Human Growth Foundation

This release implements <strong>YOLOv5-P6</strong> models and retrained <strong>YOLOv5-P5</strong> models: <strong>YOLOv5-P5</strong> models (same architecture as v4.0 release): 3 output layers P3, P4, P5 at strides 8, 16, 32, trained at <code>--img 640</code> <strong>YOLOv5-P6</strong> models: <strong>4 output layers</strong> P3, P4, P5, P6 at strides 8, 16, 32, 64 trained at <code>--img 1280</code> Example usage: <pre><code class="lang-bash"># Command Line python detect.py --weights yolov5m.pt --img 640 # P5 model at 640 python detect.py --weights yolov5m6.pt --img 640 # P6 model at 640 python detect.py --weights yolov5m6.pt --img 1280 # P6 model at 1280 </code></pre> <pre><code class="lang-python"># PyTorch Hub model = torch.hub.load('ultralytics/yolov5', 'yolov5m6') # P6 model results = model(imgs, size=1280) # inference at 1280 </code></pre> All model sizes YOLOv5s/m/l/x are now available in P5 and P6 architectures: <pre><code class="lang-bash">python detect.py --weights yolov5s.pt # P5 models yolov5m.pt yolov5l.pt yolov5x.pt yolov5s6.pt # P6 models yolov5m6.pt yolov5l6.pt yolov5x6.pt </code></pre> Notable Updates <strong>YouTube Inference</strong>: Direct inference from YouTube videos, i.e. <code>python detect.py --source 'https://youtu.be/NUsoVlDFqZg'</code>. Live streaming videos and normal videos supported. (https://github.com/ultralytics/yolov5/pull/2752) <strong>AWS Integration</strong>: Amazon AWS integration and new AWS Quickstart Guide for simple EC2 instance YOLOv5 training and resuming of interrupted Spot instances. (https://github.com/ultralytics/yolov5/pull/2185) <strong>Supervise.ly Integration</strong>: New integration with the Supervisely Ecosystem for training and deploying YOLOv5 models with Supervise.ly (https://github.com/ultralytics/yolov5/issues/2518) <strong>Improved W&amp;B Integration:</strong> Allows saving datasets and models directly to Weights &amp; Biases. This allows for --resume directly from W&amp;B (useful for temporary environments like Colab), as well as enhanced visualization tools. See this blog by @AyushExel for details. (https://github.com/ultralytics/yolov5/pull/2125) Updated Results P6 models include an extra P6/64 output layer for detection of larger objects, and benefit the most from training at higher resolution. For this reason we trained all P5 models at 640, and all P6 models at 1280. &lt;p align="center"&gt;&lt;img width="800" src="https://user-images.githubusercontent.com/26833433/114313216-f0a5e100-9af5-11eb-8445-c682b60da2e3.png"&gt;&lt;/p&gt; &lt;details&gt; &lt;summary&gt;YOLOv5-P5 640 Figure (click to expand)&lt;/summary&gt; &lt;p align="center"&gt;&lt;img width="800" src="https://user-images.githubusercontent.com/26833433/114313219-f1d70e00-9af5-11eb-9973-52b1f98d321a.png"&gt;&lt;/p&gt; &lt;/details&gt; &lt;details&gt; &lt;summary&gt;Figure Notes (click to expand)&lt;/summary&gt; * GPU Speed measures end-to-end time per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size 32, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS. * EfficientDet data from [google/automl](https://github.com/google/automl) at batch size 8. &lt;/details&gt; <strong>April 11, 2021</strong>: v5.0 release: YOLOv5-P6 1280 models, AWS, Supervise.ly and YouTube integrations. <strong>January 5, 2021</strong>: v4.0 release: nn.SiLU() activations, Weights &amp; Biases logging, PyTorch Hub integration. <strong>August 13, 2020</strong>: v3.0 release: nn.Hardswish() activations, data autodownload, native AMP. <strong>July 23, 2020</strong>: v2.0 release: improved model definition, training and mAP. Pretrained Checkpoints Model size&lt;br&gt;&lt;sup&gt;(pixels) mAP&lt;sup&gt;val&lt;br&gt;0.5:0.95 mAP&lt;sup&gt;test&lt;br&gt;0.5:0.95 mAP&lt;sup&gt;val&lt;br&gt;0.5 Speed&lt;br&gt;&lt;sup&gt;V100 (ms) params&lt;br&gt;&lt;sup&gt;(M) FLOPS&lt;br&gt;&lt;sup&gt;640 (B) YOLOv5s 640 36.7 36.7 55.4 <strong>2.0</strong> 7.3 17.0 YOLOv5m 640 44.5 44.5 63.3 2.7 21.4 51.3 YOLOv5l 640 48.2 48.2 66.9 3.8 47.0 115.4 YOLOv5x 640 <strong>50.4</strong> <strong>50.4</strong> <strong>68.8</strong> 6.1 87.7 218.8 YOLOv5s6 1280 43.3 43.3 61.9 <strong>4.3</strong> 12.7 17.4 YOLOv5m6 1280 50.5 50.5 68.7 8.4 35.9 52.4 YOLOv5l6 1280 53.4 53.4 71.1 12.3 77.2 117.7 YOLOv5x6 1280 <strong>54.4</strong> <strong>54.4</strong> <strong>72.0</strong> 22.4 141.8 222.9 YOLOv5x6 TTA 1280 <strong>55.0</strong> <strong>55.0</strong> <strong>72.0</strong> 70.8 - - &lt;details&gt; &lt;summary&gt;Table Notes (click to expand)&lt;/summary&gt; * AP&lt;sup&gt;test&lt;/sup&gt; denotes COCO [test-dev2017](http://cocodataset.org/#upload) server results, all other AP results denote val2017 accuracy. * AP values are for single-model single-scale unless otherwise noted. **Reproduce mAP** by `python test.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65` * Speed&lt;sub&gt;GPU&lt;/sub&gt; averaged over 5000 COCO val2017 images using a GCP [n1-standard-16](https://cloud.google.com/compute/docs/machine-types#n1_standard_machine_types) V100 instance, and includes FP16 inference, postprocessing and NMS. **Reproduce speed** by `python test.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45` * All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation). * Test Time Augmentation ([TTA](https://github.com/ultralytics/yolov5/issues/303)) includes reflection and scale augmentation. **Reproduce TTA** by `python test.py --data coco.yaml --img 1536 --iou 0.7 --augment` &lt;/details&gt;Changelog Changes between <strong>previous release and this release</strong>: https://github.com/ultralytics/yolov5/compare/v4.0...v5.0 Changes <strong>since this release</strong>: https://github.com/ultralytics/yolov5/compare/v5.0...HEAD <strong>Click a section</strong> below to <strong>expand details</strong>: &lt;details&gt; &lt;summary&gt;Implemented Enhancements (26) &lt;/summary&gt; - Return predictions as json [\#2703](https://github.com/ultralytics/yolov5/issues/2703) - Single channel image training? [\#2609](https://github.com/ultralytics/yolov5/issues/2609) - Images in MPO Format are considered corrupted [\#2446](https://github.com/ultralytics/yolov5/issues/2446) - Improve Validation Visualization [\#2384](https://github.com/ultralytics/yolov5/issues/2384) - Add ASFF \(three fuse feature layers\) int the Head for V5\(s,m,l,x\) [\#2348](https://github.com/ultralytics/yolov5/issues/2348) - Dear author, can you provide a visualization scheme for YOLOV5 feature graphs during detect.py? Thank you! [\#2259](https://github.com/ultralytics/yolov5/issues/2259) - Dataloader [\#2201](https://github.com/ultralytics/yolov5/issues/2201) - Update Train Custom Data wiki page [\#2187](https://github.com/ultralytics/yolov5/issues/2187) - Multi-class NMS [\#2162](https://github.com/ultralytics/yolov5/issues/2162) - 💡Idea: Mosaic cropping using segmentation labels [\#2151](https://github.com/ultralytics/yolov5/issues/2151) - Improving Confusion Matrix Interpretability: FP and FN vectors should be switched to align with Predicted and True axis [\#2071](https://github.com/ultralytics/yolov5/issues/2071) - Interpreting model YoloV5 by Grad-cam [\#2065](https://github.com/ultralytics/yolov5/issues/2065) - Output optimal confidence threshold based on PR curve [\#2048](https://github.com/ultralytics/yolov5/issues/2048) - is it valuable that add --cache-images option to detect.py? [\#2004](https://github.com/ultralytics/yolov5/issues/2004) - I want to change the anchor box to anchor circles, where do you think the change to be made ? [\#1987](https://github.com/ultralytics/yolov5/issues/1987) - Support for imgaug [\#1954](https://github.com/ultralytics/yolov5/issues/1954) - Any plan for Knowledge Distillation? [\#1762](https://github.com/ultralytics/yolov5/issues/1762) - Is there a wasy to run detections on a video/webcam/rtrsp, etc EVERY x SECONDS? [\#1742](https://github.com/ultralytics/yolov5/issues/1742) - Can yolov5 support rotated target detection? [\#1728](https://github.com/ultralytics/yolov5/issues/1728) - Deploying yolov5 to TorchServe \(GPU compatible\) [\#1681](https://github.com/ultralytics/yolov5/issues/1681) - Why diffrent colors of bboxs? [\#1638](https://github.com/ultralytics/yolov5/issues/1638) - Yet another export yolov5 models to ONNX and inference with TensorRT [\#1597](https://github.com/ultralytics/yolov5/issues/1597) - Rerange the blocks of Focus Layer into `row major` to be compatible with tensorflow `SpaceToDepth` [\#413](https://github.com/ultralytics/yolov5/issues/413) - YouTube Livestream Detection [\#2752](https://github.com/ultralytics/yolov5/pull/2752) ([ben-milanko](https://github.com/ben-milanko)) - Add TransformerLayer, TransformerBlock, C3TR modules [\#2333](https://github.com/ultralytics/yolov5/pull/2333) ([dingyiwei](https://github.com/dingyiwei)) - Improved W&amp;B integration [\#2125](https://github.com/ultralytics/yolov5/pull/2125) ([AyushExel](https://github.com/AyushExel)) &lt;/details&gt;&lt;details&gt; &lt;summary&gt;Fixed Bugs (73)&lt;/summary&gt; - it seems that check\_wandb\_resume don't support multiple input files of images. [\#2716](https://github.com/ultralytics/yolov5/issues/2716) - ip camera or web camera. error: \(-215:Assertion failed\) !ss ize.empty\(\) in function 'cv::resize' [\#2709](https://github.com/ultralytics/yolov5/issues/2709) - Model predict with forward will fail if PIL image does not have filename attribute [\#2702](https://github.com/ultralytics/yolov5/issues/2702) - ❔Question Whenever i try to run my model i run into this error AttributeError: 'NoneType' object has no attribute 'startswith' from wandbutils.py line 161 I wonder why ? Any workaround or fix [\#2697](https://github.com/ultralytics/yolov5/issues/2697) - coremltools no longer included in docker container [\#2686](https://github.com/ultralytics/yolov5/issues/2686) - 'LoadImages' path handling appears to be broken [\#2618](https://github.com/ultralytics/

.

This release incorporates many new features and bug fixes (<strong>465 PRs</strong> from <strong>73 contributors</strong>) since our last release v5.0 in April, brings architecture tweaks, and also introduces new P5 and P6 'Nano' models: <strong>YOLOv5n</strong> and <strong>YOLOv5n6</strong>. Nano models maintain the YOLOv5s depth multiple of 0.33 but reduce the YOLOv5s width multiple from 0.50 to 0.25, resulting in ~75% fewer parameters, from 7.5M to 1.9M, ideal for mobile and CPU solutions. Example usage: <pre><code class="lang-bash">python detect.py --weights yolov5n.pt --img 640 # Nano P5 model trained at --img 640 (28.4 mAP@0.5:0.95) python detect.py --weights yolov5n6.pt --img 1280 # Nano P6 model trained at --img 1280 (34.0 mAP0.5:0.95) </code></pre> Important Updates <strong>Roboflow Integration ⭐ NEW</strong>: Train YOLOv5 models directly on any Roboflow dataset with our new integration! (https://github.com/ultralytics/yolov5/issues/4975 by @Jacobsolawetz) <strong>YOLOv5n 'Nano' models ⭐ NEW</strong>: New smaller YOLOv5n (1.9M params) model below YOLOv5s (7.5M params), exports to 2.1 MB INT8 size, ideal for ultralight mobile solutions. (https://github.com/ultralytics/yolov5/discussions/5027 by @glenn-jocher) <strong>TensorFlow and Keras Export</strong>: TensorFlow, Keras, TFLite, TF.js model export now fully integrated using <code>python export.py --include saved_model pb tflite tfjs</code> (https://github.com/ultralytics/yolov5/pull/1127 by @zldrobit) <strong>OpenCV DNN</strong>: YOLOv5 ONNX models are now compatible with both OpenCV DNN and ONNX Runtime (https://github.com/ultralytics/yolov5/pull/4833 by @SamFC10). <strong>Model Architecture:</strong> Updated backbones are slightly smaller, faster and more accurate. Replacement of <code>Focus()</code> with an equivalent <code>Conv(k=6, s=2, p=2)</code> layer (https://github.com/ultralytics/yolov5/issues/4825 by @thomasbi1) for improved exportability New <code>SPPF()</code> replacement for <code>SPP()</code> layer for reduced ops (https://github.com/ultralytics/yolov5/pull/4420 by @glenn-jocher) Reduction in P3 backbone layer <code>C3()</code> repeats from 9 to 6 for improved speeds Reorder places <code>SPPF()</code> at end of backbone Reintroduction of shortcut in the last <code>C3()</code> backbone layer Updated hyperparameters with increased mixup and copy-paste augmentation New Results &lt;p align="left"&gt;&lt;img width="800" src="https://user-images.githubusercontent.com/26833433/136901921-abcfcd9d-f978-4942-9b97-0e3f202907df.png"&gt;&lt;/p&gt; &lt;details&gt; &lt;summary&gt;YOLOv5-P5 640 Figure (click to expand)&lt;/summary&gt; &lt;p align="left"&gt;&lt;img width="800" src="https://user-images.githubusercontent.com/26833433/136763877-b174052b-c12f-48d2-8bc4-545e3853398e.png"&gt;&lt;/p&gt; &lt;/details&gt; &lt;details&gt; &lt;summary&gt;Figure Notes (click to expand)&lt;/summary&gt; * **COCO AP val** denotes mAP@0.5:0.95 metric measured on the 5000-image [COCO val2017](http://cocodataset.org) dataset over various inference sizes from 256 to 1536. * **GPU Speed** measures average inference time per image on [COCO val2017](http://cocodataset.org) dataset using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) V100 instance at batch-size 32. * **EfficientDet** data from [google/automl](https://github.com/google/automl) at batch size 8. * **Reproduce** by `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt` &lt;/details&gt;mAP improves from +0.3% to +1.1% across all models, and ~5% FLOPs reduction produces slight speed improvements and a reduced CUDA memory footprint. Example YOLOv5l before and after metrics: YOLOv5l&lt;br&gt;&lt;sup&gt;Large size&lt;br&gt;&lt;sup&gt;(pixels) mAP&lt;sup&gt;val&lt;br&gt;0.5:0.95 mAP&lt;sup&gt;val&lt;br&gt;0.5 Speed&lt;br&gt;&lt;sup&gt;CPU b1&lt;br&gt;(ms) Speed&lt;br&gt;&lt;sup&gt;V100 b1&lt;br&gt;(ms) Speed&lt;br&gt;&lt;sup&gt;V100 b32&lt;br&gt;(ms) params&lt;br&gt;&lt;sup&gt;(M) FLOPs&lt;br&gt;&lt;sup&gt;@640 (B) v5.0 (previous) 640 48.2 66.9 457.9 11.6 2.8 47.0 115.4 v6.0 (this release) 640 <strong>48.8</strong> <strong>67.2</strong> <strong>424.5</strong> <strong>10.9</strong> <strong>2.7</strong> <strong>46.5</strong> <strong>109.1</strong> Pretrained Checkpoints Model size&lt;br&gt;&lt;sup&gt;(pixels) mAP&lt;sup&gt;val&lt;br&gt;0.5:0.95 mAP&lt;sup&gt;val&lt;br&gt;0.5 Speed&lt;br&gt;&lt;sup&gt;CPU b1&lt;br&gt;(ms) Speed&lt;br&gt;&lt;sup&gt;V100 b1&lt;br&gt;(ms) Speed&lt;br&gt;&lt;sup&gt;V100 b32&lt;br&gt;(ms) params&lt;br&gt;&lt;sup&gt;(M) FLOPs&lt;br&gt;&lt;sup&gt;@640 (B) YOLOv5n 640 28.4 46.0 <strong>45</strong> <strong>6.3</strong> <strong>0.6</strong> <strong>1.9</strong> <strong>4.5</strong> YOLOv5s 640 37.2 56.0 98 6.4 0.9 7.2 16.5 YOLOv5m 640 45.2 63.9 224 8.2 1.7 21.2 49.0 YOLOv5l 640 48.8 67.2 430 10.1 2.7 46.5 109.1 YOLOv5x 640 50.7 68.9 766 12.1 4.8 86.7 205.7 YOLOv5n6 1280 34.0 50.7 153 8.1 2.1 3.2 4.6 YOLOv5s6 1280 44.5 63.0 385 8.2 3.6 16.8 12.6 YOLOv5m6 1280 51.0 69.0 887 11.1 6.8 35.7 50.0 YOLOv5l6 1280 53.6 71.6 1784 15.8 10.5 76.8 111.4 YOLOv5x6&lt;br&gt;+ TTA 1280&lt;br&gt;1536 54.7&lt;br&gt;<strong>55.4</strong> <strong>72.4</strong>&lt;br&gt;72.3 3136&lt;br&gt;- 26.2&lt;br&gt;- 19.4&lt;br&gt;- 140.7&lt;br&gt;- 209.8&lt;br&gt;- &lt;details&gt; &lt;summary&gt;Table Notes (click to expand)&lt;/summary&gt; * All checkpoints are trained to 300 epochs with default settings. Nano models use [hyp.scratch-low.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml) hyperparameters, all others use [hyp.scratch-high.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-high.yaml). * **mAP&lt;sup&gt;val&lt;/sup&gt;** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.&lt;br&gt;Reproduce by `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65` * **Speed** averaged over COCO val images using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) instance. NMS times (~1 ms/img) not included.&lt;br&gt;Reproduce by `python val.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45` * **TTA** [Test Time Augmentation](https://github.com/ultralytics/yolov5/issues/303) includes reflection and scale augmentations.&lt;br&gt;Reproduce by `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment` &lt;/details&gt;Changelog Changes between <strong>previous release and this release</strong>: https://github.com/ultralytics/yolov5/compare/v5.0...v6.0 Changes <strong>since this release</strong>: https://github.com/ultralytics/yolov5/compare/v6.0...HEAD &lt;details&gt; &lt;summary&gt;New Features and Bug Fixes (465)&lt;/summary&gt; * YOLOv5 v5.0 Release patch 1 by @glenn-jocher in https://github.com/ultralytics/yolov5/pull/2764 * Flask REST API Example by @robmarkcole in https://github.com/ultralytics/yolov5/pull/2732 * ONNX Simplifier by @glenn-jocher in https://github.com/ultralytics/yolov5/pull/2815 * YouTube Bug Fix by @glenn-jocher in https://github.com/ultralytics/yolov5/pull/2818 * PyTorch Hub cv2 .save() .show() bug fix by @glenn-jocher in https://github.com/ultralytics/yolov5/pull/2831 * Create FUNDING.yml by @glenn-jocher in https://github.com/ultralytics/yolov5/pull/2832 * Update FUNDING.yml by @glenn-jocher in https://github.com/ultralytics/yolov5/pull/2833 * Fix ONNX dynamic axes export support with onnx simplifier, make onnx simplifier optional by @timstokman in https://github.com/ultralytics/yolov5/pull/2856 * Update increment_path() to handle file paths by @glenn-jocher in https://github.com/ultralytics/yolov5/pull/2867 * Detection cropping+saving feature addition for detect.py and PyTorch Hub by @Ab-Abdurrahman in https://github.com/ultralytics/yolov5/pull/2827 * Implement yaml.safe_load() by @glenn-jocher in https://github.com/ultralytics/yolov5/pull/2876 * Cleanup load_image() by @JoshSong in https://github.com/ultralytics/yolov5/pull/2871 * bug fix: switched rows and cols for correct detections in confusion matrix by @MichHeilig in https://github.com/ultralytics/yolov5/pull/2883 * VisDrone2019-DET Dataset Auto-Download by @glenn-jocher in https://github.com/ultralytics/yolov5/pull/2882 * Uppercase model filenames enabled by @r-blmnr in https://github.com/ultralytics/yolov5/pull/2890 * ACON activation function by @glenn-jocher in https://github.com/ultralytics/yolov5/pull/2893 * Explicit opt function arguments by @fcakyon in https://github.com/ultralytics/yolov5/pull/2817 * Update yolo.py by @glenn-jocher in https://github.com/ultralytics/yolov5/pull/2899 * Update google_utils.py by @glenn-jocher in https://github.com/ultralytics/yolov5/pull/2900 * Add detect.py --hide-conf --hide-labels --line-thickness options by @Ashafix in https://github.com/ultralytics/yolov5/pull/2658 * Default optimize_for_mobile() on TorchScript models by @glenn-jocher in https://github.com/ultralytics/yolov5/pull/2908 * Update export.py onnx -&gt; ct print bug fix by @glenn-jocher in https://github.com/ultralytics/yolov5/pull/2909 * Update export.py for 2 dry runs by @glenn-jocher in https://github.com/ultralytics/yolov5/pull/2910 * Add file_size() function by @glenn-jocher in https://github.com/ultralytics/yolov5/pull/2911 * Update download() for tar.gz files by @glenn-jocher in https://github.com/ultralytics/yolov5/pull/2919 * Update visdrone.yaml bug fix by @glenn-jocher in https://github.com/ultralytics/yolov5/pull/2921 * changed default value of hide label argument to False by @albinxavi in https://github.com/ultralytics/yolov5/pull/2923 * Change default value of hide-conf argument to false by @albinxavi in https://github.com/ultralytics/yolov5/pull/2925 * test.py native --single-cls by @glenn-jocher in https://github.com/ultralytics/yolov5/pull/2928 * Add verbose option to pytorch hub models by @NanoCode012 in https://github.com/ultralytics/yolov5/pull/2926 * ACON Activation batch-size 1 bug patch by @glenn-joche

A modern and scientifically based indicators forecast of toxic and potentially toxic element concentrations allows us to develop and plan organizational and technicaltechnological measures aimed at reducing the negative impact of the coal industry and heating enterprises on the ecological state of the environment. For this purpose it is necessary to have data about concentration, character and features of the distribution of toxic and potentially toxic elements, including nickel, lead and chromium in coal and the rocks that contain it. Toxic elements are one of the main sources of environmental pollution thatnegatively affects human health. Research in this direction is conducted to reduce the degree of negative effects and additional pollution of the environment. Increasing requirements for environmental protection in the coal-mining industry sector of Ukraine stipulates the need for new scientifically grounded methods for forecasting the content of toxic and potentially toxic elements in the rock mass which is extracted by mines, the waste of coal extraction and coal enrichment and also the influence of the coal-heating enterprises on the environment. In the article, the results of investigations of toxic elements in coal seams of the Donetsk-Makiivka geological and industrial area of the Donbas are considered. The research covered the whole territory of one of the most studied geological and industrial districts of the Donbas – Donetsk-Makiivka. As a result of the study, correlation coefficients were calculated that allow us to predict the concentration of nickel, lead and chromium in the products and wastes of coal enrichment and correct the technological schemes of coal enrichment taking into account their content. We also calculated the regression equation between these elements and the ash content of the coal, which will allow us to predict their concentration in the main working coal seams of the DonetskMakiivka geological and industrial districts relative to the values of coal ash content. The character of the distribution is established and the weighted average concentrations and basic descriptive statistics for nickel, lead, and chromium in the coal seams and suites are calculated. The composition and character of their typomorphic geochemical associations, as well as the features and regularities of their accumulation in the coal seams of the Donetsk-Makiivka geological and industrial districts are revealed.

We present a high-statistics lattice QCD determination of the valence parton distribution function (PDF) of the pion, with a mass of 300 MeV, using two very fine lattice spacings of a 0.06 fm and 0.04 fm. We reconstruct the x-dependent PDF, as well as infer the first few even moments of the PDF using leading-twist 1-loop perturbative matching framework. Our analyses use both RI-MOM and ratio-based schemes to renormalize the equal-time bilocal quark-bilinear matrix elements of pions boosted up to 2.4 GeV momenta. We use various model-independent and model-dependent analyses to infer the large-x behavior of the valence PDF. We also present technical studies on lattice spacing and higher-twist corrections present in the boosted pion matrix elements.

To ascertain the value of screening for tuberculosis in the New York City (NYC) alcoholic and drug abusing welfare population, 2,641 clients were interviewed, and 970 (36.7%) of them met preestablished criteria for alcohol or drug abuse. The prevalence of active tuberculosis was 0.91%, which is 28 times the age-matched NYC rate. Screening only those persons with a positive PPD and a cough substantially increased the yield of active tuberculosis to 7.2%, or 225 times the NYC rate. The prevalence of a positive tuberculin skin test was 32.4%, or 1.5 times greater than the age-matched NYC rate. Treatment or prophylaxis for tuberculosis was required in 128 or 13.2% of the screened population. Seventy thousand NYC welfare clients are routinely evaluated for medical illness each year. This study predicts that in 1 yr this subpopulation could yield 239 clients with active tuberculosis and 3,181 requiring INH prophylaxis. Screening for tuberculosis in the alcoholic and drug abusing welfare clients is therefore urgently recommended.

Background Extreme wildfires pose a serious threat to forest vegetation and human life because they spread more rapidly and are more intense than conventional wildfires. Detecting extreme wildfires is challenging due to their visual similarities to traditional fires, and existing models primarily detect the presence or absence of fires without focusing on distinguishing extreme wildfires and providing warnings. Aims To test a system for real time detection of four extreme wildfires. Methods We proposed a novel lightweight model, called LEF-YOLO, based on the YOLOv5 framework. To make the model lightweight, we introduce the bottleneck structure of MobileNetv3 and use depthwise separable convolution instead of conventional convolution. To improve the model’s detection accuracy, we apply a multiscale feature fusion strategy and use a Coordinate Attention and Spatial Pyramid Pooling-Fast block to enhance feature extraction. Key results The LEF-YOLO model outperformed the comparison model on the extreme wildfire dataset we constructed, with our model having excellent performance of 2.7 GFLOPs, 61 FPS and 87.9% mAP. Conclusions The detection speed and accuracy of LEF-YOLO can be utilised for the real-time detection of four extreme wildfires in forest fire scenes. Implications The system can facilitate fire control decision-making and foster the intersection between fire science and computer science.

The Biennial Conference of Recent Advances in Animal Nutrition – Australia was held on 27–28 July 2023. The special issue contains latest research in the field of animal nutrition across the most economically significant animal species, including poultry, pigs, sheep, cattle, companion animals and aquaculture.

We present here AMUSING++: the largest compilation of nearby galaxies observed with the MUSE integral-field spectrograph so far. This collection consists of 635 galaxies from different MUSE projects covering the redshift interval 0.0002 z &lt; 0.1. The sample and its main properties are characterized and described here. It includes galaxies of almost all morphological types, with a good coverage in its color-magnitude diagram, within the stellar mass range between 10(8) and 10(12) M, and with properties resembling those of a diameter-selected sample. The AMUSING++ sample is, therefore, suitable for studying, with unprecendented detail, the properties of nearby galaxies at global and local scales, providing us with more than 50 million individual spectra. We use this compilation to investigate the presence of galactic outflows. We exploit the use of combined emission-line images to explore the shape of the different ionized components and the distribution along classical diagnostic diagrams to disentangle the different ionizing sources across the optical extension of each galaxy. We use the cross-correlation function to estimate the level of symmetry of the emission lines as an indication of the presence of shocks and/or active galactic nuclei. We uncovered a total of 54 outflows, comprising similar to 8% of the sample. A large number of the discovered outflows correspond to those driven by active galactic nuclei (similar to 60%), suggesting some bias in the selection of our sample. No clear evidence was found that outflow host galaxies are highly star-forming, and outflows appear to be found within all galaxies around the star-formation sequence.

Nutritional effects of marine phytoplankton on population growth of the rotifer Brachionus plicatilis were investigated under sterile conditions. First-laid eggs of the rotifers were divided into two groups, and one was cultured with a species of phytoplankton and the other was cultured with Chlorella sp. (marine type) as control. They were cultured separately in many test tubes, each containing two individuals and were observed daily to obtain the survival rate and the fecundity, while food phytoplankton suspension was renewed daily. The phytoplankton suspension was adjusted at the concentration which had been decided previously as the desirable cell concentration for population growth of the rotifers. From these daily counts, intrinsic rate of population growth and net reproduction rate were calculated as the indices for evaluating the nutritional effect of phytoplankton on population growth of the rotifers. Eight species of phytoplankton were evaluated in terms of nutritional effect on population growth of the rotifers as follows. Two species of Synechococcus elongatus and Chlorella sp. were evaluated as excellent food plankton, four species of Chlamydomonas sp, , Monochrysis lutheri, Dunaliella tertiolecta and Cyclotella cryptica as average and two species of Eutreptiella sp. and Nitzschia clostelium as deficient.

Today, there are problems of increasing economic, social, and financial stability and environmental protection (security) in Ukraine, and they are extremely relevant and important. They necessitate the solution of complex tasks aimed at ensuring the sustainable development of communities by means of effective state policy. The article summarizes and develops an approach to the realization of the national interests of Ukraine in the context of the continuous development of communities. It has received international recognition and is thus a system of views on the movement of economies and communities as their consistent interaction with each other and with nature. Today, it is recognized as a valuable model of the future world civilization, which marks the latest state of social development and is recognized as post-industrial. The theory and practice of sustainable development is a reflection of the objective need for economic reorientation and all social development with urgent consideration of the preservation of natural and human opportunities for current and future generations. For Ukraine, especially in view of its integration policy, the introduction of the concept of sustainable development acquires not only scientific but also political relevance and is one of the significant aspects of improving the efficiency of municipal self-government and achieving the best results. Thus, among the criteria of sustainable development of regions, it is expedient to include the criteria of municipal self-government activities, which would contribute to the dynamic and sustainable development of municipal entities, but the basic and optimal level of implementation of ideas and national interests of sustainable development is the municipal level. Therefore, local authorities should ensure the sustainable development of communities, taking into account the principles of local self-government and creating conditions for the transfer of a minimum economic, ecological and social debt to future generations.

One hundred and seventy-five strains of Aeromons salmonicida isolated in cultured salmonid fish and wild salmonid fish from rivers in Japan from 1979 to 1981 were tested for theri susceptibility to 12 chemotherapeutics. Five of 129 (3.9%) strains isolated in the cultured fish were sensitive to all the drugs tested. The remaining 124 strains were resistant to from one to six drugs; in particular, nalidixic acid derivatives and/or nitrofuran derivatives. Transferable R plasmids were detected in only 2 out of the 124 resistant strains. The resistant marker of the detected R plasmids determined as a combination of chloramphenicol (CM), streptomycin and sulfonamides (SA). The molecular weight of the R plasmids was 29M daltons. Non-transferable R plasmids resistant to TC were detected in two A. salmonicida strains habouring transferable R plasmid and had a molecular weight of 7.6M daltons. On the other hand, 32 strains (69.6%) isolated in wild salmonid fish were sensitive to all the drugs tested. The remaining strains were resistant to SA, and to a combination of CM, colistin and SA. These resistant strains had no transferable R plasmids.

Background: Early parental interventions in the Neonatal Intensive Care Units (NICUs) have beneficial effects on preterm infants’ short and long-term outcomes. The aim of this study was to investigate the effects of Early Vocal Contact (EVC)—singing and speaking—on preterm infants’ vagal activity and autonomic nervous system (ANS) maturation. Methods: In this multi-center randomized clinical trial, twenty-four stable preterm infants, born at 25–32 weeks gestational age, were randomized to either the EVC group or control group, where mothers did not interact with the babies but observed their behavior. Heart Rate Variability (HRV) was acquired before intervention (pre-condition), during vocal contact, and after it (post condition). Results: No significant effect of the vocal contact, singing and speaking, was found in HRV when the intervention group was compared to the control group. However, a significant difference between the singing and the pre and post conditions, respectively, preceding and following the singing intervention, was found in the Low and High Frequency power nu, and in the low/high frequency features (p = 0.037). By contrast, no significant effect of the speaking was found. Conclusions: Maternal singing, but not speaking, enhances preterm infants’ vagal activity in the short-term, thus improving the ANS stability. Future analyses will investigate the effect of enhanced vagal activity on short and long-term developmental outcomes of preterm infants in the NICU.

Abstract We present high-resolution speckle interferometric imaging observations of TESS exoplanet host stars using the NN-EXPLORE Exoplanet and Stellar Speckle Imager instrument at the 3.5 m WIYN telescope. Eight TESS objects of interest that were originally discovered by Kepler were previously observed using the Differential Speckle Survey Instrument. Speckle observations of 186 TESS stars were carried out, and 45 (24%) likely bound companions were detected. This is approximately the number of companions we would expect to observe given the established 46% binarity rate in exoplanet host stars. For the detected binaries, the distribution of stellar mass ratio is consistent with that of the standard Raghavan distribution and may show a decrease in high- q systems as the binary separation increases. The distribution of binary orbital periods, however, is not consistent with the standard Ragahavan model, and our observations support the premise that exoplanet-hosting stars with binary companions have, in general, wider orbital separations than field binaries. We find that exoplanet-hosting binary star systems show a distribution peaking near 100 au, higher than the 40–50 au peak that is observed for field binaries. This fact led to earlier suggestions that planet formation is suppressed in close binaries.

Improving the Enterprise Development Model: New Solutions Based on

. : . , , . ESG-, , . , , , . , 50 , . ESG- . : , , , , . , . , ESG- . , ESG- . , : , ,

Changelog

Abstract With the help of Gaia data, it is noted that in addition to the core components, there are low-density outer halo components in the extended region of open clusters. In order to study the extended structure beyond the core radius of the cluster (∼10 pc), based on Gaia Early Data Release 3, taking up to 50 pc as the searching radius, we use the pyUPMASK algorithm to redetermine the member stars of the open cluster within 1–2 kpc. We obtain the member stars of 256 open clusters, especially those located in the outer halo region of open clusters. Furthermore, we find that the radial density profile in the outer region for most open clusters deviates from the Kings profile. In order to better describe the internal and external structural characteristics of open clusters, we propose a two-component model: a core component with a King model distribution and an outer halo component with a logarithmic Gaussian distribution, and then suggest using four radii ( r c , r t , r o , r e ) for describing the structure and distribution profile of star clusters, where r t and r e represent the boundaries of the core component and outer halo component, respectively. Finally, we provide a catalog of 256 clusters with structural parameters. In addition, our study shows the sizes of these radii are statistically linearly related, which indicates that the inner and outer regions of the cluster are interrelated and follow similar evolutionary processes. Further, we show that the structure of two components can be used to better trace the cluster evolution properties at different stages.

International Journal of Wildland Fire publishes papers on the principles of fire as a process, on its ecological impact at the stand level and the landscape level, modelling fire and its effects, or presenting information on how to effectively and efficiently manage fire

У статті обґрунтовано поняття та необхідність упровадження наукової освіти в середній школі за умов цифрової трансформації освіти, представлено огляд інноваційних педагогічних технологій, які можуть ефективно застосовуватися для поширення наукового мислення на ширший перелік навчальних предметів і формування STEAM та інноваційної компетентностей як ключових компетентностей учнів. Авторами досліджено теоретичні основи поняття наукової освіти. Подано перелік знань, навичок та діяльностей, які входять до STEAM та інноваційної компетентностей. Доведено, що для їх формування доцільно цілеспрямовано використовувати в освітньому процесі метод навчальних проєктів, проблемне навчання та дослідницько-пізнавальний метод. У статті описано особливості цих інноваційних педагогічних технологій при впровадженні наукової освіти в середніх школах на основі використання сучасних цифрових технологій та інструментів. Авторами досліджено рівень обізнаності з такими технологіями та практику їх застосування серед українських учителів. До дослідження було долучено вчителів та майбутніх учителів п’яти українських університетів, що є партнерами Київського університету імені Бориса Грінченка за проєктом MoPED («Модернізація педагогічної вищої освіти з використання інноваційних інструментів викладання MOPED» – №586098-EPP-1-2017-1-UA-EPPKA2-CBHE-JP) задля виявлення потреб у вивченні сучасних освітніх трендів та відповідних для їх забезпечення інноваційних педагогічних технологій та цифрових інструментів. У статті представлені результати, які демонструють рівень зацікавленості вчителів та майбутніх учителів в оволодінні цифровими інструментами, описані групи інструментів для розвитку наукової освіти: створення в учнів позитивної мотивації, стимулювання їх до творчості, формування нестандартного творчого та креативного мислення, до пізнання навколишнього світу, до проведення експериментів, пошуку нових методів вирішення проблемних ситуацій. На основі дослідження визначено напрями підвищення кваліфікації вчителів для розвитку наукової освіти та формування інноваційної та STEAM-компетентностей в умовах цифрової трансформації суспільства.