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Due to the harsh underground environment during coal mining, the quality of images collected by cameras is not sufficient, and the acquired images are greatly affected by noise, affecting visual observation; to a certain extent, subsequent intelligent mining is limited. A morphological Sobel coal-rock boundary recognition algorithm is proposed according to the different gray levels of coal-rock images to solve the problem of coal image quality. First, the details of the coal and rock images are smoothly preprocessed to improve the contrast between the feature boundaries and surrounding pixels, and the gray-level adaptive threshold is applied after processing. Morphological corrosion theory is used to process the morphological structure in an image, and the corresponding boundary in the image is extracted for recognition. Compared with the boundary points identified by each algorithm, the area error of coal and rock identification is calculated by using the boundary point fitting curve. The morphological Sobel algorithm is used to calculate the identification area error of coal and rock at different angles according to the camera range. The experimental results show that the boundaries identified by the morphological Sobel algorithm have the best degree of overlap with the boundaries of the original image. The identification error area is only about 10% of the Sobel operator and Canny operator algorithm. Monitoring coal and rock specimens can enable the effective identification of coal and rock boundaries from various angles.

Aims Intensive soybean/maize intercropping, a specific form of intercropping, holds promise in addressing the challenges posed by increasing food demands, diminishing cropland areas, deteriorating soil quality, and escalating environmental pollution. Methods To evaluate the potential of this system, we conducted a national meta-analysis, quantifying its absolute yield gain (net effect, NE) and land use efficacy (land equivalent ratio, LER). We further investigated the underlying mechanisms by examining local climate, soil properties, and field management practices and then developed random forest (RF) models to assess the system's potential, incorporating current information on natural resources. Results In China, an average NE of 3.2 ± 0.1 Mg ha −1 and LER of 1.4 ± 0.02 were achieved by intensive soybean/maize intercropping. The variance of NE was significantly influenced by air temperature (10%), soybean delay days (8%), and maize plant density (9%). Similarly, the LER was strongly driven by soybean delay days (14%), sunshine hours (11%), and maize density (10%). Notably, this intensive intercropping system efficiently utilizes available resources, such as light, temperature (heat), accumulated temperature, and soil nutrients, particularly in regions characterized by low soil fertility and limited agricultural resources. Ultimately, the RF model estimated substantial overyielding of 2 800 kg per hectare, representing approximately 1.4 times the current soybean and maize production under China's monoculture. Conclusions The implementation of intensive soybean/maize intercropping is highly beneficial throughout China, especially in areas with limited agricultural resources. The Yangtze River Basin, in potentially, emerges as the most suitable region for adopting this intensive intercropping practice.

In this paper, we consider the fourth hybrid power mean involving two-term exponential sums and third-order character sum modulo p, a topic of significant importance in analytic number theory. These results generalize prior research, and provide new insights for studying the relationship between character sums and exponential sums.

The specific surface area (SSA) is a crucial parameter for estimating the adsorption capacity of shale, significantly influencing its adsorption characteristics. The Brunauer-Emmett-Teller (BET) method was widely used to characterize the surface area of various porous materials. However, research on its applicability for characterizing shale surface areas, particularly concerning the adsorption mechanism of nitrogen in shale nanopores, remains limited. In this study, ultra-low-pressure nitrogen adsorption experiments and molecular simulation methods were employed to characterize the adsorption behavior of nitrogen on shale nanopore surfaces at 77 K. The results indicate that the assumptions of the classic BET isotherm model do not fully align with the state and microscopic mechanisms of nitrogen on shale surfaces. Nitrogen exhibits multilayer adsorption on shale surfaces represented by organic matter and Illite, but the initial pressure for multilayer adsorption varies with the rock phase surface. Calculating the specific surface area of organic matter in shale using the relative pressure range recommended by the classic BET theory results in a certain degree of error. Through analysis of isotherm adsorption curves, density field distributions, and intermolecular interactions, the adsorption mechanisms of nitrogen on shale pore surfaces were elucidated. It was found that for organic matter, a more suitable relative pressure range for BET calculations is 0.002–0.035, whereas for Illite, it is 0.035-0.2. This study provided crucial insights into the adsorption mechanisms of nitrogen on shale pore surfaces and the optimization of BET surface area characterization for shale nanopores, laying a theoretical foundation for predicting shale adsorption capacity and estimating in-situ natural gas in shale.

Background Vine cutting propagation in yams offers a transformative approach to conventional tuber-dependent cultivation, with enhanced tuber yield and quality. Adventitious root (AR) formation is a critical prerequisite for vine cutting survival, with substantial variability among yam varieties. However, relatively little is known about the regulatory mechanisms that restrict the application of cutting propagation in recalcitrant varieties. In this study, we integrated rooting rate comparisons, anatomical observations, phytohormone content determination, and transcriptomic profiling to elucidate the developmental mechanisms influencing AR formation. Results The adventitious rooting capacity of Dioscorea polystachya was significantly different from that of Dioscorea alata . Six D. alata cultivars showed rooting efficiencies exceeding 70%. In contrast, D. polystachya variety RuiChang Shan Yao (RCSY) exhibited a recalcitrant phenotype with a rooting rate of less than 5%. Phenotypic evaluation identified the AR formation phase from 0 to 12 days after cutting (DAC). Anatomical observations indicated AR initiation within the phloem tissues by two DAC, followed by complete penetration of the cortical and epidermal layers by four DAC in D. alata . Temporal phytohormone profiling showed higher auxin levels in Gan Bai Yu (GBY) and Gan Zi Yun (GZY) than in RCSY during AR formation. Transcriptomic profiling analysis of GBY and GZY at 0, 2, 4, and 8 DAC identified 9,680 differentially expressed genes (DEGs). Integrated with hormonal and rooting data, weighted gene co-expression network analysis delineated AR-associated modules (saddlebrown, magenta, orange). Kyoto Encyclopedia of Genes and Genomes enrichment underscored starch and sucrose metabolism (31 DEGs) and hormone signal transduction (18 DEGs) as central pathways. Exogenous application of 1-naphthaleneacetic acid enhanced the rooting rate. Sucrose and starch accumulation were positively associated with AR competence in GBY, GZY, and the recalcitrant RCSY. Cross-species analysis identified 39 conserved DEGs in RCSY, including six auxin-responsive genes (one IAA16 , one ARF9 , two ARR11 , one SAUR50, one SAUR32 ), two cytokinin-responsive genes (two RR9 ), a GA-related gene (one GID1C ), six ABA-related genes (two PYL10 , one AHG1 , two ABF ), and 24 starch and sucrose metabolism-related genes ( SUS7 , HXK1, FRK2 , SS2 , TPPs , DPEP , GLUs , and BGLUs ), which implied their roles in AR regulation. Conclusions These findings identify the key molecular drivers of AR formation in yams, offering new insights into rooting recalcitrance and strategies for optimizing clonal propagation in agricultural species.

Selenium (Se) is an essential element for humans and animals, and Se deficiency-related diseases are a significant global health concern. Tea may help ameliorate Se deficiencies. However, the mechanisms of natural Se enrichment in tea remain poorly understood, particularly in high-Se soils, such as those in Jiangxi Province. This study conducted a comprehensive field survey of 67 soil and tea samples from Jiangxi, a major tea production region in China, to analysis spatial variation in Se concentrations and identify key driving mechanisms. The average soil Se concentration across Jiangxi was 0.44 mg kg − 1 , exceeding both global (0.15 mg kg − 1 ) and Chinese (0.29 mg kg − 1 ) averages. Soil Se content was significantly influenced by soil organic matter (SOM), total potassium (K), iron (FeOX) and aluminium (AlOX) oxides, and tea planting duration, contributing 15%, 6%, 20%, 22%, and 10%, respectively. Soil Se levels increased with SOM and planting duration, but decreased with total K content. Conversely, the average Se content in tea leaves was only 0.10 mg kg − 1 , and primarily driven by soil Se (29%), followed by FeOX (13%), AlOX (15%), SOM (5%), available K (5%), as well as inputs of nitrogen and K fertilizer (6% and 5% respectively), each. Partial least squares models identified four key pathways in which environment and human management practices, directly or via interactions with soil properties (SOM, K, FeOX, AlOX), influenced Se transfer from soil to tea leaves. Overall, our findings indicate that tea cultivation is more suitable in areas with high soil Se, such as central Jiangxi Province, and suggest that tea Se content can be enhanced by increasing levels of soil Se, SOM, K, FeOX, and AlOX.

DEAD-box decapping enzyme 20 (DDX20) is a putative RNA-decapping enzyme that can be identified by the conserved motif Asp–Glu–Ala–Asp (DEAD). Cellular processes involve numerous RNA secondary structure alterations, including translation initiation, nuclear and mitochondrial splicing, and assembly of ribosomes and spliceosomes. DDX20 reportedly plays an important role in cellular transcription and post-transcriptional modifications. On the one hand, DDX20 can interact with various transcription factors and repress the transcriptional process. On the other hand, DDX20 forms the survival motor neuron complex and participates in the assembly of snRNP, ultimately affecting the RNA splicing process. Finally, DDX20 can potentially rely on its RNA-unwinding enzyme function to participate in microRNA (miRNA) maturation and act as a component of the RNA-induced silencing complex. In addition, although DDX20 is not a key component in the innate immune system signaling pathway, it can affect the nuclear factor kappa B (NF-κB) and p53 signaling pathways. In particular, DDX20 plays different roles in tumorigenesis development through the NF-κB signaling pathway. This process is regulated by various factors such as miRNA. DDX20 can influence processes such as viral replication in cells by interacting with two proteins in Epstein–Barr virus and can regulate the replication process of several viruses through the innate immune system, indicating that DDX20 plays an important role in the innate immune system. Herein, we review the effects of DDX20 on the innate immune system and its role in transcriptional and post-transcriptional modification processes, based on which we provide an outlook on the future of DDX20 research in innate immunity and viral infections.

Additive manufacturing (AM) technology is gaining acceptance as a strategic manufacturing technique for allowing new product development. Due to ongoing process improvement, design for AM (DFAM) has become a major issue in harnessing AM’s production and development possibilities to achieve design freedom. The classical design process model does not encompass all the knowledge available to take advantage of design freedom. Therefore, a conceptual and in-depth analysis of design alternatives is necessary to determine the manufacturing process. As a result, this research proposed a design process model for a DFAM to attain design freedom with a unique approach and resource selection steps for fused deposition modeling (FDM) that uses an information model based on evolving knowledge and addressing the challenges. The proposed design process model uses an event knowledge graph (EKG) to outline the product manufacturability from the perspective of DFAM limitations. Event-based knowledge representation provides causality information for knowledge-based reasoning in causality analysis tasks. A relationship-aware mechanism is then used to express events on the graph that are directed from entities to occurrences to efficiently extract the most relevant details. Thus, this implements a step-by-step approach to process and resource specifications during the design stage. Consequently, it offers a comprehensive learning approach for establishing and modeling intrinsic relationships to attain flexibility and design freedom. The efficacy and feasibility of the proposed approach are verified by using an application case study of an intake system based on the airflow sensing rate and controls how much air is fed into the engine.

Understanding the adsorption mechanisms of CO 2 and N 2 in illite, one of the main components of clay in shale, is important to improve the precision of the shale gas exploration and development. We investigated the adsorption mechanisms of CO 2 and N 2 in K-illite with varying pore sizes at the temperature of 333, 363 and 393 K over a broad range of pressures up to 30 MPa using the grand canonical Monte Carlo (GCMC) simulation method. The simulation system is proved to be reasonable and suitable through the discussion of the impact of cation dynamics and pore wall thickness. The simulation results of the excess adsorption amount, expressed per unit surface area of illite, is in general consistency with published experimental results. It is found that the sorption potential overlaps in micropores, leading to a decreasing excess adsorption amount with the increase of pore size at low pressure, and a reverse trend at high pressure. The excess adsorption amount increases with increasing pressure to a maximum and then decreases with further increase in the pressure, and the decreasing amount is found to increase with the increasing pore size. For pores with size greater larger than 2 nm, the overlap effect disappears.

Fluid movability in tight sands may not be accurately characterized by pore size-based classification methods solely because of the complex pore structure and heterogeneity in pore size . In this study, on the basis of casting thin slices and scanning electron microscope observation, pore structure was analyzed using mercury injection, NMR, and micron CT to classify and evaluate the tight oil reservoir. The experiment suggest that the quality of tight reservoir is determined by its pore structure, particularly the throat radius, with the microthroat being an essential factor in permeability. Uniquely, we divide the reservoir by Q-cluster with throat radius, displacement pressure, permeability and other parameters. Based on reservoir classification, this study proposed a method for studying the pore size classification of samples on the T 2 spectrum by combining CT scanning with mercury intrusion and a NMR experiment. Pore fluids are generally classified into movable fluid and irreducible fluid by one or two NMR T 2 cut-offs. The pore size distributions and capillarity boundaries are converted from T 2 and mercury injection capillary pressure (MICP). We categorized pores into micropores (T 2  < 1), macropores (T 2  > 10, with T 2  > 300 as fractures), and medium pores (the rest). The saturation of movable fluid and the percentage of micro-fractures can characterize the seepage characteristics of tight reservoirs, which is of great significance for the later periods of oilfield development. Highlights In this study, on the basis of casting thin slices and scanning electron microscope observation, pore structure was analyzed using mercury injection, NMR, and micron CT to classify and evaluate the tight oil reservoir. Uniquely, we divide the reservoir by Q-cluster with throat radius, displacement pressure, permeability and other parameters. Based on reservoir classification, this study proposed a method for studying the pore size classification of samples on the T2 spectrum by combining CT scanning with mercury intrusion and a NMR experiment. We categorized pores into micropores (T 2 < 1), macropores (T 2 > 10, with T 2 > 300 as fractures), and medium pores (the rest). The saturation of movable fluid and the percentage of micro-fractures can characterize the seepage characteristics of tight reservoirs, which is of great significance for the later periods of oilfield development.