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The escalating challenges of traffic congestion and environmental degradation underscore the critical importance of embracing E-Mobility solutions in urban spaces. In particular, micro E-Mobility tools such as E-scooters and E-bikes, play a pivotal role in this transition, offering sustainable alternatives for urban commuters. However, the energy consumption patterns for these tools are a critical aspect that impacts their effectiveness in real-world scenarios and is essential for trip planning and boosting user confidence in using these. To this effect, recent studies have utilised physical models customised for specific mobility tools and conditions, but these models struggle with generalization and effectiveness in real-world scenarios due to a notable absence of open datasets for thorough model evaluation and verification. To fill this gap, our work presents an open dataset, collected in Dublin, Ireland, specifically designed for energy modelling research related to E-Scooters and E-Bikes. Furthermore, we provide a comprehensive analysis of energy consumption modelling based on the dataset using a set of representative machine learning algorithms and compare their performance against the contemporary mathematical models as a baseline. Our results demonstrate a notable advantage for data-driven models in comparison to the corresponding mathematical models for estimating energy consumption. Specifically, data-driven models outperform physical models in accuracy by up to 83.83% for E-Bikes and 82.16% for E-Scooters based on an in-depth analysis of the dataset under certain assumptions., Comment: 7 pages, 5 figures, 4 tables. This manuscript has been accepted by the IEEE ITEC 2024

With the rising concern over transportation emissions and pollution on a global scale, shared electric mobility services like E-cars, E-bikes, and E-scooters have emerged as promising solutions to mitigate these pressing challenges. However, existing shared E-mobility services exhibit critical design deficiencies, including insufficient service integration, imprecise energy consumption forecasting, limited scalability and geographical coverage, and a notable absence of a user-centric perspective, particularly in the context of multi-modal transportation. More importantly, there is no consolidated open-source platform which could benefit the E-mobility research community. This paper aims to bridge this gap by providing an open-source platform for shared E-mobility. The proposed platform, with an agent-in-the-loop approach and modular architecture, is tailored to diverse user preferences and offers enhanced customization. We demonstrate the viability of this platform by providing a comprehensive analysis for integrated multi-modal route-optimization in diverse scenarios of energy availability, user preferences and E-mobility tools placement for which we use modified Ant Colony Optimization algorithm so called Multi-Model Energy Constrained ACO (MMEC-ACO) and Q-Learning algorithms. Our findings demonstrate that Q-learning achieves significantly better performance in terms of travel time cost for more than 90\% of the instances as compared to MMEC-ACO for different scenarios including energy availability, user preference and E-mobility tools distribution. For a fixed (O, D) pair, the average execution time to achieve optimal time cost solution for MMEC-ACO is less than 2 seconds, while Q-learning reaches an optimal time cost in 20 seconds on average. For a run-time of 2 seconds, Q-learning still achieves a better optimal time cost with a 20\% reduction over MMEC-ACO's time cost., Comment: 15 pages, 5 figures

Students' engagements reflect their level of involvement in an ongoing learning process which can be estimated through their interactions with a computer-based learning or assessment system. A pre-requirement for stimulating student engagement lies in the capability to have an approximate representation model for comprehending students' varied (dis)engagement behaviors. In this paper, we utilized model-based clustering for this purpose which generates K mixture Markov models to group students' traces containing their (dis)engagement behavioral patterns. To prevent the Expectation-Maximization (EM) algorithm from getting stuck in a local maxima, we also introduced a K-means-based initialization method named as K-EM. We performed an experimental work on two real datasets using the three variants of the EM algorithm: the original EM, emEM, K-EM; and, non-mixture baseline models for both datasets. The proposed K-EM has shown very promising results and achieved significant performance difference in comparison with the other approaches particularly using the Dataset. Hence, we suggest to perform further experiments using large dataset(s) to validate our method. Additionally, visualization of the resultant clusters through first-order Markov chains reveals very useful insights about (dis)engagement behaviors depicted by the students. We conclude the paper with a discussion on the usefulness of our approach, limitations and potential extensions of this work.

The visible universe is filled with different types of plasma media in the form of stars, nebulas and other forms of excited gases. These matter fields have a high influence on the gravity and are likely to be present around the black holes due to the immense gravitational attraction. Since a plasma medium affects the speed of light, therefore we investigated the null geodesics and various optical features around the rotating black hole in Kalb-Ramond gravity immersed in plasma medium. Various plasma distributions are considered to develop a comparative study for their influence on unstable null circular orbits, shadows and evaporation rate of the black hole in the presence of a plasma medium. Moreover, the shadow results are also compared with Event Horizon Telescope data for M78* and Sgr A* in order to estimate the parametric bounds for which the rotating black hole in Kalb-Ramond gravity is considered either M87* or Sgr A* under the different values of plasma parameters. From this analysis, we also found the distribution of plasma that has a significant impact on the above mentioned features and is most likely to be present around M87* and Sgr A*., Comment: 30 pages, 14 figures

Pre-trained Transformer-based speech models have shown striking performance when fine-tuned on various downstream tasks such as automatic speech recognition and spoken language identification (SLID). However, the problem of domain mismatch remains a challenge in this area, where the domain of the pre-training data might differ from that of the downstream labeled data used for fine-tuning. In multilingual tasks such as SLID, the pre-trained speech model may not support all the languages in the downstream task. To address this challenge, we propose self-supervised adaptive pre-training (SAPT) to adapt the pre-trained model to the target domain and languages of the downstream task. We apply SAPT to the XLSR-128 model and investigate the effectiveness of this approach for the SLID task. First, we demonstrate that SAPT improves XLSR performance on the FLEURS benchmark with substantial gains up to 40.1% for under-represented languages. Second, we apply SAPT on four different datasets in a few-shot learning setting, showing that our approach improves the sample efficiency of XLSR during fine-tuning. Our experiments provide strong empirical evidence that continual adaptation via self-supervision improves downstream performance for multilingual speech models., Comment: Submitted to ICASSP 2024

In this paper, we compare the scalar field dynamics in axion-like and power law potentials for both positive and negative values of the exponents. We find that, for positive exponents, both the potentials exhibit similar scalar field dynamics and it can be difficult to distinguish them at least at the background level. Even though the potentials are oscillatory in nature for positive exponents scaling solutions can be achieved for larger values of the exponent for which the dynamics can be different during early times. Because of the presence of this scaling nature there is a turnaround in the values of the scalar field equation of state as we increase the values of the exponent in both the potentials. This indicates the deviation from the oscillatory behaviour for the larger values of the exponent. For negative values of the exponent, the dynamics of the scalar field is distinguishable and axion-like potential can give rise to cosmologically viable tracker solutions unlike the power law potentials. For negative values of the exponent, axion-like potential can behave like a cosmological constant around its minima and the dark energy scale can be related to the potential scale. Due to the cosmological constant like behavior of the axion-like potential for negative exponent around its minima the late time dynamics can be similar to $\Lambda$CDM and we get similar observational constraint on the parameters for both $\Lambda$CDM and axion-like potential with negative exponent. So, while for positive exponents we may not distinguish the two potentials for negative exponents the dynamics of the scalar field is distinguishable., Comment: 16 pages, 3 tables and 13 figures. Observational data analysis added with some more clarifications. Conclusion unchanged. Accepted in PRD

This article deals with the study of some properties of the static and rotating black holes in Kalb-Ramond gravity in four dimensional spacetime. First, we discuss the action of the corresponding theory and the static black hole metric. Then we investigate the light sphere for the static black hole by using the Hamiltonian formalism and the corresponding linear radius of the shadow, angular velocity and Lyapunov exponent. For the rotating black hole, we discuss the horizon structure. Moreover, we study the effective potential to discuss the structure of null sphere and unstable circular null orbits around the rotating black hole. The properties such as energy emission rate and distortion are calculated and analyzed by using the numerical data for the shadows calculated by appropriately chosen parametric values for two different angular locations of the observer off the equatorial plane. We also obtain the constraints on the black hole parameters by comparing the shadow sizes of the black hole in Kalb-Ramond gravity and the supermassive black holes M87* and Sgr A*. Finally, we investigate the effect of mass, energy, angular momentum and the black hole parameters on the center of mass energy of two colliding particles that are accelerated in the vicinity of the black hole., Comment: 29 pages, 15 figures