The longitudinal fields of a tightly focused Laguerre–Gaussian (LG) laser can be used to accelerate electron pulse trains when it is reflected from a solid plasma. However, the normal transverse mode of laser beams in high-power laser systems is approximately Gaussian. A routine and reliable way to obtain high-intensity LG lasers in experiments remains a major challenge. One approach involves utilizing a solid plasma with a ‘light fan’ structure to reflect the Gaussian laser and obtain a relativistic intense LG laser. In this work, we propose a way to combine the mode transformation of a relativistic laser and the process of electron injection and acceleration. It demonstrates that by integrating a nanowire structure at the center of the ‘light fan’, electrons can be efficiently injected and accelerated during the twisted laser generation process. Using three-dimensional particle-in-cell simulations, it is shown that a circularly polarized Gaussian beam with ${a}_0=20$ can efficiently inject electrons into the laser beam in interaction with the solid plasma. The electrons injected close to the laser axis are driven by a longitudinal electric field to gain longitudinal momentum, forming bunches with a low energy spread and a small divergence angle. The most energetic bunch exhibits an energy of 310 MeV, with a spread of 6%. The bunch charge is 57 pC, the duration is 400 as and the divergence angle is less than 50 mrad. By employing Gaussian beams, our proposed approach has the potential to reduce experimental complexity in the demonstrations of twisted laser-driven electron acceleration.

Both energy performance certificates (EPCs) and thermal infrared (TIR) images play key roles in mapping the energy performance of the urban building stock. In this paper, we developed parametric building archetypes using an EPC database and conducted temperature clustering on TIR images acquired from drones and satellite datasets. We evaluated 1,725 EPCs of existing building stock in Cambridge, UK, to generate energy consumption profiles. Drone-based TIR images of individual buildings in two Cambridge University colleges were processed using a machine learning pipeline for thermal anomaly detection and investigated the influence of two specific factors that affect the reliability of TIR for energy management applications: ground sample distance (GSD) and angle of view (AOV). The EPC results suggest that the construction year of the buildings influences their energy consumption. For example, modern buildings were over 30% more energy-efficient than older ones. In parallel, older buildings were found to show almost double the energy savings potential through retrofitting compared to newly constructed buildings. TIR imaging results showed that thermal anomalies can only be properly identified in images with a GSD of 1 m/pixel or less. A GSD of 1-6 m/pixel can detect hot areas of building surfaces. We found that a GSD > 6 m/pixel cannot characterize individual buildings but does help identify urban heat island effects. Additional sensitivity analysis showed that building thermal anomaly detection is more sensitive to AOV than to GSD. Our study informs newer approaches to building energy diagnostics using thermography and supports decision-making for large-scale retrofitting.

A clinical and translational scientist (CTS) often seeks to increase their knowledge of statistical topics to effectively conduct biomedical research studies. A common method for obtaining this knowledge is through existing online educational materials that are suggested by a biostatistical collaborator or identified by the CTS. However, the volume of available educational materials on diverse statistical topics makes the task of identifying high-quality educational resources at an appropriate level challenging and time consuming for CTSs and collaborative biostatisticians. In response to these challenges, the Biostats4You website was created, where existing online educational materials for a variety of statistical topics are vetted to identify those most appropriate for CTSs. In this manuscript, we describe the resource review process, provide information about statistical topics and resources currently available, and make recommendations for how CTSs and collaborative biostatisticians can utilize the Biostats4You website to improve training, mentoring, and collaborative research practices.

The current study examined the comprehension and production of classifiers, case marking, and morphological passive structures among 414 child Japanese heritage speakers (mean age = 10.01 years; range = 4.02 – 18.18). Focusing on individual differences, we extracted latent experiential factors via the Q-BEx questionnaire (De Cat, Kašćelan, Prévost, Serratrice, Tuller, Unsworth, & The Q.-Be Consortium, 2022), which were then used to predict knowledge and use of these grammatical structures. The findings reveal that: (i) experiential factors such as heritage language (HL) engagement at home and within the community modulate grammatical performance differentially from childhood through adolescence, and (ii) HL proficiency, immersion experiences, and literacy systematically predict HL grammatical outcomes. These results indicate that particular language background factors hold differential significance at distinct developmental stages and that higher proficiency, richer immersion experiences, and literacy engagement in the HL are crucial for the development of core grammatical structures.

Recent advancements in Earth system science have been marked by the exponential increase in the availability of diverse, multivariate datasets characterised by moderate to high spatio-temporal resolutions. Earth System Data Cubes (ESDCs) have emerged as one suitable solution for transforming this flood of data into a simple yet robust data structure. ESDCs achieve this by organising data into an analysis-ready format aligned with a spatio-temporal grid, facilitating user-friendly analysis and diminishing the need for extensive technical data processing knowledge. Despite these significant benefits, the completion of the entire ESDC life cycle remains a challenging task. Obstacles are not only of a technical nature but also relate to domain-specific problems in Earth system research. There exist barriers to realising the full potential of data collections in light of novel cloud-based technologies, particularly in curating data tailored for specific application domains. These include transforming data to conform to a spatio-temporal grid with minimum distortions and managing complexities such as spatio-temporal autocorrelation issues. Addressing these challenges is pivotal for the effective application of Artificial Intelligence (AI) approaches. Furthermore, adhering to open science principles for data dissemination, reproducibility, visualisation, and reuse is crucial for fostering sustainable research. Overcoming these challenges offers a substantial opportunity to advance data-driven Earth system research, unlocking the full potential of an integrated, multidimensional view of Earth system processes. This is particularly true when such research is coupled with innovative research paradigms and technological progress.

Automatic license plate recognition (ALPR) systems are increasingly used to solve issues related to surveillance and security. However, these systems assume constrained recognition scenarios, thereby restricting their practical use. Therefore, we address in this article the challenge of recognizing vehicle license plates (LPs) from the video feeds of a mobile security robot by proposing an efficient two-stage ALPR system. Our ALPR system combines the on-the-shelf YOLOv7x model with a novel LP recognition model, called vision transformer-based LP recognizer (ViTLPR). ViTLPR is based on the self-attention mechanism to read character sequences on LPs. To ease the deployment of our ALPR system on mobile security robots and improve its inference speed, we also propose an optimization strategy. As an additional contribution, we provide an ALPR dataset, named PGTLP-v2, collected from surveillance robots patrolling several plants. The PGTLP-v2 dataset has multiple features to cover chiefly the in-the-wild scenario. To evaluate the effectiveness of our ALPR system, experiments are carried out on the PGTLP-v2 dataset and five benchmark ALPR datasets collected from different countries. Extensive experiments demonstrate that our proposed ALPR system outperforms state-of-the-art baselines.

Sea Surface Height Anomaly (SLA) is a signature of the mesoscale dynamics of the upper ocean. Sea surface temperature (SST) is driven by these dynamics and can be used to improve the spatial interpolation of SLA fields. In this study, we focused on the temporal evolution of SLA fields. We explored the capacity of deep learning (DL) methods to predict short-term SLA fields using SST fields. We used simulated daily SLA and SST data from the Mercator Global Analysis and Forecasting System, with a resolution of (1/12)° in the North Atlantic Ocean (26.5–44.42°N, −64.25–41.83°E), covering the period from 1993 to 2019. Using a slightly modified image-to-image convolutional DL architecture, we demonstrated that SST is a relevant variable for controlling the SLA prediction. With a learning process inspired by the teaching-forcing method, we managed to improve the SLA forecast at 5 days by using the SST fields as additional information. We obtained predictions of 12 cm (20 cm) error of SLA evolution for scales smaller than mesoscales and at time scales of 5 days (20 days) respectively. Moreover, the information provided by the SST allows us to limit the SLA error to 16 cm at 20 days when learning the trajectory.