When reconstructing a shooting incident with a shotgun, the muzzle-to-target distance can be determined by relating the size of a dispersion pattern found on a crime scene to that of test shots. Ideally, the test shots are performed with the weapon and ammunition that were used in the incident. But sometimes examiners will have to resort to alternatives, such as using cartridges of the same brand and type but with another pellet size. For this reason, the relationship between pellet size and shotgun dispersion patterns was studied with both lead and steel shotgun pellets. Cartridges were loaded with identical cartridge cases, powder charges, and wads but with different pellet sizes, below size B. The cartridges were fired, and the dispersion patterns at 5 m in front of the muzzle were measured and compared. The results provide strong support for the proposition that shotgun dispersion patterns with both lead and steel shot increase with decreasing pellet size if all other relevant parameters are kept equal. The results also provide an indicative measure of the magnitude of the effect. Pattern sizes were approximately 1.7 times larger with #9 than with #0 lead shot and 1.4 times larger with #9 than with #1 steel shot. The differences between consecutive shot sizes were generally smaller. This means that cartridges of equal brand and type but with the next nearest shot number can be used for a muzzle-to-target distance determination, keeping the information of the current study in mind in the final interpretation of the results.
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The paper introduced an automatic score detection model using object detection techniques. The performance of sevenmodels belonging to two different architectural setups was compared. Models like YOLOv8n, YOLOv8s, YOLOv8m, RetinaNet-50, and RetinaNet-101 are single-shot detectors, while Faster RCNN-50 and Faster RCNN-101 belong to the two-shot detectors category. The dataset was manually captured from the shooting range and expanded by generating more versatile data using Python code. Before the dataset was trained to develop models, it was resized (640x640) and augmented using Roboflow API. The trained models were then assessed on the test dataset, and their performance was compared using matrices like mAP50, mAP50-90, precision, and recall. The results showed that YOLOv8 models can detect multiple objects with good confidence scores.
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Many studies have shown that experts possess better perceptual-cognitive skills than novices (e.g., in anticipation, decision making, pattern recall), but it remains unclear whether a relationship exists between performance on those tests of perceptual-cognitive skill and actual on-field performance. In this study, we assessed the in situ performance of skilled soccer players and related the outcomes to measures of anticipation, decision making, and pattern recall. In addition, we examined gaze behaviour when performing the perceptual-cognitive tests to better understand whether the underlying processes were related when those perceptual-cognitive tasks were performed. The results revealed that on-field performance could not be predicted on the basis of performance on the perceptual-cognitive tests. Moreover, there were no strong correlations between the level of performance on the different tests. The analysis of gaze behaviour revealed differences in search rate, fixation duration, fixation order, gaze entropy, and percentage viewing time when performing the test of pattern recall, suggesting that it is driven by different processes to those used for anticipation and decision making. Altogether, the results suggest that the perceptual-cognitive tests may not be as strong determinants of actual performance as may have previously been assumed.
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