Electromechanical dynamic analysis for the cutting transmission system of the unmanned long-wall shearer under variable speed process

The drum shearer is one of the main equipments of the long-wall mining system. A typical condition to adjust the hauling and drum speeds is when the drum load exceeds the allowable value due to the hardness increase of the coal seam. Two schemes are utilized in this condition herein: (1) increasing the drum speed directly and maintaining the hauling speed; (2) decreasing the hauling speed firstly, then increasing the drum speed, finally increasing the hauling speed to the original value. The electromechanical dynamic model is firstly constructed for the Cutting Transmission System, and then the electromechanical dynamic analysis is conducted with both schemes, discovering that: the first scheme is quicker but may bring instability; the second is stable but slower; the resonance in frequencies obtained in different meshing conditions can be excited at the same time. At last, some advices are given for the development of the speed control strategies and mechanical design of the unmanned long-wall shearer

Design Optimization of a Disc Brake Based on a Multi-Objective Optimization Algorithm and Analytic Hierarchy Process Method

Multiple optimization objectives and the Pareto set often arise from engineering structural optimization. Normalization methods (such as the weighting method) have the disadvantage that the weighted value is not set by the decision maker but the designer and is greatly influenced by the opinion of the designer. On this basis, in this paper a non-dominated sorting genetic algorithm - analytic hierarchy process (NSGA-AHP) method is proposed for decision making and analysis of the Pareto solution set of the multiple-objective optimization in a structural optimal model. In addition, illustrated by the example of a disc brake, a multiple-objective optimization model for a disc brake has been here developed. Besides, the NSGA-AHP method is adopted for the analysis optimization. The research results show that the NSGA-AHP method can be utilized to select the Pareto solution set in an effective way and that this method is effective in solving a multiple-objective problem in the structural optimization design

The obesity paradox in intracerebral hemorrhage: a systematic review and meta-analysis

BackgroundIntracerebral hemorrhage (ICH) has a mortality rate which can reach 30–40%. Compared with other diseases, obesity is often associated with lower mortality; this is referred to as the ‘obesity paradox’. Herein, we aimed to summarize the studies of the relations between obesity and mortality after ICH.MethodFor this systematic review and meta-analysis (PROSPERO registry CRD42023426835), we conducted searches for relevant articles in both PubMed and Embase. Non-English language literature, irrelevant literature, and non-human trials were excluded. All included publications were then qualitatively described and summarized. Articles for which quantitative analyses were possible were evaluated using Cochrane’s Review Manager.ResultsTen studies were included. Qualitative analysis revealed that each of the 10 studies showed varying degrees of a protective effect of obesity, which was statistically significant in 8 of them. Six studies were included in the quantitative meta-analysis, which showed that obesity was significantly associated with lower short-term (0.69 [0.67, 0.73], p<0.00001) and long-term (0.62 [0.53, 0.73], p<0.00001) mortality. (Data identified as (OR [95%CI], p)).ConclusionObesity is likely associated with lower post-ICH mortality, reflecting the obesity paradox in this disease. These findings support the need for large-scale trials using standardized obesity classification methods.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023426835, identifier CRD42023426835

Deep learning time pattern attention mechanism-based short-term load forecasting method

Accurate load forecasting is crucial to improve the stability and cost-efficiency of smart grid operations. However, how to integrate multiple significant factors for enhancing load forecasting performance is insufficiently investigated in previous studies. To fill the gap, this study proposes a novel hybrid deep learning model for short-term load forecasting. First, the long short-term memory network is utilized to capture patterns from historical load data. Second, a time pattern attention (TPA) mechanism is incorporated to improve feature extraction and learning capabilities. By discerning valuable features and eliminating irrelevant ones, the TPA mechanism enhances the learning process. Third, fully-connected layers are employed to integrate external factors such as climatic conditions, economic indicators, and temporal aspects. This comprehensive approach facilitates a deeper understanding of the impact of these factors on load profiles, leading to the development of a highly accurate load forecasting model. Rigorous experimental evaluations demonstrate the superior performance of the proposed approach in comparison to existing state-of-the-art load forecasting methodologies

Thrombospondin-1 Gene Deficiency Worsens the Neurological Outcomes of Traumatic Brain Injury in Mice

Background: Thrombospondin-1 (TSP-1) is an extracellular matrix protein that plays multiple physiological and pathophysiological roles in the brain. Experimental reports suggest that TSP-1 may have an adverse role in neuronal function recovery under certain injury conditions. However, the roles of TSP-1 in traumatic brain injury (TBI) have not been elucidated. In this study we for the first time investigated the roles of TSP-1 in a controlled cortical impact (CCI) model of TBI in TSP-1 knockout (TSP-1 KO) and wild type (WT) mice. Methods: We examined blood brain-barrier (BBB) damage using at 1 day post-TBI by measuring Evans Blue leakage, and neurological functional recovery at 3 weeks post-TBI by measuring neurological severity score (NSS), wire gripping, corner test and Morris Water Maze (MWM). Mechanistically, we quantified pro-angiogenic biomarkers including cerebral vessel density, vascular endothelial growth factors (VEGF) and angiopoietin-1 (Ang-1) protein expression, synaptic biomarker synaptophysin, and synaptogenesis marker brain-derived neurotrophic factor (BDNF) protein expression in contralateral and ipsilateral (peri-lesion) cortex at 21 days after TBI using immunohistochemistry and Western Blot. Results: TSP-1 is upregulated at early phase of TBI in WT mice. Compared to WT mice, TSP-1 KO (1) significantly worsened TBI-induced BBB leakage at 1 day after TBI; (2) had similar lesion size as WT mice at 3 weeks after TBI; (3) exhibited a significantly worse neurological deficits in motor and cognitive functions; (4) had no significant difference in cerebral vessel density, but significant increase of VEGF and Ang-1 protein expressions in peri-lesion cortex; (5) significantly increased BDNF but not synaptophysin protein level in peri-lesion cortex compared to sham, but both synaptophysin and BDNF expressions were significantly decreased in contralateral cortex compared to WT. Conclusion: Our results suggest that TSP-1 may be beneficial for maintaining BBB integrity in the early phase and functional recovery in late phase after TBI. The molecular mechanisms of TSP-1 in early BBB pathophysiology, and long-term neurological function recovery after TBI need to be further investigated

Electromechanical dynamic analysis for the drum driving system of the long-wall shearer

The drum driving system is one of the weakest parts of the long-wall shearer, and some methods are also needed to monitor and control the long-wall shearer to adapt to the important trend of unmanned operation in future mining systems. Therefore, it is essential to conduct an electromechanical dynamic analysis for the drum driving system of the long-wall shearer. First, a torsional dynamic model of planetary gears is proposed which is convenient to be connected to the electric motor model for electromechanical dynamic analysis. Next, an electromechanical dynamic model for the drum driving system is constructed including the electric motor, the gear transmission system, and the drum. Then, the electromechanical dynamic characteristics are simulated when the shock loads are acted on the drum driving system. Finally, some advices are proposed for improving the reliability, monitoring the operating state, and choosing the control signals of the long-wall shearer based on the simulation

An Improved Lightweight YOLOv5 Algorithm for Detecting Strawberry Diseases

This paper proposes an improved lightweight YOLOv5 model for the real-time detection of strawberry diseases. The ghost convolution (GhostConv) module is incorporated into the YOLOv5 network, reducing the parameter numbers and floating-point operations (FLOPs) for extracting feature information using the backbone network. An involution operator is utilized in the backbone network to expand the receptive field, enhance the spatial information on strawberry disease characteristics, and reduce the number of FLOPs in the model. A convolutional block attention module (CBAM) is incorporated into the backbone network to enhance the network’s ability to extract strawberry disease features and suppress non-critical information. The upsampling module is replaced by a lightweight upsampling operator called Content-Aware ReAssembly of Features (CARAFE), which extracts feature map information and enhances the ability to focus on strawberry disease features. The experimental results on an open-source strawberry disease dataset show that the model achieves mean average precision (mAP)@0.5 of 94.7% with 3.9 M parameters and 3.6 G FLOPs. The improved model has higher detection precision than the original one and lower hardware requirements, providing a new strategy for strawberry disease identification and control

Relationship between the Coloration Mechanism and Gemological Properties of Purple Scapolite

Purple scapolite is a precious gemstone. In this paper, we compared the crystal structure and spectral characteristics of purple scapolite before and after heat treatment with conventional gemological tests, EPMA, XRF, LA-ICP-MS, infrared spectroscopy, Raman spectroscopy, UV–vis spectrophotometer, EPR, and other tests. The XRD results showed that the structure of purple scapolite fits perfectly with that of marialite. Compositional analyses indicate that purple scapolite has an average Me value of 16.85 and belongs to the subspecies marialite, and thus its specific gravity and refractive index are low. The absorption peak at 1045 cm−1 in the infrared spectra has a direct relationship with the Me value, which is blue-shifted with increasing Me value. After heating at 400 °C for 2 h, the purple scapolite changed to colorless, and no phase transformation or significant structural changes occurred during this process. But this process is accompanied by the disappearance of the signal at g = 2.011 in the EPR spectra, which indicates the presence of oxygen hole centers, thus proving that the color of purple scapolite is caused by oxygen hole centers rather than Fe3+. The chlorine in the marialite structure occupies the structural center, which provides for the appearance of oxygen hole centers, and thus purple scapolite always has a high marialite content. This further leads to the refractive index and specific gravity always being lower. That is a new explanation for the relationship between scapolite coloration mechanism, specific gravity, and refractive index