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The polarisation of light induced by aligned interstellar dust serves as a significant tool in investigating cosmic magnetic fields and dust properties, while posing a challenge in characterising the polarisation of the cosmic microwave background and other sources. To establish dust polarisation as a reliable tool, the physics of the grain alignment process must be studied thoroughly. The magnetically enhanced radiative torque (MRAT) alignment is the only mechanism that can induce highly efficient alignment of grains with magnetic fields required by polarisation observations of the diffuse interstellar medium. Here, we aim to test the MRAT mechanism in starless cores using the multi-wavelength polarisation from optical to submillimetre. Our numerical modelling of dust polarisation using the MRAT theory demonstrates that the alignment efficiency of starlight polarisation (p_ext/A_V) and the degree of thermal dust polarisation (p_em) first decrease slowly with increasing visual extinction (A_V) and then fall steeply as ∝A_v^-1at largeA_Vdue to the loss of grain alignment, which explains the phenomenon known as polarisation holes. Visual extinction at the transition from shallow to steep slope (A_V^loss) increases with maximum grain size. By applying physical profiles suitable for a starless core, 109 in the Pipe nebula (Pipe-109), our model successfully reproduces the existing observations of starlight polarisation in the R band (0.65 μm) and the H band (1.65 μm), as well as emission polarisation in the submillimetre (870 μm). Successful modelling of observational data requires perfect alignment of large grains, which serves as evidence for the MRAT mechanism, and an increased maximum grain size with higher elongation at higherA_V. The latter reveals the first evidence for a new model of anisotropic grain growth induced by magnetic grain alignment. This paper introduces the framework for probing the fundamental physics of grain alignment and dust evolution using multi-wavelength dust polarisation (GRADE-POL), and it is the first of our GRADE-POL series. 

Public speaking is one of the most important ways to share ideas with many people in different domains such as education, training, marketing, or healthcare. Being able to master this skill allows the speaker to clearly advocate for their subject and greatly influence others. However, most of the population reported having public speaking anxiety or glossophobia, which prevents them from effectively conveying their messages to others. One of the best solutions is to have a safe and private space to practice speaking in front of others. As a result, this research work is proposed with the overarching goal of providing people with virtual environments to practice in front of simulated audiences. In addition, the proposed work will aim to have live audience feedback and speech analysis details which could be useful for the users. The experiments via a user study provide insights into the proposed public speaking simulator. 

Big cities are well-known for their traffic congestion and high density of vehicles such as cars, buses, trucks, and even a swarm of motorbikes that overwhelm city streets. Large-scale development projects have exacerbated urban conditions, making traffic congestion more severe. In this paper, we proposed a data-driven city traffic planning simulator. In particular, we make use of the city camera system for traffic analysis. It seeks to recognize the traffic vehicles and traffic flows, with reduced intervention from monitoring staff. Then, we develop a city traffic planning simulator upon the analyzed traffic data. The simulator is used to support metropolitan transportation planning. Our experimental findings address traffic planning challenges and the innovative technical solutions needed to solve them in big cities.