Secondary dust generation in rotary coal cutting.

In a rotary coal cutting system, the mined coal has to be transported through the cutting path. This transportation of mined coal through the cutting zone of a rotary cutting head is comparable to a rotary grinder. In the grinder , the mined coal particles are subjected to secondary comminution, known as regrinding. A significant amount of coal dust could be generated during regrinding. This study was undertaken to investigate the mechanisms of regrinding and dust generation, to quantify the amount of dust generated during regrinding and evaluate the effects of influencing parameters on dust generation. Three comminution mechanisms of particles in the regrinding process, namely slow compression, impact, and attrition, were recognized. Compression and dust generation were studied utilizing an electro-hydraulic servo control testing machine, with a controlled displacement rate of 0.00025 cm/s. A dynamic finite element model was employed to study impact failure. Dust generation due to impact failure was observed using a high speed video camera system. It was found that compression and impact generate dust because they create a crushed zone under the contact area. Dust generation by attrition was also investigated and it was found that attrition of coal particles generates a significant amount of dust. An Automated Rotary Coal Cutting Simulator was utilized to simulate the regrinding process. Laboratory simulation studies showed that the amount of feed particles subjected to compression and impact breakage was only 5% {dollar}\\sim{dollar} 11% and it decreased as feed particle size decreased. But the amount of dust generated during regrinding was significant and it increased as feed particle size decreased. This indicates that attrition of coal particles is the primary mechanism of the secondary dust generation. An orthogonal fractional factorial experiment was conducted to estimate the effects of the major parameters on dust generation during regrinding. Experimental results showed that the amount of dust generated increased with increasing depth of sump, cutting velocity, and grindability index of coal materials. Dust generation during regrinding can be reduced by increasing the particle size in the product of the primary cutting, reducing the depth of sump and cutting velocity

Improved YOLOv5 Based on Attention Mechanism and FasterNet for Foreign Object Detection on Railway and Airway tracks

In recent years, there have been frequent incidents of foreign objects intruding into railway and Airport runways. These objects can include pedestrians, vehicles, animals, and debris. This paper introduces an improved YOLOv5 architecture incorporating FasterNet and attention mechanisms to enhance the detection of foreign objects on railways and Airport runways. This study proposes a new dataset, AARFOD (Aero and Rail Foreign Object Detection), which combines two public datasets for detecting foreign objects in aviation and railway systems.The dataset aims to improve the recognition capabilities of foreign object targets. Experimental results on this large dataset have demonstrated significant performance improvements of the proposed model over the baseline YOLOv5 model, reducing computational requirements.Improved YOLO model shows a significant improvement in precision by 1.2%, recall rate by 1.0%, and [email protected] by 0.6%, while [email protected] remained unchanged. The parameters were reduced by approximately 25.12%, and GFLOPs were reduced by about 10.63%. In the ablation experiment, it is found that the FasterNet module can significantly reduce the number of parameters of the model, and the reference of the attention mechanism can slow down the performance loss caused by lightweight

Changes in Maternal Glucose Metabolism after the Administration of Dexamethasone for Fetal Lung Development

Aims. Antenatal dexamethasone administration for fetal lung development may impair maternal glucose tolerance. In this study, we investigated whether glucose and insulin levels differed among singleton and twin pregnancies and pregnancies with impaired glucose tolerance (IGT) after treatment with dexamethasone. Methods. Singleton pregnancies, twin pregnancies, and pregnancies with IGT between 28 and 33 weeks of gestation whose mothers were treated with dexamethasone were enrolled in this study. Exclusion criteria included gestational hypertension, diabetes, renal disorders, and infectious diseases. The fasting plasma glucose and insulin levels were checked before administration and 24 h, 48 h, and 72 h after treatment was completed. Results. Mean glucose levels were significantly higher in the twin pregnancy and IGT groups at 24 h and 48 h after the administration of dexamethasone than those in the singleton pregnancy group (P < 0.05). Although there was no significant difference in glucose levels before administration and 72 h after dexamethasone administration among the three groups, insulin levels in the IGT group were significantly higher (P < 0.05). Insulin levels in the singleton pregnancy group at 24 h and 48 h after treatment were significantly lower than in the twin and IGT groups. Conclusion. The effects on maternal fasting blood glucose and insulin levels of dexamethasone administrated to promote fetal lung maturation correlated with embryo number and the presence of IGT

Enhancing Black-Box Few-Shot Text Classification with Prompt-Based Data Augmentation

Training or finetuning large-scale language models (LLMs) such as GPT-3 requires substantial computation resources, motivating recent efforts to explore parameter-efficient adaptation to downstream tasks. One practical area of research is to treat these models as black boxes and interact with them through their inference APIs. In this paper, we investigate how to optimize few-shot text classification without accessing the gradients of the LLMs. To achieve this, we treat the black-box model as a feature extractor and train a classifier with the augmented text data. Data augmentation is performed using prompt-based finetuning on an auxiliary language model with a much smaller parameter size than the black-box model. Through extensive experiments on eight text classification datasets, we show that our approach, dubbed BT-Classifier, significantly outperforms state-of-the-art black-box few-shot learners and performs on par with methods that rely on full-model tuning

INSTRUCTSCORE: Explainable Text Generation Evaluation with Finegrained Feedback

Automatically evaluating the quality of language generation is critical. Although recent learned metrics show high correlation with human judgement, these metrics can not explain their verdict or associate the scores with defects in generated text. To address this limitation, we present InstructScore, an explainable evaluation metric for text generation. By harnessing both explicit human instruction and the implicit knowledge of GPT-4, we fine-tune a text evaluation metric based on LLaMA, producing both a score for generated text and a human readable diagnostic report. We evaluate InstructScore on a variety of generation tasks, including translation, captioning, data-to-text and commonsense generation. Experiments show that our 7B model surpasses all other unsupervised metrics, including those based on 175B GPT-3 and GPT-4. Surprisingly, our InstructScore, even without direct supervision from human-rated data, achieves performance levels on par with state-of-the-art metrics like COMET22, which were fine-tuned on human ratings.Comment: Accepted to EMNLP2023 Main Conferenc