This study evaluates the performance of YOLOv7 with modified mosaic augmentation, which resizes images without cutting, against newer YOLO versions (YOLOv8, YOLOv9, YOLOv10, and YOLOv11) when trained with an unbalanced dataset in the context of sewage inspection. The modified mosaic augmentation helps preserve small, underrepresented objects in unbalanced datasets which are often cut off with original mosaic augmentation. Results show that YOLOv7 with modified mosaic augmentation outperforms YOLOv7 and YOLOv8 in precision and recall metrics, while YOLOv9 slightly surpasses it. However, YOLOv10 and YOLOv11 offer the best overall performance, in terms of both precision and recall. These findings show the importance of model selection based on application-specific needs and highlight the potential of modified mosaic augmentation for handling class imbalances. Future research should validate these findings across different datasets.

This study aimed to evaluate the impact of Twin Block appliance therapy on skeletal, dentoalveolar, and facial soft tissue characteristics. The study included 18 participants with Class II skeletal malocclusion who were treated with the Twin Block appliance. Lateral cephalograms and 3D face scans were analyzed before and after therapy for each participant. Dependent t-test results showed a significant increase in the protrusion of the lower incisors (p < 0.001), proclination of the lower incisors (p = 0.021), SNB (p = 0.005), Ls:E (p = 0.040), mandibular length (p < 0.001), and soft tissue mandible length (p < 0.001) and a significant decrease in the ANB (p = 0.003), Wits (p = 0.001), ANPG (p = 0.001), overbite (p = 0.001), and the retrusion of upper incisors (p = 0.002). Twin Block therapy caused changes in skeletal and soft tissue characteristics. The increase in the SNB angle and mandibular length, accompanied by the decrease in the ANB and Wits values reduced the skeletal discrepancy. The reduction in the ANPG indicated an improvement in the skeletal profile. Additionally, the increase in the soft tissue mandible length and distance between the upper lip and E-line contributed to improved esthetic soft tissue profile characteristics.

Soccer player performance is influenced by multiple unpredictable factors. During a game, score changes and pre-game expectations affect the effort exerted by players. This study used GPS wearable sensors to track players’ energy expenditure in 5-min intervals, alongside recording the goal timings and the win and lose probabilities from betting sites. A mathematical model was developed that considers pre-game expectations (e.g., favorite, non-favorite), endurance, and goal difference (GD) dynamics on player effort. Particle Swarm and Nelder–Mead optimization methods were used to construct these models, both consistently converging to similar cost function values. The model outperformed baselines relying solely on mean and median power per GD. This improvement is underscored by the mean absolute error (MAE) of 396.87±61.42 and root mean squared error (RMSE) of 520.69±88.66 achieved by our model, as opposed to the B1 MAE of 429.04±84.87 and RMSE of 581.34±185.84, and B2 MAE of 421.57±95.96 and RMSE of 613.47±300.11 observed across all players in the dataset. This research offers an enhancement to the current approaches for assessing players’ responses to contextual factors, particularly GD. By utilizing wearable data and contextual factors, the proposed methods have the potential to improve decision-making and deepen the understanding of individual player characteristics.

A performance in soccer depends on various factors which are difficult to predict. During a game, a score change affects players’ willingness to invest more or less effort depending on the active score. A player’s energy expenditure in 5-minute segments was extracted from GPS wearable sensors, and an energy consumption model for each player was created. To obtain player characteristics, the model was fitted on all available data using Particle Swarm (PSO) and Nelder-Mead (NM) optimization methods. The methods had similar performance, with NM performing slightly better overall (0.02%). This work presents a novel way of assessing players’ energy expenditure by considering a score difference influence and the fatigue effect. The proposed methods could potentially enable soccer practitioners to gain objective insight into their players’ psychological characteristics – determining who is a better substitute when losing, drawing, or winning.

The inspection of patients' soft tissues and the effects of various dental procedures on their facial physiognomy are quite challenging. To minimise discomfort and simplify the process of manual measuring, we performed facial scanning and computer measurement of experimentally determined demarcation lines. Images were acquired using a low-cost 3D scanner. Two consecutive scans were obtained from 39 participants, to test the scanner repeatability. An additional ten persons were scanned before and after forward movement of the mandible (predicted treatment outcome). Sensor technology that combines red, green, and blue (RGB) data with depth information (RGBD) integration was used for merging frames into a 3D object. For proper comparison, the resulting images were registered together, which was performed with ICP (Iterative Closest Point)-based techniques. Measurements on 3D images were performed using the exact distance algorithm. One operator measured the same demarcation lines directly on participants; repeatability was tested (intra-class correlations). The results showed that the 3D face scans were reproducible with high accuracy (mean difference between repeated scans <1%); the actual measurements were repeatable to some extent (excellent only for the tragus-pogonion demarcation line); computational measurements were accurate, repeatable, and comparable to the actual measurements. Three dimensional (3D) facial scans can be used as a faster, more comfortable for patients, and more accurate technique to detect and quantify changes in facial soft tissue resulting from various dental procedures.

This paper compares several new implementations of the YOLO (You Only Look Once) object detection algorithms in harsh underwater environments. Using a dataset collected by a remotely operated vehicle (ROV), we evaluated the performance of YOLOv5, YOLOv6, YOLOv7, and YOLOv8 in detecting objects in challenging underwater conditions. We aimed to determine whether newer YOLO versions are superior to older ones and how much, in terms of object detection performance, for our underwater pipeline dataset. According to our findings, YOLOv5 achieved the highest mean Average Precision (mAP) score, followed by YOLOv7 and YOLOv6. When examining the precision-recall curves, YOLOv5 and YOLOv7 displayed the highest precision and recall values, respectively. Our comparison of the obtained results to those of our previous work using YOLOv4 demonstrates that each version of YOLO detectors provides significant improvement.

In this paper, we present automatic, deep-learning methods for pipeline detection in underwater environments. Seafloor pipelines are critical infrastructure for oil and gas transport. The inspection of those pipelines is required to verify their integrity and determine the need for maintenance. Underwater conditions present a harsh environment that is challenging for image recognition due to light refraction and absorption, poor visibility, scattering, and attenuation, often causing poor image quality. Modern machine-learning object detectors utilize Convolutional Neural Network (CNN), requiring a training dataset of sufficient quality. In the paper, six different deep-learning CNN detectors for underwater object detection were trained and tested: five are based on the You Only Look Once (YOLO) architectures (YOLOv4, YOLOv4-Tiny, CSP-YOLOv4, YOLOv4@Resnet, YOLOv4@DenseNet), and one on the Faster Region-based CNN (RCNN) architecture. The models’ performances were evaluated in terms of detection accuracy, mean average precision (mAP), and processing speed measured with the Frames Per Second (FPS) on a custom dataset containing underwater pipeline images. In the study, the YOLOv4 outperformed other models for underwater pipeline object detection resulting in an mAP of 94.21% with the ability to detect objects in real-time. Based on the literature review, this is one of the pioneering works in this field.