The precise detection of plant centres is important for growth monitoring, enabling the continuous tracking of plant development to discern the influence of diverse factors. It holds significance for automated systems like robotic harvesting, facilitating machines in locating and engaging with plants. In this paper, we explore the YOLOv4 (You Only Look Once) real-time neural network detector for plant centre detection. Our dataset, comprising over 12,000 images from 151 Arabidopsis thaliana accessions, is used to fine-tune the model. Evaluation of the dataset reveals the model's proficiency in centre detection across various accessions, boasting an mAP of 99.79% at a 50 % IoU threshold. The model demonstrates real-time processing capabilities, achieving a frame rate of approximately 50 FPS. This outcome underscores its rapid and efficient analysis of video or image data, showcasing practical utility in time-sensitive applications.

This paper presents a robust exploration of the capabilities of conditional Generative Adversarial Networks (GANs) in harnessing labeled data to produce high-quality labels for unlabeled samples. By leveraging conditional information, our approach guides the network to generate contextually relevant labels for specific time series data, accelerating the labeling process. A comprehensive evaluation of our model's performance, incorporating diverse metrics, visual representations, and his-tograms, illuminates the effectiveness of conditional GANs for the Assistive Label Generation (ALG) of time series Arabidopsis thaliana images. The Structural Similarity Index (SSIM) high-lights an average similarity of 98.89 % between the generated and manually labeled images. This innovative methodology holds the promise of significantly reducing labeling efforts.

Abstract Jennings, J, Štaka, Z, Wundersitz, DW, Sullivan, CJ, Cousins, SD, Čustović, E, and Kingsley, MI. Position-specific running and technical demands during male elite-junior and elite-senior Australian rules football match-play. J Strength Cond Res 37(7): 1449–1455, 2023—The aim of this study was to compare position-specific running and technical demands of elite-junior and elite-senior Australian rules football match-play to better inform practice and assist transition between the levels. Global positioning system and technical involvement data were collated from 12 Victorian U18 male NAB League (n = 553) and 18 Australian Football League (n = 702) teams competing in their respective 2019 seasons. Players were grouped by position as nomadic, fixed, or ruck, and data subsets were used for specific analyses. Relative total distance (p = 0.635, trivial effect), high-speed running (HSR) distance (p = 0.433, trivial effect), acceleration efforts (p = 0.830, trivial effect), deceleration efforts (p = 0.983, trivial effect), and efforts at >150 m·min−1 (p = 0.229, trivial effect) and >200 m·min−1 (p = 0.962, trivial effect) did not differ between elite-junior and elite-senior match-play. Elite juniors covered less total and HSR distance during peak periods (5 seconds–10 minutes) of demand (p ≤ 0.022, small-moderate effects). Within both leagues, nomadic players had the greatest running demands followed by fixed position and then rucks. Relative disposals (p = 0.330, trivial effect) and possessions (p = 0.084, trivial effect) were comparable between the leagues. During peak periods (10 seconds to 2 minutes), elite juniors had less technical involvements than elite seniors (p ≤ 0.001, small effects). Although relative running demands and positional differences were comparable between the leagues, elite juniors perform less running, HSR, and technical involvements during peak periods when compared with elite seniors. Therefore, coaching staff in elite-senior clubs should maintain intensity while progressively increasing the volume of training that recently drafted players undertake when they have transitioned from elite-junior leagues.

Solar Particle Events (SPEs) generate cosmic radiation of different magnitude in a time span of several hours or even days. This contributes to an increased probability of higher magnitude Single-Event Upsets (SEUs) occurrence in space applications. It is critical to establish early detection of SEU rate or Soft Error Rate (SRE) changes to enable timely radiation hardening measures. This research paper focuses on the high-accuracy detection of SPEs using the manually collected space data. Additionally, the prediction of SRE increase or decrease was established with the seven widely used supervised machine learning algorithms. Excellent performance of 97.82%, including a high F1-score, was achieved during the presence of SPE using $k$-Nearest Neighbor algorithms.