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.

Assessment of skeletal maturity is typical strategy applied in clinical pediatrics today. The main goal of a Bone Age Assessment (BAA) is to determine endocrinology and growth disorders by comparing the bone and chronological age of the patient. Several methods are developed to determine skeletal maturity, but Greulich-Pyle and Tanner-Whitehouse represent the two most common methods that involve left hand and wrist radiographs. However, these methods are extremely time-dependent and rely on an experienced radiologist, who further evaluates bone age using hand atlas as a reference. In this paper, VGG-16 and ResNet50 are two Deep Convolutional Neural Network (DCNN) models applied with ImageNet pre-trained weights in order to estimate correct bone age and achieve high accuracy of gender prediction using public RSNA dataset that includes 12611 radiographs. The experimental results show month discrepancy of approximately eight months and 82% accuracy during the process of gender classification.

Staunton, CA, Stanger, JJ, Wundersitz, DW, Gordon, BA, Custovic, E, and Kingsley, MI. Criterion validity of a MARG sensor to assess countermovement jump performance in elite basketballers. J Strength Cond Res XX(X): 000-000, 2018-This study assessed the criterion validity of a magnetic, angular rate, and gravity (MARG) sensor to measure countermovement jump (CMJ) performance metrics, including CMJ kinetics before take-off, in elite basketballers. Fifty-four basketballers performed 2 CMJs on a force platform with data simultaneously recorded by a MARG sensor located centrally on the player's back. Vertical accelerations recorded from the MARG sensor were expressed relative to the direction of gravity. Jumps were analyzed by a blinded assessor and the best jump according to the force platform was used for comparison. Pearson correlation coefficients (r) and mean bias with 95% ratio limits of agreement (95% RLOA) were calculated between the MARG sensor and the force platform for jumps performed with correct technique (n = 44). The mean bias for all CMJ metrics was less than 3%. Ninety-five percent RLOA between MARG- and force platform-derived flight time and jump height were 1 ± 7% and 1 ± 15%, respectively. For CMJ performance metrics before takeoff, impulse displayed less random error (95% RLOA: 1 ± 13%) when compared with mean concentric power and time to maximum force displayed (95% RLOA: 0 ± 29% and 1 ± 34%, respectively). Correlations between MARG and force platform were significant for all CMJ metrics and ranged from large for jump height (r = 0.65) to nearly perfect for mean concentric power (r = 0.95). Strong relationships, low mean bias, and low random error between MARG and force platform suggest that MARG sensors can provide a practical and inexpensive tool to measure impulse and flight time-derived CMJ performance metrics.