This research is focused on traffic congestion in the small island of Malta which is the most densely populated country in the EU with about 1,672 inhabitants per square kilometre (4,331 inhabitants/sq mi). Furthermore, Malta has a rapid vehicle growth. Based on our research, the number of vehicles increased by around 11,000 in a little more than 6 months, which shows how important it is to have an accurate and comprehensive means of collecting data to tackle the issue of fluctuating traffic in Malta. In this paper, we first present the newly built comprehensive traffic dataset, called MalTra. This dataset includes realistic trips made by members of the public across the island over a period of 200 days. We then describe the methodology we adopted to generate syntactic data to complete our data set as much as possible. In our research, we consider both MalTra and the Q-Traffic dataset, which has been used in several other research studies. The statistical ARIMA model and two graph neural networks, the spatial temporal graph convolutional network (STGCN) and the diffusion convolutional recurrent network (DCRNN) were used to analyse and compare the results with existing research. From the evaluation, we found that the DCRNN model outperforms the STGCN with the former resulting in MAE of 3.98 (6.65 in the case of the latter) and a RMSE of 7.78 (against 12.73 of the latter).

The success of Large Language Models (LLMs) in other domains has raised the question of whether LLMs can reliably assess and manipulate the readability of text. We approach this question empirically. First, using a published corpus of 4,724 English text excerpts, we find that readability estimates produced ``zero-shot'' from GPT-4 Turbo and GPT-4o mini exhibit relatively high correlation with human judgments (r = 0.76 and r = 0.74, respectively), out-performing estimates derived from traditional readability formulas and various psycholinguistic indices. Then, in a pre-registered human experiment (N = 59), we ask whether Turbo can reliably make text easier or harder to read. We find evidence to support this hypothesis, though considerable variance in human judgments remains unexplained. We conclude by discussing the limitations of this approach, including limited scope, as well as the validity of the ``readability'' construct and its dependence on context, audience, and goal.

Retinex (MSR), which enhances image quality. MSIDA typically performs the projection of the input region tensor into a novel multilinear The objective of kinship verification from face images is to subspace, which results in an increased distance between ascertain the biological relationship between two individuals samples belonging to different classes and a decreased distance by analyzing their faces appearances [1].