimize vehicle repositioning.

Abstract: A growing number of digital platforms operate in a dual mode: running marketplaces for third-party products, while selling their own products on those marketplaces. We build a model to explore the implications of this controversial practice. We analyze the tradeoffs that arise from a regulatory ban on the dual mode, showing how such a ban can harm consumer surplus and welfare even when the platform would otherwise engage in product imitation and self-preferencing. In the empirically most relevant scenarios, policies that prevent platform imitation and self-preferencing generate better outcomes than an outright ban on the dual mode.

Abstract: Recent work applying the notion of pseudospectrum to gravitational physics showed that the quasinormal mode spectrum of black holes is unstable, with the possible exception of the longest-lived (fundamental) mode. The fundamental mode dominates the expected signal in gravitational wave astronomy, and there is no reason why it should have privileged status. We compute the quasinormal mode spectrum of two model problems where the Schwarzschild potential is perturbed by a small "bump" consisting of either a Pöschl-Teller potential or a Gaussian, and we show that the fundamental mode is destabilized under generic perturbations. We present phase diagrams and study a simple double-barrier toy problem to clarify the conditions under which the spectral instability occurs.

Abstract: In this article, a decentralized adaptive sliding mode control scheme is proposed for stabilization of large-scale semi-Markovian jump interconnected systems, in which dead-zone linearity in the input and unknown interconnections among subsystems are tackled. First, by designing an integral sliding surface for each subsystem, local sliding mode dynamics are obtained in good property of dynamics. Second, sufficient conditions are established for checking the stochastic stability of the sliding mode dynamics with generally uncertain and unknown transition rates. Third, a variable structure controller is designed to guarantee finite-time reachability of sliding surface, and the unknown interconnections among subsystems are compensated by adaptive laws. Finally, a numerical example is provided to verify the effectiveness of the proposed control scheme.

Abstract: • LightGBDT application for travel mode choice prediction is proposed. • Predictive performance of LightGBDT is compared with four traditional machine learning models. • Prediction results showed LightGBDT model achieved better performance. • Feature sensitivity and SHAP summary analysis are conducted to explore the significant factors influencing the travelers’ mode preferences. • Study could provide analysts with key insights for effective transportation planning. Prediction of mode choice for travelers has been the subject of keen interest among transportation planners. Traditionally, mode choice analysis is conducted by statistical models or simple machine learning (ML) paradigms. Although statistical analysis approaches have a good theoretical basis and interpretability, they are built on several unrealistic assumptions regarding the distribution of data, which may lead to biased model predictions. On the other hand, the ML methods widely used in this regard have poor interpretability and fail to capture the behavioral aspects. To fill this gap, this study proposes a systematic machine learning (ML) framework for a better understanding of traveler’s mode choice decisions. Five different ML models (Logistic Regression, Random Forests, Decision Tree, Multilayer Perceptron, Light Gradient Boosting Decision Tree (LightGBDT)) were developed to model the travel mode choices of travelers using three years of Dutch National Travel Survey data. Empirical results of various performance evaluation metrics (overall accuracy, average precision, precision-recall curves) showed that LightGBDT outperformed other models for both under and over-sampling strategies. To overcome the blackbox criticism of ML models and to improve their interpretability, variable importance and SHAP dependency analysis were also conducted. The analysis showed that predictors that significantly influence the travel mode decisions of travelers include trip distance, travelers’ age and annual income, number of cars/bicycles owned, and trip density. The results can be used for better understanding and effective modeling of travelers’ mode choice preferences.

Abstract: In order to solve the multiple unmanned aerial vehicles (UAVs) collaborative path planning problem under complex environments with multiple constraints, the multi-objective particle swarm optimization algorithm with multi-mode collaboration based on reinforcement learning (MCMOPSO-RL) is proposed in this paper to find optimal paths and handle constraints simultaneously. Reinforcement learning (RL) is applied to enable the proposed algorithm to choose the suitable position updated mode to achieve the high performance. Multi-mode collaboration strategy is developed to update the particle positions, where three modes are designed to balance the population diversity and the convergence speed, including the exploration, exploitation modes, and the hybrid update mode. Experimental results show that MCMOPSO-RL can solve the path planning problem for multiple UAVs more efficiently and robustly than other algorithms.

Abstract: This article is concerned with the problem of output feedback control for a class of continuous-time hidden semi-Markov jump systems with time delays. Due to the limitations of the actual environment, system modes are usually undetectable, which are called hidden modes. The controller modes are described as observable modes. Emission probabilities are used to establish the relationship between abovementioned two concepts. The jump parameters are governed by the hidden semi-Markov process, which can better describe the asynchronous information between the controller modes and the system modes. Besides, time delays are considered to be time-varying and dependent on the hidden modes. By employing some mathematical transformation and constructing a novel Lyapunov–Krasovskii functional, some new parameter-dependent sufficient stabilization conditions can be obtained by designing an observed-mode-dependent static output-feedback controller. Finally, a practical example is provided to illustrate the effectiveness and merits of the proposed methods.

Abstract: In actual engineering scenarios, multichannel datasets that contain complete information contribute to better accuracy of bearing fault diagnosis. Multivariate variational mode decomposition (MVMD), as an extension of variational mode decomposition (VMD), can deal with multivariate signals. However, the performance of MVMD is affected by initial parameters, i.e., the number of decomposition modes, the bandwidth balance parameter, and the initial center frequencies (ICFs). To overcome the difficulty of initial parameter selection, a self-adaptive MVMD is proposed, where the number of decomposition modes and the ICFs are determined adaptively on the basis of the convergence tendency in MVMD. The bandwidth balance parameter of each extracted mode is also optimized adaptively in the process. In addition, the normalized frequency-to-energy ratio is used as the evaluation index to identify faulty mode. Final results of experiments pave the way for a new method in bearing fault diagnosis with prominent superiority.

Abstract: Chaotic semiconductor lasers have been widely investigated for generating unpredictable random numbers, especially for lasers with external optical feedback. Nevertheless, chaotic lasers under external feedback are hindered by external feedback loop time, which causes correlation peaks for chaotic output. Here, we demonstrate the first self-chaotic microlaser based on internal mode interaction for a dual-mode microcavity laser, and realize random number generation using the self-chaotic laser output. By adjusting mode frequency interval close to the intrinsic relaxation oscillation frequency, nonlinear dynamics including self-chaos and period-oscillations are predicted and realized numerically and experimentally due to internal mode interaction. The internal mode interaction and corresponding carrier spatial oscillations pave the way of mode engineering for nonlinear dynamics in a solitary laser. Our findings provide a novel and easy method to create controllable and robust optical chaos for high-speed random number generation.

Abstract: With the large-scale access of all kinds of distributed generations (DGs), the operation mode of the distribution network is increasingly diverse and changeable. To monitor the operation of an active distribution network, an adaptive dynamic estimation method is proposed to address the new generation of power system. Considering the features of different types of operation scenario change of distribution network and DGs, the proposed method uses the state deviation index to identify the current operation mode before state estimation. In the adaptive estimation stage, two typical estimators are improved to cope with the typical operation mode and embedded in the interactive multiple model (IMM) algorithm framework. IMM uses the identification results of operation mode to give higher weight to the corresponding estimator and finally outputs the joint estimation results. The proposed estimation method is investigated in an improved IEEE 33-bus system and an actual distribution network in China, which results indicate the proposed method converges more quickly and maintains better accuracy while facing the complex distribution network.

Abstract: Abstract Cellular ceramic structures (CCSs) are promising candidates for structural components in aerospace and modern industry because of their extraordinary physical and chemical properties. Herein, the CCSs with different structural parameters, i.e., relative density, layer, size of unit cells, and structural configuration, were designed and prepared by digital light processing (DLP)-based additive manufacturing (AM) technology to investigate their responses under compressive loading systematically. It was demonstrated that as the relative density increased and the size of the unit cells decreased, the mechanical properties of one-layer CCSs increased. The mechanical properties of three-layer CCSs were more outstanding than those of the CCSs with one and two layers. In addition, structural configurations also played a vital role in the mechanical properties of the CCSs. Overall, the mechanical properties of the CCSs from superior to inferior were that with the structural configurations of modified body-centered cubic (MBCC), Octet, SchwarzP, IWP, and body-centered cubic (BCC). Furthermore, structural parameters also had significant impacts on the failure mode of the CCSs under compressive loading. As the relative density increased, the failure mode of the one-layer CCSs changed from parallel—vertical—inclined mode to parallel—vertical mode. It was worth noting that the size of the unit cells did not alter the failure mode. Inclined fracture took a greater proportion in the failure mode of the multi-layer CCSs. But it could be suppressed by the increased relative density. Similarly, the proportions of the parallel—vertical mode and the fracture along a specific plane always changed with the variation of the structural configurations. This study will serve as the base for investigating the mechanical properties of the CCSs.