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The change of regional hydrodynamic field caused by coal mining process affects the multi-field process of the formation and evolution of mine water quality, which may lead to the pollution of the water environment in the coal mining area. Research on the driving mechanism of the hydrodynamic field and the method of quantitative calculation of key parameters is the premise of quantitative description on the multi-field effects of site pollution in the coal mining areas and its corresponding pollution control. Taking the Menkeqing Coal Mine as the geological background, this paper clarifies the evolution mechanism of the hydrodynamic field of goaf water. According to the difference in the flow path of the hydrodynamic field, it is divided into the stages of water level recovery and filling-up. It reveals the complex process of the formation and evolution of water quality in the goaf, with the hydrodynamic field as the main driving force, and the synergistic effects of multiple fields such as the hydrochemical field and the microbial field. Then the concept model of the water level recovery stage and filling-up stage and the theoretical calculation model of the analytical solution of the key parameters (permeability coefficient, water level, and water volume) of the hydrodynamic field are proposed. The measured data of the similar material simulation test and the three-dimensional box simulation test are compared with the model calculation results. The theoretical calculation of the permeability coefficient and the curve of goaf water level with time and the experimental measurement error are about 13.2% and 22.5%, respectively. The reliability of the theoretical calculation model is verified. Finally, on the basis of the equations of the hydrodynamic field, and the hydrochemical field and the microbial field under the influence of the hydrodynamic field in the goaf, a multi-field coupling effect model dominated by the hydrodynamic field is constructed. The constitutive model provides an inspiration for exploring the formation and evolution of coal mine water quality driven by the coupling of hydrodynamic field. This study supplements and expands the theory of coal mine water pollution prevention and control.

Accurately detecting tea bud leaves is crucial for the automation of tea picking robots. However, challenges arise due to tea stem occlusion and overlapping of buds and leaves, presenting varied shapes of one bud–one leaf targets in the field of view, making precise segmentation of tea bud leaves challenging. To improve the segmentation accuracy of one bud–one leaf targets with different shapes and fine granularity, this study proposes a novel semantic segmentation model for tea bud leaves. The method designs a hierarchical Transformer block based on a self-attention mechanism in the encoding network, which is beneficial for capturing long-range dependencies between features and enhancing the representation of common features. Then, a multi-path feature aggregation module is designed to effectively merge the feature outputs of encoder blocks with decoder outputs, thereby alleviating the loss of fine-grained features caused by downsampling. Furthermore, a refined polarized attention mechanism is employed after the aggregation module to perform polarized filtering on features in channel and spatial dimensions, enhancing the output of fine-grained features. The experimental results demonstrate that the proposed Unet-Enhanced model achieves segmentation performance well on one bud–one leaf targets with different shapes, with a mean intersection over union (mIoU) of 91.18% and a mean pixel accuracy (mPA) of 95.10%. The semantic segmentation network can accurately segment tea bud leaves, providing a decision-making basis for the spatial positioning of tea picking robots.

The high growth height and substantial weight of bananas present challenges for robots to harvest autonomously. To address the issues of high labor costs and low efficiency in manual banana harvesting, a highly autonomous and integrated banana-picking robot is proposed to achieve autonomous harvesting of banana bunches. A prototype of the banana-picking robot was developed, featuring an integrated end-effector capable of clamping and cutting tasks on the banana stalks continuously. To enhance the rapid and accurate identification of banana stalks, a target detection vision system based on the YOLOv5s deep learning network was developed. Modules for detection, positioning, communication, and execution were integrated to successfully develop a banana-picking robot system, which has been tested and optimized in multiple banana plantations. Experimental results show that this robot can continuously harvest banana bunches. The average precision of detection is 99.23%, and the location accuracy is less than 6 mm. The robot picking success rate is 91.69%, and the average time from identification to harvesting completion is 33.28 s. These results lay the foundation for the future application of banana-picking robots.

In order to improve the efficiency of fine segmentation and obstacle removal in the road of tea plantation in hilly areas, a lightweight and high-precision DR-YOLO instance segmentation algorithm is proposed to realize environment awareness. Firstly, the road data of tea gardens in hilly areas were collected under different road conditions and light conditions, and data sets were generated. YOLOv8n-seg, which has the highest operating efficiency, was selected as the basic model. The MSDA-CBAM and DR-Neck feature fusion network were added to the YOLOv8-seg model to improve the feature extraction capability of the network and the feature fusion capability and efficiency of the model. Experimental results show that, compared with the YOLOv8-seg model, the DR-YOLO model proposed in this study has 2.0% improvement in AP@0.5 and 1.1% improvement in Precision. In this study, the DR-YOLO model is pruned and quantitatively compressed, which greatly improves the model inference speed with little reduction in AP. After deploying on Jetson, compared with the YOLOv8n-seg model, the Precision of DR-YOLO is increased by 0.6%, the AP@0.5 is increased by 1.6%, and the inference time is reduced by 17.1%, which can effectively improve the level of agricultural intelligent automation and realize the efficient operation of the instance segmentation model at the edge.

The main purpose of this work is to develop a transient analytical model of an industrial tea dryer based on the theory of heat and mass balance, and provide the variation characteristics in tea moisture content and drying air temperature throughout the whole drying process in the drying chamber. A numerical simulation focusing on the established transient analytical model was conducted, and the simulation results show that the moisture content of tea in the drying chamber performs a descend trend along the flow direction over drying time, while the temperature of the dry air performs an ascend trend along the flow direction of tea. The variations coefficient for the tea moisture content and the dry air temperature in the whole drying chamber were determined to be 0.009–0.09 and 0.065–0.126, respectively, indicating that the overall drying process is relatively stable. An actual production drying test was conducted to validate the analytical model, the test results showed that under dynamic drying conditions, the dynamic changes in tea moisture content and dry air temperature in the drying warehouse are consistent with the corresponding trends predicted by the model. Besides, the determination coefficient of tea moisture content is greater than 0.99, the determination coefficient of dry air in the initial drying period is 0.8027, while it is determined to be 0.9872 in the redrying period. The predicted results of the model are in good agreement with the experimental results, which confirms the effectiveness and reliability of the model. The developed transient analytical model can provide a certain reference for the anaphase development of energy optimization, dynamic state monitoring, and parameter matching systems for tea dryer.

Taking the Yili mining area in Xinjiang as an example, combined with the typical hydrogeological characteristics of the Tianshan Mountains, such as strong snowmelt runoff and uneven permeability of Jurassic coal-rich strata, the macroscopic hydrogeological conditions, microscopic water-bearing media characteristics and water-rich law of the main aquifers (aquicludes) layers in the mining area were systematically studied by means of field sampling, electron microscope scanning, mercury injection experiment, X-ray diffraction and pumping test. The hydrodynamic characteristics and migration law of aquifers under special stratigraphic and geomorphological conditions are revealed. The results show that the effective porosity range of the sandstone aquifer in the roof and floor of the Jurassic main coal seam in the study area is 17.1%-29.5%, and the effective porosity range of the Neogene and Jurassic aquiclude is 17.6%-29.0%. The aquifers (aquicludes) layer contains high clay minerals, which are expanded and argillized in water. The special microstructure and mineral composition make the aquifers (aquicludes) layer show the characteristics of high porosity and poor permeability. In the study area, the water abundance of the Quaternary pore aquifer and the Jurassic main coal seam roof and floor sandstone aquifer is weak, and some areas show moderate water abundance, mainly static reserves, with block characteristics. The hydrodynamic characteristics and migration law of the main aquifers (aquicludes) are systematically studied. The Quaternary system is affected by the macro-geomorphology and hydrogeological conditions of the piedmont, showing the dynamic characteristics of ‘slope transit flow’ in the form of pore phreatic water with high intensity flow. At the same time, it is blocked by the lower Neogene mudstone aquifuge and has no hydraulic connection with the sandstone aquifer of the roof of the coal measures. The sandstone aquifer of the 5# coal seam floor mainly affects the coal seam mining, in view of this layer, the “factory” shape of “lateral closure and lateral cover” was constructed in a large range and the dredging effect was remarkable, which created good hydrogeological conditions for underground mining. The relevant research results reveal the characteristics of special water-bearing media and the law of water storage and water control formed by the Jurassic coal-rich strata in the study area under the influence of sedimentary environment, which can provide certain theoretical and engineering basis for water prevention and control under similar mining conditions in the study area and western mining area.

As the world's demand for rare earths soars, the supply of rare earths is in danger of being depleted. The recycling rate of rare earth is only 1 % of all rare earth. Therefore, the recovery of rare earth is crucial and challenging. A phosphoric acid modified kaolin (P-K) was prepared by a simple mechanochemical method, and the selective recovery of rare earth element lanthanum (La) was studied. The P-K was characterized and analyzed by SEM, EDS, and FT-IR. Additionally, the different modification conditions of P-K preparation such as milling speed, time, and mass ratio of H3PO4/kaolin on La adsorption were investigated, and the effects of operating conditions such as solution initial pH, adsorption time, and adsorbent concentration on La adsorption were studied. The adsorption behavior of La by P-K was consistent with the Langmuir isothermal adsorption model with a saturated adsorption capacity of 18.55 mg/g, and the adsorption kinetics accorded with the pseudo-second-order kinetic model. Additionally, La in the mixture of La and magnesium (Mg) can be selectively adsorbed through P-K, while the adsorption rate of Mg is almost 0. P-K load with La could be eluted by 0.03 mol/L HCl, and the elution rate of La can reach 99.9 %. As a result, this study demonstrates a method for the recovery of La from rare earth wastewater.

Machine vision technology has dramatically improved the efficiency, speed, and quality of fruit-picking robots in complex environments. Target recognition technology for fruit is an integral part of the recognition systems of picking robots. The traditional digital image processing technology is a recognition method based on hand-designed features, which makes it difficult to achieve better recognition as it results in dealing with the complex and changing orchard environment. Numerous pieces of literature have shown that extracting special features by training data with deep learning has significant advantages for fruit recognition in complex environments. In addition, to realize fully automated picking, reconstructing fruits in three dimensions is a necessary measure. In this paper, we systematically summarize the research work on target recognition techniques for picking robots in recent years, analyze the technical characteristics of different approaches, and conclude their development history. Finally, the challenges and future development trends of target recognition technology for picking robots are pointed out.

In this paper, we introduce a low-cost, expansible, and compatible measurement and control system for atomic force microscopes (AFM) based on a quartz tuning fork (QTF) self-sensing probe and frequency modulation, which is mainly composed of an embedded control system and a probe system. The embedded control system is based on a dual-core OMAPL138 microprocessor (DSP + ARM) equipped with 16 channels of a 16-bit high-precision general analog-to-digital converter (ADC) and a 16-bit high-precision general digital-to-analog converter (DAC), six channels of an analog-to-digital converter with a second-order anti-aliasing filter, four channels of a direct digital frequency synthesizer (DDS), a digital input and output (DIO) interface, and other peripherals. The uniqueness of the system hardware lies in the design of a high-precision and low-noise digital—analog hybrid lock-in amplifier (LIA), which is used to detect and track the frequency and phase of the QTF probe response signal. In terms of the system software, a software difference frequency detection method based on a digital signal processor (DSP) is implemented to detect the frequency change caused by the force gradient between the tip and the sample, and the relative error of frequency measurement is less than 3%. For the probe system, a self-sensing probe controller, including an automatic gain control (AGC) self-excitation circuit, is designed for a homemade balanced QTF self-sensing probe with a high quality factor (Q value) in an atmospheric environment. We measured the quality factor (Q value) of the balanced QTF self-sensing probes with different lengths of tungsten tips and successfully realized AFM topography imaging with a tungsten-tip QTF probe 3 mm in length. The results show that the QTF-based self-sensing probe and the developed AFM measurement and control system can obtain high quality surface topography scanning images in an atmospheric environment.

Ganoderma resinaceum is a valuable Chinese medicine. This study aimed to investigate whether a G. resinaceum alcohol extract (GRAE) improves sleep, and analyze the potential mechanism. After 30 days of continuous administration of GRAE at various doses, GRAE (1,000 mg/kg.bw) prolonged pentobarbital sodium-induced sleep, increased the rate of sleeping in mice treated with a subthreshold dose of pentobarbital sodium, and shortened sleep latency. The mice brain was analyzed using UPLC-MS/MS and RNA-sequencing. Metabolomics analysis revealed that 73 metabolites in the high-dose (HD) group had changed significantly, mainly in amino acids and their derivatives, especially the accumulation of L-glutamine and PGJ2 (11-oxo-15S-hydroxy-prosta-5Z, 9, 13E-trien-1-oic acid). Transcriptome analysis revealed 500 differential genes between HD and control groups, mainly enriched in neuroactive ligand-receptor interaction, amphetamine addiction, and cocaine addiction pathways. The conjoint analysis of the transcriptome and metabolome showed that the biosynthesis of L-glutamine might be regulated by Homer1, Homer3, and Grin3b. This suggests that GRAE may affect L-glutamine accumulation by regulating the expression of these genes. This study showed that GRAE may prolong the sleep time of mice by reducing the accumulation of L-glutamine and deepens our understanding of the regulatory network between certain genes and L-glutamine.

In order to prevent the occurrence of water inrush accidents, it is particularly important to predict the mine water inrush, especially the accurate prediction of the mine water inflow in the coal mining process. Mine water inflow is caused by mining disturbance, groundwater flows into mining panels along the mining-induced fissures, and a depression cone of water table is formed in certain range around the mining sites. Mining panel is usually regarded as an irregular “virtual large diameter well” so as to calculate mine water inflow in China. However, in the mining process, the area, shape, and cross section of goafs are constantly changing, so is the water inflow. Therefore, the central position and influence radius of the “virtual large diameter well” in the spatial and temporal distribution are in a dynamic process of continuous movement and expansion, rather than being confined to a static position. This paper firstly analyzes the formation mechanism of coal mine roof water inflow and the errors and defects in the calculation of mine water inflow by virtual large diameter well (VLDWM). Then, based on the theory of steady flow and combined with the dynamic change process of goaf area and depression cone of water table during mining activities, this paper proposes an improved method and puts forward a concept of dynamic virtual large diameter well (DVLDWM) and establishes a theoretical model of the central position and influence radius of the “mining large diameter well” moving forward. The first (periodic) caving step is taken as the calculation unit and generalized as the “dynamic large diameter well,” which is used to calculate the dynamic water inflow in the process of mine advancing. Taking the No.7208 mining panel in Zhangshuanglou Coalmine in Xuzhou City, Jiangsu Province, as the study area, the mine water inflow was calculated dynamically by using the DVLDWM. The results show that the mine water inflow calculated is similar to the actual mine water inflow of the No.7208 mining panel observed, thus proving the reliability and credibility of the DVLDWM.

Nanofluid as heat transfer medium in microchannel heat sink is very common. It is a promising approach to employ electrohydrodynamic(EHD) effect to improve the heat transfer efficiency. A modified Buongiorno’s model including EHD effect was established. And numerical simulations on flow and heat transfer for various electrode arrangements, various electric field intensity and frequencies were carried out. The numerical results show that, the main mechanisms to elevate the heat transfer in microchannel are chaotic convection induced by the electrophoretic effect and thermophoretic effect. The electrode-pair with top positive bottom negative arrangement and an alternating voltage are beneficial to the heat transfer enhancement. The heat transfer enhancement factor can be improved up to 10% and temperature on heated wall decreased up to 5 K for 1 V applied voltage. Besides, a moderate frequency of alternating electric field should be a good choice because about 20% increase in enhancement factor can be obtained. A promising approach can be provided for the design of microchannel heat sink based EHD effect.

Free radicals such as reactive oxygen species (ROS) play a significant role in the innate immune system. Nicotinamide adenine dinucleotide phosphate (NADPH) oxidases (Noxes) are found to be involved in ROS production and played crucial roles in host biological processes of humans, insects, and plants. However, there are relatively few studies on the function of Noxes in crustaceans. In this study, the full-length cDNA of Nox was cloned from Litopenaeus vannamei by rapid amplification of cDNA ends (RACE) to obtain a 3790 base pairs (bp) cDNA sequence. LvNox cDNA comprised a 5’-untranslated region (UTR) of 454 bp, a 3’-UTR of 333 bp, and a putative open reading frame (ORF) of 3006 bp. Structural analysis showed that LvNox had a transmembrane helical segment (THS) at positions 233-255, a flavin adenine dinucleotide (FAD) binding domain (at positions 447-579), and a nicotinamide adenine dinucleotide (NAD) binding domain (at positions 815-990). Tissue distribution analysis revealed that LvNox mRNA was expressed in heart, intestine, muscle, gill, hepatopancreas, stomach, epidermis, hemocyte, and eyestalk. High expression levels of LvNox were observed in epidermis, hemocyte and eyestalk. The expression levels of LvNox were significantly induced after Vibrio alginolyticus injection for 6, 12, 24 and 48 h, and after Cu exposure for 24 and 48 h. Furthermore, the RNA interference of LvNox significantly reduced the production of ROS and the expression levels of copper/zinc superoxide dismutase (Cu-Zn SOD), catalase (CAT) and nitric oxide synthase (NOS). These results indicated that LvNox could be induced by V. alginolyticus infection and Cu exposure, which might be involved in immune response of shrimp via regulating the expression of antioxidant genes. These findings provide a solid foundation for further study of the function of Noxes in crustaceans.

Recently, great advances had been made by using scanning probe microscopy (SPM) to quantify the relative permittivity of thin film materials on a nanometer scale. The imaging techniques of permittivity for thin film materials with SPM, especially for photoelectric materials, have not been fully researched until now. Here, we presented a method to image permittivity of thin film materials by using a scanning capacitance microscope (SCM). This method combined the quantitative measurement by using SCM with the capacitance gradient–distance fitting curve to obtain the two-dimensional (2D) permittivity image at room temperature under atmospheric conditions. For the demonstration, a 2D permittivity image of film of molybdenum oxide (MoO3), a kind of photoelectric material, was acquired. From the image, it could be found that the average values of permittivity of MoO3 film and of MoO3 film-doped NaCl were about 8.0 and 9.5, respectively. The experimental results were quantitatively consistent with other experimental results of the same material. The reported technique here could provide a novel method for imaging the relative permittivity with nanometer resolution and be helpful for the study of photoelectric materials.

The purpose of this research work is to perform detailed exergetic, energetic and techno-economic analysis of the black tea drying process in the gas-type industrial dryer. Exergy–energy and techno-economic methodology was applied to investigate the heat loss, exergetic and energetic performance, exergy efficiency, improvement potential rate, sustainability index and techno-economic performance of a drying system. The results showed that the heat loss of exhaust air in the late drying process played a main contributing role in the heat loss and exergy loss of the whole drying system. Therefore, the exergy efficiency of the initial drying period and the redrying period varied from 38.08% to 65.09% and 24.76% to 26.97%, respectively. In addition, the improvement potential rate and sustainability index of the whole system varied from 6.93 kW to 12.94 kW and 1.33 to 2.86, respectively. The improvement potential obtained in the present work indicated that the drying operation is greatly in need of exergy performance improvement. Finally, the net present value and payback period obtained from techno-economic analysis were 179,442.03 USD and 5.3 years, the result is useful for investors or contractors to refer to and make investment decisions.

Maize (Zea mays L.) is the most essential food crop in the world. However, maize is highly susceptible to drought stress, especially at the seedling stage, and the molecular mechanisms underlying drought tolerance remain elusive. In this study, we conducted comparative transcriptome and physiological analyses of drought-tolerant (CML69) and susceptible (LX9801) inbred lines subjected to drought treatment at the seedling stage for three and five days. The tolerant line had significantly higher relative water content in the leaves, as well as lower electrolyte leakage and malondialdehyde levels, than the susceptible line. Using an RNA-seq-based approach, we identified 10,084 differentially expressed genes (DEGs) with 6906 and 3178 DEGs been annotated and unannotated, respectively. Two critical sets of drought-responsive DEGs, including 4687 genotype-specific and 2219 common drought-responsive genes, were mined out of the annotated DEGs. The tolerant-line DEGs were predominantly associated with the cytoskeleton, cell wall modification, glycolysis/gluconeogenesis, transport, osmotic regulation, drought avoidance, ROS scavengers, defense, and transcriptional factors. For the susceptible line, the DEGs were highly enriched in the photosynthesis, histone, and carbon fixation pathways. The unannotated DEGs were implicated in lncRNAs, including 428 previously reported and 22% putative TE-lncRNAs. There was consensus on both the physiological response and RNA-seq outcomes. Collectively, our findings will provide a comprehensive basis of the molecular networks mediating drought stress tolerance of maize at the seedling stage.

In an orchard environment with a complex background and changing light conditions, the banana stalk, fruit, branches, and leaves are very similar in color. The fast and accurate detection and segmentation of a banana stalk are crucial to realize the automatic picking using a banana picking robot. In this paper, a banana stalk segmentation method based on a lightweight multi-feature fusion deep neural network (MFN) is proposed. The proposed network is mainly composed of encoding and decoding networks, in which the sandglass bottleneck design is adopted to alleviate the information a loss in high dimension. In the decoding network, a different sized dilated convolution kernel is used for convolution operation to make the extracted banana stalk features denser. The proposed network is verified by experiments. In the experiments, the detection precision, segmentation accuracy, number of parameters, operation efficiency, and average execution time are used as evaluation metrics, and the proposed network is compared with Resnet_Segnet, Mobilenet_Segnet, and a few other networks. The experimental results show that compared to other networks, the number of network parameters of the proposed network is significantly reduced, the running frame rate is improved, and the average execution time is shortened.