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Tetraphyllum Griff. ex C. B. Clarke, a newly recorded genus of Gesneriaceae from Guangxi, China is reported. Tetraphyllum has only three species. The morphological description and the distinctive features to the species of Tetraphyllum are provided, and the detailed morphological description and photos of T. confertiflorum (Drake) B. L. Burtt are provided. The voucher specimens are preserved in the Herbarium of Guangxi Institute of Botany (IBK) and Shanghai Chenshan Herbarium (CSH).

Currently, the intelligent construction of coal mines is in a transitional exploration period from primary to intermediate and advanced intelligence. The independent innovation capability of existing intelligent equipment still needs to be strengthened. The core technologies are bottleneck problems. The reliability problem under complex underground storage conditions is prominent. It is of great significance to use the new generation of information technology to promote the digital transformation and upgrading of coal mine equipment, and to promote safe, efficient, and green coal mining through digitization and intelligence. This paper reviews the evolution process of intelligent development of coal mining technology and equipment in China. This paper analyzes the production characteristics of each stage of coal mining. It is considered that the coal mine intelligence has completely changed the coal production mode. The production factors are mainly intelligent equipment, intelligent systems, and compound technical talents. They have essentially improved the level of safe and efficient production of coal mines, and helped the coal mines to reduce personnel, increase safety and improve efficiency. Among them, intelligent coal mine equipment is the core element of coal mine intelligence. Considering the demand for the digital and intelligent transformation and development of future coal mine equipment, we have explored the path of integrating the new generation of information technology and coal mine equipment. It uses equipment as an intelligent carrier. It endows the equipment with humanized intelligent capabilities such as intelligent perception and analysis, intelligent judgment and reasoning, intelligent decision-making and control, intelligent diagnosis and learning through the new generation of information technology. The system architecture of the Internet of things, 5G, big data, cloud computing, and artificial intelligence technology enabling intelligent perception, intelligent transmission, intelligent analysis, intelligent computing and intelligent decision-making of coal mine equipment has been constructed. The paper analyzes the theoretical, technical, and talent challenges in the development of digital intelligence in coal mining equipment. The paper proposes suggestions for the development of digital intelligence in coal mining equipment. Consolidating the foundation is the priority. It is necessary to build a top-level design theoretical system for intelligent coal mining equipment, strengthen key core technology research, accelerate the construction of new infrastructure, and consolidate the foundation of intelligent coal mining development. The intelligent technology for coal mining equipment and special application scenarios in coal mines develop in collaboration and coupling. It is suggested to meet the on-site needs of coal mines, form the intelligent technology for coal mine specific scenarios, and explore innovative models of equipment digital intelligence integration. The intelligent technology and green mining technology integrate and develop. It is suggested to create digital, intelligent, and green equipment. It is suggested to develop new models of intelligent, efficient, and low-carbon coal mining, and promote energy conservation and consumption reduction in coal production. It is suggested to establish a sound system for cultivating intelligent talents in coal mines, cultivate interdisciplinary and composite talents, and enhance talent reserves for the construction of intelligent coal mines.

The low light images can lead to many computer vision tasks not achieving the expected results. This can affect subsequent image analysis and intelligent decision-making. The existing low light image enhancement methods for underground coal mines do not consider the real noise of the image. In order to solve this problem, a method for enhancing low light images in coal mines based on Retinex model containing noise is proposed. The Retienx model containing noise is established. The noise estimation module (NEM) is used to estimate real noise. The original image and estimated noise are used as inputs to the illumination component estimation module (IEM) and reflection estimation module (REM) to generate and couple the illumination and reflection components. At the same time, gamma correction and other adjustments are made to the illumination components. And division operations are performed on the coupled image and adjusted illumination components to obtain the final enhanced image. NEM uses a three-layer CNN to perform Bayer sampling on noisy images. It reconstructs them to generate a three channel feature map which is the same size as the original image. Both IEM and REM use ResNet-34 as the image feature extraction network. The multi-scale asymmetric convolution and attention module (MACAM) is introduced to enhance the network's capability to filter details and important features. The qualitative and quantitative evaluation results indicate that this method can balance the relationship between light sources and dark environments, reduce real-world noise's impact, and perform well in image naturalness, realism, contrast, structure, and other aspects. The image enhancement effect is superior to models such as Retinex-Net, Zero-DCE, DRBN, DSLR, TBEFN, RUAS, etc. The effectiveness of NEM and MACAM is verified through ablation experiments.

Technoeconomic and life-cycle analyses are presented for catalytic conversion of ethanol to fungible hydrocarbon fuel blendstocks, informed by advances in catalyst and process development. Whereas prior work toward this end focused on 3-step processes featuring dehydration, oligomerization, and hydrogenation, the consolidated alcohol dehydration and oligomerization (CADO) approach described here results in 1-step conversion of wet ethanol vapor (40 wt% in water) to hydrocarbons and water over a metal-modified zeolite catalyst. A development project increased liquid hydrocarbon yields from 36% of theoretical to >80%, reduced catalyst cost by an order of magnitude, scaled up the process by 300-fold, and reduced projected costs of ethanol conversion 12-fold. Current CADO products conform most closely to gasoline blendstocks, but can be blended with jet fuel at low levels today, and could potentially be blended at higher levels in the future. Operating plus annualized capital costs for conversion of wet ethanol to fungible blendstocks are estimated at $2.00/GJ for CADO today and $1.44/GJ in the future, similar to the unit energy cost of producing anhydrous ethanol from wet ethanol ($1.46/GJ). Including the cost of ethanol from either corn or future cellulosic biomass but not production incentives, projected minimum selling prices for fungible blendstocks produced via CADO are competitive with conventional jet fuel when oil is $100 per barrel but not at $60 per barrel. However, with existing production incentives, the projected minimum blendstock selling price is competitive with oil at $60 per barrel. Life-cycle greenhouse gas emission reductions for CADO-derived hydrocarbon blendstocks closely follow those for the ethanol feedstock.

When the steel belt auxiliary bolt is used in the coal mine underground heading roadway, if the positioning of the steel belt anchor hole is not accurate, the drill bit is easy to cause equipment damage when hitting the steel belt or anchor net. There are large potential safety hazards. In order to solve the above problems, an intelligent identification and positioning method of steel belt anchor hole in coal mine roadway support based on improved YOLOv5s model is proposed. ① The definition of the anchor hole image is increased by the super-resolution(SR). The high-frequency information of the anchor hole edge in the image is prevented from being lost due to image blurring. ② Because the anchor hole is small and the camera has a certain distance from the anchor hole, it is easy to lose the characteristic information of the small anchor hole in the convolutional neural network. This affects the detection effect of the anchor hole. The coordinate attention mechanism (CA) module is added to the Backbone network of YOLOv5s model. The network layers of the characteristic extraction network in the YOLOv5s network are increased. The coordinate information of the target object is integrated into the convolutional network. The characteristic information of the anchor hole small target can be effectively extracted, and the success rate of anchor hole detection is improved. ③ The YOLOv5s network embedded in the CA module is trained to the anchor hole dataset reconstructed by SR, and the improved YOLOv5s model, namely SR-CA-YOLOv5s model, is obtained. ④ The SR-CA-YOLOv5s model combined with the binocular camera is used to identify and locate the anchor hole in real-time. The experimental results show that compared with the YOLOv5s model, the mean average precision of the SR-CA-YOLOv5s model is 96.8%, which is 3.1% higher than the YOLOv5s model. The SR-CA-YOLOv5s model has better detection capability and avoids missing detection to a certain extent. Although the frames per second (FPS) of the SR-CA-YOLOv5s model is reduced by 18.5 frames/s, its FPS remains at 166.7 frames/s, which does not affect the real-time detection function of the model. The actual test results show that the SR-CA-YOLOv5s model can accurately detect the anchor hole and obtain the three-dimensional coordinate of the anchor hole relative to the camera under different lighting conditions. The coordinate error is within 6 mm, and the FPS meets the real-time requirements.

Autonomous and accurate positioning of roadheader is the key to realize intelligent and unmanned heading face. The combination of strapdown inertial navigation and odometer is an ideal positioning scheme for roadheader. The positioning error of strapdown inertial navigation accumulates with time and the position and posture perception capability of single odometer is limited. In order to solve the above problems, a roadheader combined positioning method based on strapdown inertial navigation and differential odometer is proposed by introducing differential odometer as auxiliary positioning on the basis of strapdown inertial navigation. The realization of the method consists of three parts: position and posture perception based on strapdown inertial navigation, dead reckoning based on differential odometer, and data fusion based on Kalman filtering. The reference displacement and attitude angle of roadheader in navigation coordinate system are obtained by strapdown inertial navigation. The differential odometer is composed of two odometers installed on the left and right tracks of the roadheader, and the dead reckoning of the roadheader is calculated by the differential odometer. Kalman filter is designed according to the error equation of strapdown inertial navigation and differential odometer. The difference between the position and posture data of roadheader obtained by strapdown inertial navigation and differential odometer is used as the input of Kalman filter. The output value of Kalman filter is used to correct and compensate the strapdown inertial navigation data. The validity judgment of odometer data is integrated into the combined positioning method, so that the influence of track slipping on the positioning precision is avoided. The positioning experiment of roadheader is carried out in the simulated roadway. The results show that the heading angle error measured by the combined positioning method can be controlled within 0.6° and the position error can be controlled within 0.19 m. The method has high positioning precision.