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Lilium davidii var. unicolor is an important edible Llilium species in China. In this study, we investigated the regulatory proteins and pathways affecting bulblet development through analyzing the proteomes among three developmental stages using the isobaric tags for relative and absolute quantitation proteomics approach. A total of 4,802 proteins were detected, 529, 735, and 637 proteins were differentially expressed between the 0 d vs 15 d, 15 d vs 30 d, 0 d vs 30 d stages, respectively. The Gene Otology and Kyoto Encyclopedia of Genes and Genomes analysis indicated that the metabolic pathway, biosynthesis of secondary metabolites and microbial metabolism in diverse environments were the most enriched pathways, which may promote energy formation and protein synthesis processes. Starch and sucrose metabolism was vital to Lilium bulblets development, and energy needed was opposite in the early and later stage of lily bulblets development. Many proteins were significantly regulated, among them LdGASA was detected as being extremely differentially expressed. We cloned the full length of LdGASA, analyzed its expression profile during lily bulblet development, and constructed its overexpression vector and transformed into Arabidopsis thaliana. The results showed that the full length of LdGASA was 725 bp, and the CDS sequence was 336 bp, LdGASA was mainly expressed at the morphogenesis and development stages of bulblets, and reached the highest at the basic morphogenesis of bulblets (35 d), Arabidopsis thaliana transformation with LdGASA presented stronger growth. These results not only deepened our understanding of the bulblets proteome, but also uncovered new insights into lily bulblet enlargement.

Abstract Members of the eukaryotic translation initiation complex are co-opted in viral infection, leading to susceptibility in many crop species, including stone fruit trees (Prunus spp.). Therefore, modification of one of those eukaryotic translation initiation factors or changes in their gene expression may result in resistance. We searched the crop and wild Prunus germplasm from the Armeniaca and Amygdalus taxonomic sections for allelic variants in the eIF4E and eIFiso4E genes, to identify alleles potentially linked to resistance to Plum pox virus (PPV). Over one thousand stone fruit accessions (1397) were screened for variation in eIF4E and eIFiso4E transcript sequences which are in single copy within the diploid Prunus genome. We identified new alleles for both genes differing from haplotypes associated with PPV susceptible accessions. Overall, analyses showed that eIFiso4E is genetically more constrained since it displayed less polymorphism than eIF4E. We also demonstrated more variations at both loci in the related wild species than in crop species. As the eIFiso4E translation initiation factor was identified as indispensable for PPV infection, a selection of ten different eIFiso4E haplotypes along 13 accessions were tested by infection with PPV and eight of them displayed a range of reduced susceptibility to resistance, indicating new potential sources of resistance to sharka.

Mechanoluminescent (ML) materials can exhibit visible-to-near-infrared mechanoluminescence when responding to the fracture or deformation of a solid under mechanical stimulation. Transforming mechanical energy into light demonstrates promising applications in terms of visual mechanical sensing. In this work, we synthesized the phosphor CaZnOS:Tb3+, Sm3+, which exhibited intense and tunable multicolor mechanoluminescence without pre-irradiation. Intense green ML materials were obtained by doping Tb3+ with different concentrations. Tunable multicolor mechanoluminescence (such as green, yellow-green, and orange-red) could be realized by combining green emission (about 542 nm), attributed to Tb3+, and red emission (about 600 nm) generated from the Sm3+ in the CaZnOS substrate. The tunable multicolor ML materials CaZnOS:Tb3+, Sm3+ exhibited intense luminance and recoverable mechanoluminescence when responding to mechanical stimulation. Benefiting from the excellent ML performance and multicolor tunability in CaZnOS:Tb3+, Sm3+, we mixed the phosphor with PDMS and a curing agent to explore its practical application. An application for visual mechanical sensing was designed for handwriting identification. By taking a time-lapsed shot while writing, we easily obtained images of the writer’s handwriting. The images of the ML intensity were acquired by using specific software to transform the shooting data. We could easily distinguish people’s handwriting through analyzing the different ML performances.

Abstract To describe the spatiotemporal variations characteristics and future trends of urban air quality in China, this study evaluates the spatiotemporal evolution features and linkages between the air quality index (AQI) and six primary pollution indicators, using air quality monitoring data from 2014 to 2022. Seasonal autoregressive integrated moving average (SARIMA) and random forest (RF) models are created to forecast air quality. (1) The study’s findings indicate that pollution levels and air quality index values in Chinese cities decline annually, following a “U”-shaped pattern with a monthly variation. The pollutant levels are high in winter and low in spring, and low in summer and rising in the fall (O3 shows the opposite). (2) The spatial distribution of air quality in Chinese cities is low in the southeast and high in the northwest, and low in the coastal areas and higher in the inland areas. The correlation coefficients between AQI and the pollutant concentrations are as follows: fine particulate matter (PM2.5), inhalable particulate matter (PM10), carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O3) values are correlated at 0.89, 0.84, 0.54, 0.54, 0.32, and 0.056, respectively. (3) In terms of short-term AQI predictions, the RF model performs better than the SARIMA model. The long-term forecast indicates that the average AQI value in Chinese cities is expected to decrease by 0.32 points in 2032 compared to the 2022 level of 52.95. This study has some guiding significance for the analysis and prediction of urban air quality.

Kernel-using apricot (Prunus armeniaca L.) is an economically important fruit tree species in arid areas owing to its hardiness and cold and drought tolerance. However, little is known about its genetic background and trait inheritances. In the present study, we first evaluated the population structure of 339 apricot accessions and the genetic diversity of kernel-using apricots using whole genome re-sequencing. Second, the phenotypic data of 222 accessions were investigated for two consecutive seasons (2019 and 2020) for 19 traits, including kernel and stone shell traits and the pistil abortion rate of flowers. Heritability and correlation coefficient of traits were also estimated. The stone shell length (94.46%) showed the highest heritability, followed by the length/width ratio (92.01%) and length/thickness ratio (92.00%) of the stone shell, whereas breaking force of the nut (17.08%) exhibited a very low heritability. A genome-wide association study (GWAS) using general linear model and generalized linear mixed model revealed 122 quantitative trait loci (QTLs). The QTLs of the kernel and stone shell traits were unevenly assigned on the eight chromosomes. Out of the 1,614 candidate genes identified in the 13 consistently reliable QTLs found using the two GWAS methods and/or in the two seasons, 1,021 were annotated. The sweet kernel trait was assigned to chromosome 5 of the genome, similar to the almond, and a new locus was also mapped at 17.34–17.51 Mb on chromosome 3, including 20 candidate genes. The loci and genes identified here will be of significant use in molecular breeding efforts, and the candidate genes could play essential roles in exploring the mechanisms of genetic regulation.

Underwater target detection is an important method for detecting marine organisms. However, due to the image occlusion of underwater targets, blurred water quality, poor lighting conditions, small targets, and complex backgrounds, the detection of underwater biological targets has posed significant challenges. In the intricate underwater environment, the conventional feature extraction method has a few drawbacks, including imprecise feature extraction, sluggish detection speed, and inadequate robustness. Consequently, an underwater target detection method based on the enhanced You Only Look Once 7 (YOLOv7) is proposed in this study. The network architecture is reconstructed, and the Deformable Convolutional Network (DCN) modules replace some 3×3 convolutional blocks in the ELAN structure to offset sampling points and reduce background interference. Skip connections and 1 1 convolutional architecture are added to the DCN module to improve the model's perception of image details. In addition, Contextual Transformer 3 (COT3) is also incorporated to improve visual performance. Finally, to improve the detection efficiency of small objects, the CIoU loss function is finally replaced by the Normalized Wasserstein Distance (NWD) algorithm. The mAP of DCCN-YOLOv7 on the URPC dataset is 80.4%, according to the experimental results, 2.8% higher than the YOLOv7 network model that is used as a baseline. Furthermore, in contrast to the original YOLOv7 algorithm, the detection speed and accuracy are higher, making it more appropriate for target recognition underwater. [ABSTRACT FROM AUTHOR]

Abstract Achieving structure optimizing and component regulation simultaneously in the ZnIn2S4‐based photocatalytic system is an enormous challenge in improving its hydrogen evolution performance. 3D hollow‐structured photocatalysts have been intensively studied due to their obvious advantages in solar energy conversion reactions. The synthesis of 3D hollow‐structured ZnIn2S4, however, is limited by the lack of suitable template or synthesis methods, thereby restricting the wide application of ZnIn2S4 in the field of photocatalysis. Herein, Ce‐doped ZnIn2S4 photocatalysts with hollow nanocages are obtained via one‐step hydrothermal method with an ordered large‐pore tetrakaidecahedron cerium‐based metal–organic frameworks (Ce‐MOFs) as template and Ce ion source. The doping of Ce and the formation of ZnIn2S4 tetrakaidecahedron hollow nanocages with ultrathin nanosheet subunits are simultaneously induced by the Ce‐MOFs, making this groundbreaking work. The Ce‐doped ZnIn2S4 with a nonspherical 3D hollow nanostructure inherit the tetrakaidecahedron shape of the Ce‐MOF templates, and the shell is composed of ultrathin nanosheet subunits. Both theoretical and experimental results indicate that the doping of Ce and the formation of hollow nanocages increase light capture and the separation of photogenerated charge carriers.

A new practical automatic planning method for MV distribution network considering complementation of load characteristic and division of power supply is presented to solve the problems of large network size and low equipment utilization ratio caused by the lack of consideration of complementation of load characteristic in distribution network planning. First, based on the existing research results on load clustering and grid planning, the concepts of MV distribution network feeder block and power supply unit are redefined and the technical framework of MV distribution network planning considering division of power supply is designed. Then, the evaluation indexes of feeder block partitioning and power supply unit partitioning considering complementation of load characteristic are set up, and rotation center line distance weighting alternate location algorithm is proposed to divide the feeder block. Further, the MV distribution network planning model based on power supply unit partitioning is proposed, which takes the minimum value of the sum of interruption cost based on reliability index and line integrated cost of the planning year. Combining ant colony algorithm, automatic planning algorithm is proposed to get the combinatorial solution of trunk line and branch line. Finally, the scientificalness and practicability of the model and method in this paper was verified through the case study.

Improving fruit quality is one of the main tasks in modern commercial apricot breeding. Because of the lack of high-density linkage maps and fine mapping, it is difficult to obtain molecular markers that can assist in breeding for quantitative inheritance of fruit quality traits. In this study, specific-locus amplified fragment sequencing was used to genotype 169 seedlings of F1 apricot (Prunus armeniaca L.) progenies derived from crossing “Chuanzhihong” (H) with “Saimaiti” (S). After aligning to the Prunus armeniaca reference genome and filtering out low-quality variants, 6,012 high-quality single nucleotide polymorphisms were obtained and employed to construct a genetic map for each parent. The genetic linkage maps showed eight linkage groups of apricot, covering a distance of 809.6 cM in “H” and 1076.4 cM in “S”. The average distance between markers in “H” and “S” was 0.62 and 0.95 cM, respectively. To map quantitative trait loci (QTLs) for fruit quality, we investigated fruit quality traits, including fruit weight (FW), fruit height (FH), fruit lateral width (FL), fruit ventral width (FV), soluble solids content (SSC), and fruit firmness (FF) for all seedlings genotyped in 2018 and 2019. Eleven and nine QTLs linked to fruit quality traits were anchored on the “H” and “S” maps, respectively, and 1,138 putative candidate genes for 16 most significant regions on the corresponding chromosome were identified based on gene annotation. Among them, fruit size contained 648 genes in 11 intervals on the reference genome, SSC contained 372 genes in 3 intervals, and FF contained 117 genes in 2 intervals. Our findings uncovered the genetic basis of apricot fruit quality, and provided candidate genes for further molecular genetic studies on fruit quality and QTL targets for future marker-assisted selection of apricot quality improvement breeding.

To understand the metabolite dynamics and genetic regulatory mechanism of apricot shell, a typical endocarp, before and after lignification are unknown, we investigated the metabolite differences of the endocarp of ‘Youyi,’ a popular kernel-using apricot cultivar, using ultra-performance liquid chromatography tandem mass spectrometry strategy. The endocarp thickness increased rapidly from 8 to 37 days after flowering (DAF) and lignin deposition began at 37 DAF. In total, 626 non-volatile metabolites were obtained from the endocarp tissues before (33 DAF) and after (41 and 45 DAF) lignification. The relative sugar and organic acid contents decreased continuously and those of L-phenylalanine and L-tyrosine increased after lignification. In the non-lignified endocarp, the phenylpropanoid metabolites were mainly in the form of p-coumaric acid, ferulic acid, neochlorogenic acid, dicumarol, coniferin, and some lignans. After lignification, the metabolites were mainly in the form of glycoside lignin or lactone coumarins, and the relative contents of L-asarinin and forsythin increased. The results of transcriptome confirmed the upregulation of genes related to lignin biosynthesis, including β-glucosidase and coniferyl-alcohol glucosyltransferase and laccases, accelerated lignification. This study provides insights into the formation of lignified endocarp in a kernel-using apricot and clarifies the role of monolignin transport and oxidative polymerization.

This research proposes a refined deep learning framework aimed at boosting the precision and efficacy of detecting surface imperfections in strip steel. This method integrates enhancement and simplification techniques inspired by the You Only Look Once version 7 (YOLOv7) detection method, resulting in significant enhancements in the model's accuracy, speed, and flexibility. The substitution of ELAN with Bottleneck Transformer 3 (BoT3) leads to improved accuracy and mean Average Precision (mAP) values, while also introducing a more lightweight network architecture. The incorporation of the Involution mechanism enhances the model's feature extraction capabilities, thereby improving its ability to recognize small targets through the utilization of local perceptual fields. The ASPP_CA architecture leverages a multi-scale feature fusion technique along with an attention mechanism to reduce model parameters and enhance inference speed. Furthermore, it extends the model's receptive field, allowing it to capture additional visual information. The enhanced algorithm, denoted as YOLOv7-IBA, demonstrates empirical results that underscore its superiority over the three current state-of-the-art detection techniques in identifying surface flaws on strip steel. The accuracy has been improved to 82.9%, representing a significant increase of 7.2% compared to the previous performance. Furthermore, the mean mAP value has experienced a 3.2% increase, reaching a total of 79.9%. Moreover, there has been a remarkable 8.8% improvement in efficiency. The adoption of this approach holds the potential to enhance both the precision and productivity of strip surface flaw detection, while also providing valuable methodological support for the advancement of other related disciplines. [ABSTRACT FROM AUTHOR]

The characterization of the largest worldwide representative data set of apricot (Prunus armeniaca L.) germplasm was performed using molecular markers. Genetic diversity and structure of the cultivated apricot genetic resources were analyzed to decipher the history of diffusion of this species around the world. A common set of 25 microsatellite markers was used for genotyping a total of 890 apricot accessions in different collections from the center of origin to the more recent regions of apricot culture. Using a Bayesian model-based clustering approach, the apricot genotypes can be structured into five different genetic clusters (FST = 0.174), correlated with the geographical regions of origin of the accessions. Accessions from China and Central Asia were clustered together and exhibited the highest levels of diversity, confirming an origin in this region. A loss of genetic diversity was observed from the center of origin to both western and eastern zones of recent apricot culture. Altogether, our results revealed that apricot spread from China and Central Asia, defined as the center of origin, following three major diffusion routes with a decreasing gradient of genetic variation in each geographical group. The identification of specific alleles outside the center of origin confirmed the existence of different secondary apricot diversification centers. The present work provides more understanding of the worldwide history of apricot species diffusion as well as the field of conservation of the available genetic resources. Data have been used to define an apricot core collection based on molecular marker diversity which will be useful for further identification of genomic regions associated with commercially important horticultural traits through genome-wide association studies to sustain apricot breeding programs.

Anthocyanins are important pigments in peach fruit and are beneficial to human health. Sugars are both energy-storing and signaling molecules and their roles in inducing anthocyanin biosynthesis have received a great deal of research attention. However, the mechanism by which sugars induce anthocyanin biosynthesis in peach fruit is unknown. In order to understand this induction mechanism, comprehensive transcriptome and metabolome were performed in fruit flesh treated with four different sugars for 12 and 24 h, respectively. Here, we found that cyanidin-3-O-(6-O-p-coumaroyl) glucosides accumulated in fruit flesh treated with glucose, sucrose, sorbitol, and fructose in vitro. Two key structural genes of the anthocyanin biosynthesis pathway, namely, PpDFR and PpUFGT, were upregulated in the flesh of sugar-treated peach fruit. By contrast, the two main transcription factors (TFs) PpMYB10.1 and PpBL regulating anthocyanin biosynthetic genes in peach fruit were not upregulated accordingly. Interestingly, two MYB family genes (PpMYB6 and PpMYB44-like) and three bHLH family genes (PpbHLH35, PpbHLH51, and PpbHLH36-like) were upregulated. A dual luciferase assay revealed that PpMYB6 strongly activated the PpUFGT promoter when it was co-infiltrated with PpbHLH35, PpbHLH51, and PpbHLH36-like. When PpMYB44-like was co-infiltrated with PpbHLH35, it also potently activated the PpUFGT promoter. The results of this study help clarify the molecular mechanisms by which glucose, sucrose, sorbitol, and fructose regulate anthocyanin accumulation in peach fruit.

Abstract A multifunctional fluorescent probe BHN-Fe3O4@SiO2 nanostructure for Fe3+ was designed and developed. It has a good selective response to Fe3+ with fluorescence quenching and can be recycled using an external magnetic field. With adding EDTA (2.5 × 10−5 M) to the consequent product Fe3+-BHN-Fe3O4@SiO2, Fe3+ can be removed from the complex, and its fluorescence probing ability recovers, which means that this constituted on-off type fluorescence probe could be reversed and reused. At the same time, the probe has been successfully applied for quantitatively detecting Fe3+ in a linear mode with a low limit of detection 1.25 × 10−8 M. Furthermore, the BHN-Fe3O4@SiO2 nanostructure probe is successfully used to detect Fe3+ in living HeLa cells, which shows its great potential in bioimaging detection.

This paper mainly focuses on the relationship between the subjective evaluation of air quality and the quality of life (QOL) of middle-aged and elderly residents in China. The 2018 China Health and Retirement Longitudinal Study (CHARLS) project database is the key sources of data, from which 16,736 valid samples were used in our research. Multivariate linear regression analysis and binomial logistic regression model were applied to detect the impact of the subjective evaluation of air quality on QOL, which was evaluated in two dimensions, which are health utility and experienced utility, using the health utility EQ-5D score and the experienced utility of life satisfaction score. Our results show that there is a significant positive correlation between the subjective evaluation of air quality and the two dimensions of QOL. Age, education, marital status and sleep status also have a relatively great impact on the QOL of residents. This worked studied the overall QOL of middle-aged and elderly residents in China, while policy suggestions regarding high-quality air public goods are also given in the paper.

The detection of small objects in UAV (Unmanned Aerial Vehicle) target acquisition is a persistent difficulty, mainly because of the abundance of small targets, significant mutual obstruction, limited feature statistics, and the intricate background in the target area. These factors collectively result in low detection accuracy. In order to tackle this challenge, we provide a small target recognition model dubbed YOLOv7X+ (You Only Look Once v7X+), which aims to improve the capability of detecting small targets by UAVs while also ensuring its operational effectiveness. The model effectively addresses the issue of significant mutual occlusion among small objects by integrating the Conv2Former module, which enhances the extraction of spatial information features with more precision. Concurrently, we present a Bi-level routing attention technique that is based on cavity convolution. This mechanism regulates the process of extracting and transmitting features at various levels, hence expanding the range of perception. It improves the model's ability to understand and recognize the connections between closely grouped small targets by analyzing individual pixels. This makes the model more resistant to recognizing these targets within complex and changing scenarios. Moreover, it saves crucial contextual details to enhance the precision of differentiation. Finally, the model incorporates multiple dimensions of input data obtained from beneath the UAV by dynamically modulating the size of the convolution kernel. This improves the algorithm's ability to adjust to the specific demands of real-world scenes. The YOLOv7X+ model attains a mean average precision (mAP50) of 60.3% with a validation test Intersection over Union (IoU) criterion of 0.5. The mean average precision (mAP) of the enhanced YOLOv7X model has been increased by 4.9%. and surpasses five other modern sophisticated detection methods. The data from experiments clearly shows that the model effectively handles the identification of small targets in complicated circumstances, achieving a good compromise between accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Abstract Background A complete and hardened endocarp is a typical trait of drupe fruits. However, the ‘Liehe’ (LE) apricot cultivar has a thin, soft, cleavable endocarp that represents 60.39% and 63.76% of the thickness and lignin content, respectively, of the ‘Jinxihong’ (JG) apricot (with normal hardened-endocarp). To understand the molecular mechanisms behind the LE apricot phenotype, comparative transcriptomes of Prunus armeniaca L. were sequenced using Illumina HiSeq™ 2500. Results In this study, we identified 63,170 unigenes including 15,469 genes >1000 bp and 25,356 genes with Gene Function annotation. Pathway enrichment and expression patterns were used to characterize differentially expression genes. The DEGs encoding key enzymes involved in phenylpropanoid biosynthesis were significantly down-regulated in LE apricot. For example, CAD gene expression levels, encoding cinnamyl alcohol dehydrogenase, were only 1.3%, 0.7%, 0.2% and 2.7% in LE apricot compared with JG cultivar at 15, 21, 30, 49 days after full bloom (DAFB). Furthermore, transcription factors regulating secondary wall and lignin biosynthesis were identified. Especially for SECONDARY WALL THICKENING PROMOTING FACTOR 1 (NST 1), its expression levels in LE apricot were merely 2.8% and 9.3% compared with JG cultivar at 15 and 21 DAFB, respectively. Conclusions Our comparative transcriptome analysis was used to understand the molecular mechanisms underlie the endocarp-cleaving phenotype in LE apricot. This new apricot genomic resource and the candidate genes provide a useful reference for further investigating the lignification during development of apricot endocarp. Transcription factors such as NST1 may regulate genes involved in phenylpropanoid pathway and affect development and lignification of the endocarp.

Aiming at the drift problem that the tracking control of the actual load relative to the target load during the electromagnetic excitation biaxial fatigue test of wind turbine blades is easy to drift, a biaxial fatigue testingmachine for electromagnetic excitation is designed, and the following strategy of the actual load and the target load is studied. A Fast Transversal Recursive Least Squares algorithm based on fuzzy logic (Fuzzy FTRLS) is proposed to develop a fatigue loading following dynamic strategy, which adjusts the forgetting factor in the algorithmthrough fuzzy logic to overcome the contradiction between convergence accuracy and convergence speed and solve the phenomenon of amplitude overshoot and phase lag of the actual load relative to the target load. Combined with the previous research results, a simulation model was constructed to verify the strategy's effectiveness. Field tests were carried out to verify its follow-up effect. The results showthat the tracking error of flapwise and edgewise direction iswithin 4%, which has better robustness and dynamic and static performance than the traditional Recursive Least Squares (RLS) algorithm. [ABSTRACT FROM AUTHOR]

In order to shorten fatigue test period of wind turbine blades, a fatigue loading method based on the dual-axes resonance driven by electricity is proposed. The method for detecting the speed and phase of the two loading sources is given, and the synchronization control strategy is formulated. The master-slave network framework mode is applied in the control system. The speed and phase of the two loading sources are measured by using high-speed pulse counting sensor, the blade amplitude is obtained by laser range finder, and the PC monitoring interface is developed by using Labview. Test results show that the speed, phase, blade amplitude of the loading sources and other characteristic parameters can be measured well by using the monitoring system, which provides a new test platform for dual-axis resonance fatigue test of wind turbine blades.

Uniform water-soluble MoS2 quantum dots (WS-MSQDs) were synthesized via a sequential combination of sintering/etching/exfoliation method and solvothermal route. The obtained WS-MSQDs with average size of approximately 3.4 nm exhibited sufficient water solubility and remarkable fluorescence properties. The WS-MSQDs were utilized as a probe for detection of Fe3+ ions with high selectivity and specificity. Furthermore, the WS-MSQDs exhibited high fluorescence stability under different conditions. Finally, the WS-MSQDs were successfully applied for the fluorescence imaging of Fe3+ in living cells, which exhibited practical potential for biomedical applications.

Solid-free drilling fluid is a matter of cardinal significance in the course of Coal bed Methane (CBM) reservoir drilling. This study evaluated the performance of solid-free CBM drilling fluid in central Hunan. Three types of surfactants, namely TX-10 (nonionic), HSB1618 (zwitterionic) and penetrant T (anionic), were added in basic fluid at various concentrations of 0.05, 0.10 and 0.15% (m/m). This study comprised of drilling fluid rheology, sample mineral analysis, sample nuclear magnetic resonance (NMR) scanning, sample wettability, and sample surface micro characteristics tests. The results show that TX-10 and HSB1618 enhance the rheological properties of drilling fluid, such as yield point and gel strength. Penetrant T has opposite effect on it. It was found that the minimum American Petroleum Institute (API) filtration is only 0.3 mL. This study adopted a new method using laser particle size analyzer to evaluate suspension performance. Based on the surface micro characteristics of the sample and the NMR scanning tests, it is found that the residual amount of basic fluid + HSB1618 in the sample is the smallest. The wettability modification curve indicates that three surfactants decrease the sample’s hydrophobicity. With the increase of surfactant concentration, all above parameters change regularly. The basic fluid + 0.10% HSB1618 has the strongest hydrophobicity for sample at pH = 10. This study obtained a set of solid-free drilling fluid system, which provides better suspension capacity and large contact angle and reduces residue of drilling fluid in CBM reservoir. Ultimately, it can accelerate the desorption of coal gas and reduce damage to the reservoir.

Complex labels have been widely used in various industries. The accuracy of its content is critical both in the fields of people's livelihood, such as supermarkets and shopping centers, and in the management of goods in the industrial fields, such as logistics and factories. Inaccurate label information identification can make item management difficult. Because complex labels can simultaneously contain text, icons, bar codes, QR codes, and other information with different aspect ratios. Traditional methods like feature extraction and template matching have problems, such as detection frames breaking between Chinese, English, and numeric symbols. As a result, entire lines of text on complex labels cannot be detectable, resulting in low detection accuracy. In this paper, a deep learning-based text detection algorithm was proposed. By employing the operation of inverse convolution, improved the object detection algorithm you only look once 5 (YOLOv5). In the backbone part of the original model, involution is used instead of the convolution layer to improve target classification and prediction. The original anchor frame was modified using k-means clustering to make it more applicable to text of various sizes in labels. The enhanced algorithm is called as Invo-YOLOv5. Experiments show that this model can significantly improve detection efficiency while also addresse the problems of false detection and missed detection. Finally, the detected text is verified by using CRNN and Tesseract OCR with complex labels as samples for recognition. Both methods can be effectively recognized, demonstrating the efficacy and generality of the Invo-YOLOv5 method in the process of complex labels text detection and improving the detection accuracy. [ABSTRACT FROM AUTHOR]