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Two-dimensional (2D) ferroelectrics usually exhibit instability or a tendency toward degradation when exposed to the ambient atmosphere, and the mechanism behind this phenomenon remains unclear. To unravel this affection mechanism, we have undertaken an investigation utilizing NH3 and two-dimensional ferroelectric SnS. Herein, the adsorption and desorption of NH3 molecules can reversibly modulate the electrical properties of SnS, encompassing I–V curves and transfer curves. The response time for NH3 adsorption is approximately 1.12 s, which is much quicker than that observed in other two-dimensional materials. KPFM characterizations indicate that air molecules’ adsorption alters the surface potentials of SiO2, SnS, metal electrodes, and contacts with minimal impact on the electrode contact surface potential. Upon the adsorption of NH3 molecules or air molecules, the hole concentration within the device decreases. These findings elucidate the adsorption mechanism of NH3 molecules on SnS, potentially fostering the advancement of rapid gas sensing applications utilizing two-dimensional ferroelectrics.

Pectin, a heteropolysaccharide derived from plant cell walls, is essential in the food, pharmaceutical, and environmental industries. Currently, citrus and apple peels are the primary sources for commercial pectin production. The yield and quality of pectin extracted from various plant sources significantly differ based on the extraction methods employed, which include physical, chemical, and biological processes. The complex structures of pectin, composed of polygalacturonic acid and rhamnogalacturonan, influence its physicochemical properties and, consequently, its functionality. As a common polysaccharide, pectin finds applications across multiple sectors. In the food industry, it acts as a gelling agent and a packaging material; in pharmaceuticals, it is utilized for drug delivery and wound healing. Environmentally, pectin contributes to wastewater treatment by adsorbing pollutants. Current research focuses on alternative sources, sustainable extraction methods, and multifunctional applications of pectin. Ongoing studies aim to enhance extraction technologies and broaden the applications of pectin, thereby supporting sustainable development goals.

This study was conducted to investigate the effects of different levels of Green Tea Powder on the performance, egg quality, serum immune and antioxidant indices, and cecal microflora of 300-day-old Chishui black-bone chickens during the peak laying period. A total of 360 Chishui black-bone chickens were selected as the experimental animals. They were randomly allocated into four groups: the control group (CON), trial group I (T1), trial group II (T2), and trial group III (T3), each group with six replicates and 15 hens in each replicate. The control group was fed a basal diet, and the experimental groups were fed a basal diet supplemented with 0.8%, 1.6%, and 2.4% Green Tea Powder, respectively. The accommodation period was 14 d, and the experimental period was 60 d. The statistical software SPSS was used to perform a one-way analysis of variance (ANOVA) on the experimental data, and Duncan’s method was used to perform multiple comparisons among groups. The results showed the following: compared with those of the control group, the average daily gain of the laying hens significantly decreased in the 1.6% Green Tea Powder group (p < 0.05); adding Green Tea Powder significantly reduced the content of malondialdehyde in the serum (p < 0.05), and the addition of 0.8% tea leaves significantly increased the immunoglobulin M and immunoglobulin A contents (p < 0.05); the egg yolk weight, eggshell thickness, eggshell strength, and yolk color of the laying hens significantly decreased in the 1.6% Green Tea Powder group (p < 0.05), and the addition of Green Tea Powder at the level of 2.4% significantly increased the percentage of umami, essential, and total amino acids (p < 0.05); and the structure of intestinal microorganisms was improved, and the abundance of Bacteroidetes and Bacteroidaceae significantly increased, while the abundance of Firmicutes and Lachnospiraceae significantly decreased (p < 0.05).

Lactic acid bacteria fermentation is a beneficial bioprocessing method that can improve the flavor, transform nutrients, and maintain the biological activity of foods. The aim of this study is to investigate the effects of Lactiplantibacillus plantarum dy-1 fermentation on the nutritional components, flavor and taste properties, and composition of saponin compounds and their hypolipidemic and antioxidant activities. The results suggested that the total polyphenol content increased, and the soluble polysaccharides and total saponin contents decreased in fermented bitter melon juice (FJ) compared with those in non-fermented bitter melon juice (NFJ). The determination of volatile flavor substances by GC-MS revealed that the response values of acetic acid, n-octanol, sedumol, etc., augmented significantly, and taste analysis with an electronic tongue demonstrated lower bitterness and higher acidity in FJ. Furthermore, UPLC-Q-TOF-MS/MS testing showed a significant decrease in bitter compounds, including momordicines I and II, and a significant increase in the active saponin momordicine U in the fermented bitter melon saponin group (FJBMS). The in vitro assays indicated that FJBMS exhibited similar antioxidant activities as the non-fermented bitter melon saponin group (NFBMS). The in vitro results show that both NFBMS and FJBMS, when used at 50 μg/mL, could significantly reduce fat accumulation and the malondialdehyde (MDA) content and increased the catalase (CAT) activity, while there was no significant difference in the bioactivities of NFBMS and FJBMS. In conclusion, Lactiplantibacillus plantarum dy-1 fermentation is an effective means to lower the bitterness value of bitter melon and preserve the well-known bioactivities of its raw materials, which can improve the edibility of bitter melon.

Enhancing the sensory appeal of jasmine instant tea, particularly its aroma, poses a significant challenge due to the loss of volatile organic compounds during conventional processing. This study introduces a novel approach to address this issue through the application of microencapsulation techniques, aimed at preserving these key aromatic elements. Our investigation focused on the encapsulating agents gelatin, acacia gum, carboxymethylcellulose (CMC), and maltodextrin, chosen for their compatibility with the volatile organic compounds of tea. A statistical analysis was conducted on the analytical results through comprehensive analytical techniques like Principal Component Analysis (PCA), Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA), and Variable Importance in Projection (VIP) analysis for microcapsule characterization. The statistical analysis revealed gelatin to be a particularly effective encapsulating medium, preserving an aroma profile more akin to fresh tea. The statistical analysis confirmed the reliability of these findings, highlighting the potential of microencapsulation in refining the quality of jasmine instant tea products. The results of this research suggest that microencapsulation could be instrumental in improving the sensory quality and shelf life of instant tea products, offering new opportunities for product enhancement in the beverage industry.

The high-quality development of the Yellow River Basin is still facing the issue of imbalance and inadequacy, and it urgently requires the backing and assistance of a well-coordinated and efficient comprehensive transportation network system. Based on this purpose, this study uses a composite system synergy degree model incorporating “mode dimension + regional dimension” to analyze the evolution of this network. The results indicate the following: (1) Railway and air transportation surpass highway transportation in growth and synergy, though overall system synergy is still low. (2) Downstream areas are gradually taking the lead, and the orderliness of each subsystem is gradually becoming consistent. The complexity and variability of the composite system’s synergy are high, with an overall low level of synergy. Overall, despite improvements in service capacity and quality, better integration of different transport modes and enhanced cross-regional cooperation are needed. This study reveals and analyzes in depth the complexity of the comprehensive transportation network system in the Yellow River Basin and its spatiotemporal characteristics of synergistic evolution. We propose suggestions for high-quality development at national, regional, and industry levels, fostering the continuous improvement of the system structure, which aids in overcoming the tangible obstacles faced by the high-quality development of the Yellow River Basin.

The 1.55 μm TECSL has excellent characteristics such as wide tuning, narrow linewidth, high SMSR, and high output power and has a wide range of applications in optical communications, spectral sensing, gas detection, atomic physics, and biomedicine. For the TECSL, the choice of collimating lens is very significant. In order to obtain a wider tuning range, five structures are constructed in this paper to investigate the effect of lens focal length on the output characteristics of 1.55 μm TECSL. It is shown that when the lens focal length is 4.51 mm, the minimum threshold current is 52 mA, the maximum output power is 42.36 mW, the maximum SMSR is 62.15 dB, the narrowest linewidth is 0.26 nm, and 152.3 nm (1458.2~1610.5 nm) can be tuned continuously. It is shown that different lens focal lengths affect the output characteristics of the TECSL, and the performance of the TECSL can be improved by appropriately changing the lens focal length.

Overuse of chemical nitrogen (N) fertilizers leads to N leaching and soil degradation. Replacing chemical N fertilizers with organic fertilizers can enhance soil nutrition, reduce N loss, and improve soil productivity. However, the effects of combining organic and chemical fertilizers on soil N components and N transformation remain unclear. A 12-year field study included four treatments: no fertilizer (CK), chemical fertilizer alone (CF), 50% chemical N fertilizer combined with co-composted organic fertilizer (CFM), and composted (CFMC) organic fertilizer. The results showed that CFM and CFMC significantly enhanced SOC, TN, LFON, DON, NH4+-N, and MIN levels compared to CF. The CFM and CFMC treatments enhanced the soil N supply capacity and N pool stability by increasing the N mineralization potential (N0) and decreasing the N0/TN ratio. The CFM and CFMC treatments decreased net N ammonification rates by 108.03%–139.83% and 0.44%–64.91% and net mineralization rates by 60.60%–66.30% and 1.74%–30.38%, respectively. Changes in N transformation have been attributed to increased soil pH, enzyme activity, and substrate availability. These findings suggest that partial organic fertilizer substitution, particularly with co-composted organic fertilizers, is a viable strategy for enhancing soil fertility, improving soil N supply and stability, and reducing N loss in rubber plantations.

Color characteristics are a crucial indicator of green tea quality, particularly in needle-shaped green tea, and are predominantly evaluated through subjective sensory analysis. Thus, the necessity arises for an objective, precise, and efficient assessment methodology. In this study, 885 images from 157 samples, obtained through computer vision technology, were used to predict sensory evaluation results based on the color features of the images. Three machine learning methods, Random Forest (RF), Support Vector Machine (SVM) and Decision Tree-based AdaBoost (DT-AdaBoost), were carried out to construct the color quality evaluation model. Notably, the DT-Adaboost model shows significant potential for application in evaluating tea quality, with a correct discrimination rate (CDR) of 98.50% and a relative percent deviation (RPD) of 14.827 in the 266 samples used to verify the accuracy of the model. This result indicates that the integration of computer vision with machine learning models presents an effective approach for assessing the color quality of needle-shaped green tea.

China’s vegetation ecosystem has undergone profound changes, and there is an urgent need to explore the mechanisms behind vegetation changes in different ecological sub-regions and historical periods across China. Based on NDVI (normalized difference vegetation index) data, this study analyzed the spatial and temporal evolution patterns and driving mechanisms of six ecological sub-regions in China. The results showed that: (1) over the past 20 years, the vegetation coverage in mainland China showed a decreasing trend from east to west. (2) Over the past 20 years, the vegetation coverage of the six ecological sub-regions showed an increasing trend, with the highest increase in Central Southern China (0.0039) and the lowest increase in East China (0.002). (3) The gravity center of Northeast China showed a trend of migration to the northwest. The gravity center of North China, East China, and Central South China showed a trend of migration to the southwest, while that of Northwest and Southwest China showed a trend of migration to the southeast. (4) During the period from 2000 to 2020, vegetation cover levels showed an upward trend. (5) The lag time of vegetation types in different regions was different. (6) Precipitation was the dominant influencing factor in the evolution of vegetation in Northeast China, North China, Northwest China, and Southwest China. The dominant influencing factors of vegetation evolution in East China were land use and GDP (gross domestic product), while the dominant influencing factors of vegetation evolution in Central South China were precipitation and land use.

The influence of epigallocatechin gallate (EGCG) on the physicochemical–rheological properties of silver carp surimi gel was investigated. The gel strength, texture, water-holding capacity (WHC), dynamic distribution of water, and rheological properties of surimi gels added with different levels (0, 0.02, 0.04, 0.06, 0.08, and 0.1%) of EGCG were measured. The results showed that with the increase of EGCG content, the gel strength, hardness, WHC, and immobilized water contents of surimi gels showed a trend of first increasing and then decreasing, and EGCG 0.02% and EGCG 0.04% showed better gel performance as compared with the control. EGCG 0.02% had the highest gel strength (406.62 g·cm), hardness (356.67 g), WHC (64.37%), and immobilized water contents (98.958%). The gel performance decreased significantly when the amounts of EGCG were higher than 0.06%. The viscosity, G′, and G″ of the rheological properties also showed the same trends. The chemical interaction of surimi gels, secondary structure of myofibrillar protein (MP), and molecular docking results of EGCG and silver carp myosin showed that EGCG mainly affected the structure and aggregation behavior of silver carp myosin through non-covalent interactions such as those of hydrogen bonds, hydrophobic interactions, and electrostatic interactions. The microstructures of EGCG 0.02% and EGCG 0.04% were compact and homogeneous, and had better gel formation ability. The lower concentrations of EGCG formed a large number of chemical interactions such as those of disulfide bonds and hydrophobic interactions inside the surimi gels by proper cross-linking with MP, and also increased the ordered β-sheet structure of MP, which facilitated the formation of the compact three-dimensional network gel.

Henckelia longisepala (H. W. Li) D. J. Middleton & Mich. Möller is a rare and endangered plant species found only in Southeastern Yunnan, China, and Northern Vietnam. It is listed as a threatened species in China and recognized as a plant species with extremely small populations (PSESP), while also having high ornamental value and utilization potential. This study used ddRAD-seq technology to quantify genetic diversity and structure for 32 samples from three extant populations of H. longisepala. The H. longisepala populations were found to have low levels of genetic diversity (Ho = 0.1216, He = 0.1302, Pi = 0.1731, FIS = 0.1456), with greater genetic differentiation observed among populations (FST = 0.3225). As indicated by genetic structure and phylogenetic analyses, samples clustered into three distinct genetic groups that corresponded to geographically separate populations. MaxEnt modeling was used to identify suitable areas for H. longisepala across three time periods and two climate scenarios (SSP1-2.6, SSP5-8.5). High-suitability areas were identified in Southeastern Yunnan Province, Northern Vietnam, and Eastern Laos. Future H. longisepala distribution was predicted to remain centered in these areas, but with a decrease in the total amount of suitable habitat. The present study provides key data on H. longisepala genetic diversity, as well as a theoretical basis for the conservation, development, and utilization of its germplasm resources.

The accurate identification of different restoration stages of degraded alpine meadow patches is essential to effectively curb the deterioration trend of ‘Heitutan’ (areas of severely degraded alpine meadows in western China). In this study, hyperspectral imaging (HSI) and machine learning techniques were used to develop a method for accurately distinguishing the different restoration stages of alpine meadow patches. First, hyperspectral images representing the four restoration stages of degraded alpine meadow patches were collected, and spectral reflectance, vegetation indexes (VIs), color features (CFs), and texture features (TFs) were extracted. Secondly, valid features were selected by competitive adaptive reweighted sampling (CARS), ReliefF, recursive feature elimination (RFE), and F-test algorithms. Finally, four machine learning models, including the support vector machine (SVM), k-nearest neighbor (KNN), random forest (RF), and extreme gradient boosting (XGBoost), were constructed. The results demonstrated that the SVM model based on the optimal wavelengths (OWs) and prominent VIs achieved the best value of accuracy (0.9320), precision (0.9369), recall (0.9308), and F1 score (0.9299). In addition, the models that combine multiple sets of preferred features showed a significant performance improvement over the models that relied only on a single set of preferred features. Overall, the method combined with HSI and machine learning technology showed excellent reliability and effectiveness in identifying the restoration stages of meadow patches, and provided an effective reference for the formulation of grassland degradation management measures.

Asphalt concrete impervious facings, widely adopted as the impervious structures for rockfill dams and upper reservoirs in pumped storage power stations, typically have a multilayer structure with a thin sealing layer, a thick impervious layer, and a thick leveling bonding layer. The properties of the interfaces between these layers are crucial for the overall performance of the facings. This paper develops a model to investigate the complex interface damage behavior of the facing under static water pressure and gravity. The model considers two damage origins: one is the interface adhesion–decohesion damage, which is described by the cohesive zone model (CZM) combined with the Weibull-type random interface strength distribution, and the other is the bulk damage of each layer, described by Mazars’ model. Primarily, a comparison between numerical simulation and indoor direct shear tests validates the reliability of the CZM for the asphalt concrete layer interface. Then, the damage distribution of the two interfaces is simulated, and the characteristics of the interface stress are analyzed in detail. The interface shear stresses of the ogee sections, which have different curvatures, all show an interesting oscillation between the thin sealing layer and the impervious layer, and the interface damage at this interface exhibits high heterogeneity. Furthermore, tension stress exists in the local zones of the ogee section, and the damage in this section is significantly greater than in other parts of the facings.

Due to the molding-free property and dry shrinkage of extrusion-based three-dimensional printable concrete (3DPC), the precision issues of 3DPC have not been solved effectively. One of the viable solutions for 3DPC precision improvement is to print using ultra-thin filaments. The challenges of ultra-thin-filament printing are extrudability, flowability, and fast solidification. To overcome these challenges and enhance precision, a customized 3D concrete printer with an ultra-thin diameter nozzle (6 mm) and fully sealed extrusion system was developed, and the mix design of ultra-thin-filament 3DPC (UTF-3DPC) was studied, including ingredients such as fly ash (FA), silica fume (SF), ordinary Portland cement (OPC), sodium dodecyl sulfate and cellulose (SDSC), water reducer, water, and sand. The function of UTF-3DPCs flowability and fast solidification with the proportion of water and SDSC was explored to obtain the optimal mix design. The standard compressive and flexural strengths of UTF-3DPC specimens were compared with the mold-cast vibrated and the mold-cast non-vibrated concrete. Their meso-scale and micro-scale structures were analyzed to expose the strength mechanism, according to the scanning electron microscope (SEM) images. A suitable mix design of UTF-3DPC was obtained and UTF-3DPC strength reached 80% of standard mold-cast concrete. The findings reported here provide a pathway to improve the precision of 3DPC and extend the application of 3D printing technology in engineering.

Sex determination is remarkably diverse, with frequent transitions between sex chromosomes, in amphibians. Under these transitions, some chromosomes are more likely to be recurrently co-opted as sex chromosomes, as they are often observed across deeply divergent taxa. However, little is known about the pattern of sex chromosome evolution among closely related groups. Here, we examined sex chromosome and sex determination in two spiny frogs, Nanorana quadranus and Quasipaa yei. We conducted an analysis of genotyping-by-sequencing (GBS) data from a total of 34 individuals to identify sex-specific makers, with the results verified by PCR. The results suggest that chromosome 1 is a homologous sex chromosome with an XY pattern in both species. This chromosome has been evolutionarily conserved across these closely related groups within a period of time. The DMRT1 gene is proposed to be implicated in homology across two distantly related spiny frog species as a putative candidate sex-determining gene. Harboring the DMRT1 gene, chromosome 1 would have been independently co-opted for sex determination in deeply divergent groups of anurans.

Pine wilt disease (PWD) is a forest disease characterized by rapid spread and extremely high lethality, posing a serious threat to the ecological security of China’s forests and causing significant economic losses in forestry. Given the extensive forestry area, limited personnel for inspection and monitoring, and high costs, utilizing UAV-based remote sensing monitoring for diseased trees represents an effective approach for controlling the spread of PWD. However, due to the small target size and uneven scale of pine wilt disease, as well as the limitations of real-time detection by drones, traditional disease tree detection algorithms based on RGB remote sensing images do not achieve an optimal balance among accuracy, detection speed, and model complexity due to real-time detection limitations. Consequently, this paper proposes Light-ViTeYOLO, a lightweight pine wilt disease detection method based on Vision Transformer-enhanced YOLO (You Only Look Once). A novel lightweight multi-scale attention module is introduced to construct an EfficientViT feature extraction network for global receptive field and multi-scale learning. A novel neck network, CACSNet(Content-Aware Cross-Scale bidirectional fusion neck network), is designed to enhance the detection of diseased trees at single granularity, and the loss function is optimized to improve localization accuracy. The algorithm effectively reduces the number of parameters and giga floating-point operations per second (GFLOPs) of the detection model while enhancing overall detection performance. Experimental results demonstrate that compared with other baseline algorithms, Light-ViTeYOLO proposed in this paper has the least parameter and computational complexity among related algorithms, with 3.89 MFLOPs and 7.4 GFLOPs, respectively. The FPS rate is 57.9 (frames/s), which is better than the original YOLOv5. Meanwhile, its mAP@0.5:0.95 is the best among the baseline algorithms, and the recall and mAP@0.5 slightly decrease. Our Light-ViTeYOLO is the first lightweight method specifically designed for detecting pine wilt disease. It not only meets the requirements for real-time detection of pine wilt disease outbreaks but also provides strong technical support for automated forestry work.

Diabetes is a common disease that seriously endangers human health. Continuous glucose monitoring (CGM) is important for the prevention and treatment of diabetes. Glucose-sensing photonic nanochains (PNCs) have the advantages of naked-eye colorimetric readouts, short response time and noninvasive detection of diabetes, showing immense potential in CGM systems. However, the developed PNCs cannot disperse in physiological environment at the pH of 7.4 because of their poor hydrophilicity. In this study, we report a new kind of PNCs that can continuously and reversibly detect the concentration of glucose (Cg) in physiological environment at the pH of 7.4. Polyacrylic acid (PAA) added to the preparation of PNCs forms hydrogen bonds with polyvinylpyrrolidone (PVP) in Fe3O4@PVP colloidal nanoparticles and the hydrophilic monomer N-2-hydroxyethyl acrylamide (HEAAm), which increases the content of PHEAAm in the polymer shell of prepared PNCs. Moreover, 4-(2-acrylamidoethylcarbamoyl)-3-fluorophenylboronic acid (AFPBA), with a relatively low pKa value, is used as the glucose-sensing monomer to further improve the hydrophilicity and glucose-sensing performances of PNCs. The obtained Fe3O4@(PVP-PAA)@poly(AFPBA-co-HEAAm) PNCs disperse in artificial serum and change color from yellow-green to red when Cg increases from 3.9 mM to 11.4 mM, showing application potential for straightforward CGM.

In order to improve the performance of self-driving commercial vehicles for half-hill starting, a ramp control strategy based on the back-slip speed corresponding to the parking moment is proposed. Firstly, the longitudinal dynamics model of the vehicle is established, the force of the vehicle on the ramp is analyzed, and the rear slip speed of the vehicle is matched with the parking moment, and finally the target speed is tracked based on the sliding-mode controller, and in order to validate the validity of the method, two comparative algorithms of the pure PI controller and the proportional gain controller based on the back-sliding speed corresponding to the resting moment are designed for comparative experiments, and the data results show that the control strategy based on the resting moment corresponding to the backsliding speed of the sliding mode ramp start control strategy can stably complete the ramp start under different weights and different slopes, and greatly reduce the backsliding distance of the vehicle.

Responsive photonic crystal hydrogel sensors are renowned for their colorimetric sensing ability and can be utilized in many fields such as medical diagnosis, environmental detection, food safety, and industrial production. Previously, our group invented responsive photonic nanochains (RPNCs), which improve the response speed of photonic crystal hydrogel sensors by at least 2 to 3 orders of magnitude. However, RPNCs are dispersed in a liquid medium, which needs a magnetic field to orient them for the generation of structural colors. In addition, during repeated use, the process of cleaning and redispersing can cause entanglement, breakage, and a loss of RPNCs, resulting in poor stability. Moreover, when mixing with the samples in liquid, the RPNCs may lead to the contamination of the samples being tested. In this paper, we incorporate one-dimensional oriented RPNCs with agarose gel film to prepare heterogeneous hydrogel films. Thanks to the non-responsive and porous nature of the agarose gel, the protons diffuse freely in the gel, which facilitates the fast response of the RPNCs. Furthermore, the “frozen” RPNCs in agarose gel not only enable the display of structural colors without the need for a magnet but also improve the cycling stability and long-term durability of the sensor, and will not contaminate the samples. This work paves the way for the application of photonic crystal sensors.

Lammers and Stapel reported that high power increases deontological (rule-based) moral thinking, and low power increases utilitarian (outcome-based) moral thinking. However, the dilemmas were mild and did not involve harm to life. Here, we examined whether the presence or absence of harm to life affects the moral decisions of powerholders. To help establish the replicability and validity of the effects of power on moral judgments in the absence of harm to life, we first performed an exact replication of a study conducted by Lammers and Stapel, and this experiment was followed up by a similar study in an organizational context in China (Studies 1 and 2). Studies 3 and 4 investigated whether power and the presence/absence of harm to life interacted with preferences for deontological versus utilitarian moral judgments. Power consistently triggered deontological thinking. However, power differences in moral reasoning only emerged when there was no harm to life. Harm prompted deontological responses among control and powerless individuals, which nullified differences across the power conditions. The findings demarcate the generalizability of the association between power and a moral thinking style.

Crop yield estimation plays a crucial role in agricultural production planning and risk management. Utilizing simultaneous localization and mapping (SLAM) technology for the three-dimensional reconstruction of crops allows for an intuitive understanding of their growth status and facilitates yield estimation. Therefore, this paper proposes a VINS-RGBD system incorporating a semantic segmentation module to enrich the information representation of a 3D reconstruction map. Additionally, image matching using L_SuperPoint feature points is employed to achieve higher localization accuracy and obtain better map quality. Moreover, Voxblox is proposed for storing and representing the maps, which facilitates the storage of large-scale maps. Furthermore, yield estimation is conducted using conditional filtering and RANSAC spherical fitting. The results show that the proposed system achieves an average relative error of 10.87% in yield estimation. The semantic segmentation accuracy of the system reaches 73.2% mIoU, and it can save an average of 96.91% memory for point cloud map storage. Localization accuracy tests on public datasets demonstrate that, compared to Shi–Tomasi corner points, using L_SuperPoint feature points reduces the average ATE by 1.933 and the average RPE by 0.042. Through field experiments and evaluations in a strawberry field, the proposed system demonstrates reliability in yield estimation, providing guidance and support for agricultural production planning and risk management.

Obtaining accurate and real-time spatial distribution information regarding crops is critical for enabling effective smart agricultural management. In this study, innovative decision fusion strategies, including Enhanced Overall Accuracy Index (E-OAI) voting and the Overall Accuracy Index-based Majority Voting (OAI-MV), were introduced to optimize the use of diverse remote sensing data and various classifiers, thereby improving the accuracy of crop/vegetation identification. These strategies were utilized to integrate crop/vegetation classification outcomes from distinct feature sets (including Gaofen-6 reflectance, Sentinel-2 time series of vegetation indices, Sentinel-2 time series of biophysical variables, Sentinel-1 time series of backscatter coefficients, and their combinations) using distinct classifiers (Random Forests (RFs), Support Vector Machines (SVMs), Maximum Likelihood (ML), and U-Net), taking two grain-producing areas (Site #1 and Site #2) in Haixi Prefecture, Qinghai Province, China, as the research area. The results indicate that employing U-Net on feature-combined sets yielded the highest overall accuracy (OA) of 81.23% and 91.49% for Site #1 and Site #2, respectively, in the single classifier experiments. The E-OAI strategy, compared to the original OAI strategy, boosted the OA by 0.17% to 6.28%. Furthermore, the OAI-MV strategy achieved the highest OA of 86.02% and 95.67% for the respective study sites. This study highlights the distinct strengths of various remote sensing features and classifiers in discerning different crop and vegetation types. Additionally, the proposed OAI-MV and E-OAI strategies effectively harness the benefits of diverse classifiers and multisource remote sensing features, significantly enhancing the accuracy of crop/vegetation classification.

In this article, we address the problem of synchronizing multiple analog-to-digital converter (ADC) and digital-to-analog converter (DAC) chains in a multi-channel system, which is constrained by the sampling frequency and inconsistencies among the components during system integration. To evaluate and compensate for the synchronization differences, we propose a pulse compression shape-based algorithm to measure the entire delay parameter of the ADC/DAC chain, which achieves sub-sampling resolution by mapping the shape of the discrete pulse compression peak to the signal propagation delay. Moreover, owing to the matched filtering in the pulse compression process, the algorithm exhibits good noise performance and is suitable for wireless scenarios. Experiments verified that the algorithm can achieve precise measurements with sub-sampling resolution in scenarios where the signal-to-noise ratio (SNR) is greater than −10 dB.

The brain often switches freely between focused attention and divergent thinking, and the Default Mode Network (DMN) is activated during brain rest. Since its discovery, the DMN, together with its function and characteristics, indicates that learning does not stop when the brain “rests”. Therefore, DMN plays an important role in learning. Neural activities such as beta wave rhythm regulation, “subconscious” divergence thinking mode initiation, hippocampal function, and neural replay occur during default mode, all of which explains that “rest” promotes learning. This paper summarized the function and neural mechanism of DMN in learning and proposed that the DMN plays an essential role in learning, which is that it enables rest to promote learning.

The diagenetic evolution of sandstone is very complicated under the conditions of high temperatures and pressures in deep-water, deep-buried regimes, which have great influence on reservoir quality. This study investigates the typical reservoir target of Neogene deep-water, submarine-fan sandstones under high-temperature, high-pressure regimes in the Qiongdongnan Basin, South China Sea. Utilizing a thin section, scanning electron microscope (SEM), mineral geochemistry combined with burial history evolution, complex diagenetic events, and main controlling factors of the sandstone in the Neogene Meishan Formation were determined. The results show that the evolution of sandstone reservoirs is initially controlled by depositional framework compositions and subsequently modified by eogenetic and mesogenetic alterations during progressive burial. Eogenetic alterations mainly include the following: (1) mechanical compaction; (2) dissolution of feldspar; (3) low-Fe calcite cementation. Mesogenetic events were identified as the following: (1) dissolution of feldspar; (2) ferroan calcite and ankerite formation; (3) precipitation of quartz and clay mineral. Mechanical compaction is greatly influenced by the original depositional framework composition, and sandstone samples enriched in high contents of detrital clay matrix always experienced extensive mechanical compaction. Different phases of carbonate cement during different diagenetic regimes lead to continuous destruction on reservoir porosity. The dissolution of unstable feldspar minerals during eogenetic and mesogenetic environments leads to the development of secondary porosities and would enhance the quality of the reservoir. Overpressure formation is pervasively developed owing to early disequilibrium compaction and subsequent natural gas charging. Only well-sorted sandstones with low contents of detrital clay matrix could resist early mechanical compaction, lead to ample residual original porosities, and then undergo extensive mineral dissolution to generate sufficient secondary porosities. Subsequently, these porosities would be effectively protected by overpressure formation. Poor-sorted sandstones with high contents of detrital clay matrix would experience strong mechanical compaction and extensive destruction of original porosities. Thus, these sandstones are difficult to have significant dissolution and are unable to be effectively protected by overpressure formation. Therefore, the interplay between the original framework composition and the corresponding diagenetic pathways coupled with overpressure formation would result in strong reservoir heterogeneity for the deep-buried sandstones during progressive burial.

Flying cars offer huge advantages due to their deformable structure, which can adapt to external environments and mission requirements. They represent a novel system that can realize vertical takeoff and landing. However, the structure of a flying car is complicated, placing higher requirements on modeling accuracy and control effectiveness. Thus, in this paper, a dynamic model of a flying car is proposed by combining a car body, motor, and propellers. Then, a double-loop controller based on active disturbance rejection control is proposed to accurately control its flight altitude. Utilizing the extended state observer, external wind and other disturbances are regarded as an extended state, which can be dynamically observed and compensated to significantly improve tracking accuracy. The effectiveness of the proposed controller is validated through detailed simulations and flight experiments. The proposed controller significantly improves control accuracy and disturbance rejection capability.

Resonators are passive time-invariant components that do not produce a frequency shift. However, they respond to an excitation signal close to resonance with an oscillation at their natural frequencies with exponentially decreasing amplitudes. If resonators are connected to antennas, they form purely passive sensors that can be read remotely. In this work, we model the external excitation of a resonator with different excitation signals and its subsequent decay characteristics analytically as well as numerically. The analytical modeling explains the properties of the resonator during transient response and decay behavior. The analytical modeling clarifies how natural oscillations are generated in a linear time-invariant system, even if their spectrum was not included in the stimulation spectrum. In addition, it enables the readout signals to be optimized in terms of duration and bandwidth.

Integrated photonic chips leverage the recent developments in integrated circuit technology, along with the control and manipulation of light signals, to realize the integration of multiple optical components onto a single chip. By exploiting the power of light, integrated photonic chips offer numerous advantages over traditional optical and electronic systems, including miniaturization, high-speed data processing and improved energy efficiency. In this review, we survey the current status of quantum computation, optical neural networks and the realization of some algorithms on integrated optical chips.

The circadian clock refers to the formation of a certain rule in the long-term evolution of an organism, which is an invisible ‘clock’ in the body of an organism. As one of the largest TF families in higher plants, the MYB transcription factor is involved in plant growth and development. MYB is also inextricably correlated with the circadian rhythm. In this study, the transcriptome data of the tea plant ‘Baiyeyihao’ were measured at a photoperiod interval of 4 h (24 h). A total of 25,306 unigenes were obtained, including 14,615 unigenes that were annotated across 20 functional categories within the GO classification. Additionally, 10,443 single-gene clusters were annotated to 11 sublevels of metabolic pathways using KEGG. Based on the results of gene annotation and differential gene transcript analysis, 22 genes encoding MYB transcription factors were identified. The G10 group in the phylogenetic tree had 13 members, of which 5 were related to the circadian rhythm, accounting for 39%. The G1, G2, G8, G9, G15, G16, G18, G19, G20, G21 and G23 groups had no members associated with the circadian rhythm. Among the 22 differentially expressed MYB transcription factors, 3 members of LHY, RVE1 and RVE8 were core circadian rhythm genes belonging to the G10, G12 and G10 groups, respectively. Real-time fluorescence quantitative PCR was used to detect and validate the expression of the gene transcripts encoding MYB transcription factors associated with the circadian rhythm.

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The eastern extension of the Tibetan Plateau (EETP) has complex terrain, unique climate characteristics, and significant regional differences. Based on the hourly precipitation data from 444 stations over the EETP, the characteristics of the extreme precipitation event (EPE) diurnal cycle over the EETP and their regional differences during the warm season (May–September) have been indicated and revealed in this study. The mean duration of EPEs at most stations over the EETP is over 6 h, except for some stations in the eastern part of the Tibetan Plateau and Yunnan province. In addition to the Qinba Mountain area, EPEs developed rapidly in most stations. EPEs with long (short) durations usually start at night (afternoon). But in the southwestern part of the Sichuan Basin (eastern part of the Tibetan Plateau), long-duration EPEs and short-duration EPEs often start at night (afternoon to early night). Meanwhile, the long-duration EPEs lead to the nocturnal diurnal peaks and eastward propagating features of extreme precipitation amount (EPA) over the EETP. In the Sichuan Basin (the eastern part of the Tibetan Plateau), the onset and peak moments of total EPEs show a single diurnal peak and appear at midnight (late afternoon to early night). The onset and peak moments of EPEs in the Yunnan–Guizhou Plateau and the Qinba Mountain area exhibit two diurnal peaks, one at midnight and the other from afternoon to early night. Over the EETP, for the long-duration EPEs, the peak moments are often delayed by 2–3 h compared to the start moment, while for the short-duration EPEs, the peak moment and the start moment almost coincide.

The use of eco-friendly biomass as a resource is an efficient way to address the problems of fossil fuel depletion and climate change. In biomass conversion, versatile γ-valerolactone (GVL) is generally obtained from levulinic acid (LA) hydrogenation via a multimetallic catalyst system. Despite conversion efficiency being enhanced in mild conditions due to metal interactions, maintaining high catalyst stability is still a challenge. In this study, we synthesized a surrounded Co0.52Ni0.48@Al2O3-IE catalyst that exhibited excellent alloying and synergistic interaction between the metal constituents. Under relatively mild reaction conditions, the GVL yield over the catalyst exceeded 99% in LA hydrogenation. The catalyst showed no deactivation in a test of five cycles, displaying superiority in stability, possibly due to reasons of the physical isolation of the shell and the alumina retention on the Co-Ni alloys surface caused by the reversibility of exchange equilibrium. The present work demonstrated that a surrounded structured catalyst fabricated by ion exchange (IE) with active metals physically enclosed can lead to high catalytic activity and superior stability.

In the Qinghai-Tibet Plateau region, operational deficiencies and limited maintenance capacities often impair automatic air quality monitoring stations. This results in frequent data omissions, compromising the reliability of environmental assessment data. Therefore, an effective data imputation method is required to address the gaps in observational records. Utilizing a Sequence-to-Sequence framework, we introduce a model termed Bidirectional Recurrent Imputation for Time Series-Attention-based Long Short-Term Memory (BRITS-ALSTM). The encoder of BRITS-ALSTM applies BRITS to integrate single-station historical characteristics with multi-station correlation features. Concurrently, the decoder employs LSTM within an attention mechanism to capitalize on previously observed data, thereby generating hourly imputations for missing air quality data values. The model was trained using six types of air quality data from 16 stations across Qinghai Province. Through localized testing and parameter optimization, BRITS-ALSTM achieved a reduction in mean relative error (MRE) by 74.88% compared to the baseline mean-filling approach. Additionally, ablation studies demonstrated an improvement in the coefficient of determination R-squared (R2) from 0.67 to 0.76, outperforming the standalone BRITS. Consequently, BRITS-ALSTM enhances the accuracy of air quality data evaluations in the Tibetan Plateau and offers an efficacious strategy for data imputation in elevated terrains.

To estimate the progressive collapse resistance capacity of a multi-column frame tube structure with an assembled truss beam composite floor (ATBCF), pushdown analysis and nonlinear dynamic analysis are conducted for such a structure using the alternate load path (ALP) method. The bearing capacities of the remaining structures under three different work conditions, which are the side middle column removal, the edge middle column removal, and the corner column removal, are individually studied, and the collapse mechanism of the remaining structures is analyzed based on the aspects of the internal force redistribution and the failure mode of the second defense line. Simultaneously, the influence of the column failure time on the dynamic response of the remaining structure and the dynamic amplification coefficient is discussed. The results indicate that the residual bearing capacity of the remaining structure following the bottom corner column removal is higher than that of the one following the side or edge middle column removal, while the latter has a stronger plastic deformation capacity. When the ALP method is adopted to operate the progressive collapse analysis, it is reasonable to take the column failure time as 0.1 times the period of the first-order vertical vibration mode of the remaining structure, and it is suitable to set the dynamic amplification coefficient as 2.0, which is the ratio of the maximum dynamic displacement to the static displacement of the remaining structure under the transient loading condition.

Repetitive transcranial magnetic stimulation (rTMS) to the left dorsolateral prefrontal cortex (L-DLPFC) is commonly used for the clinical treatment of major depressive disorder (MDD). The neuroimaging biomarkers and mechanisms of rTMS are still not completely understood. This study aimed to explore the functional neuroimaging changes induced by rTMS in adolescents with MDD. A total of ten sessions of rTMS were administrated to the L-DLPFC in thirteen adolescents with MDD once a day for two weeks. All of them were scanned using resting-state functional magnetic resonance imaging at baseline and after rTMS treatment. The regional homogeneity (ReHo), amplitude of low-frequency fluctuation (ALFF), and the subgenual anterior cingulate cortex (sgACC)-based functional connectivity (FC) were computed as neuroimaging indicators. The correlation between changes in the sgACC-based FC and the improvement in depressive symptoms was also analyzed. After rTMS treatment, ReHo and ALFF were significantly increased in the L-DLPFC, the left medial prefrontal cortex, bilateral medial orbital frontal cortex, and the left ACC. ReHo and ALFF decreased mainly in the left middle occipital gyrus, the right middle cingulate cortex (MCC), bilateral calcarine, the left cuneus, and the left superior occipital gyrus. Furthermore, the FCs between the left sgACC and the L-DLPFC, the right IFGoper, the left MCC, the left precuneus, bilateral post-central gyrus, the left supplementary motor area, and the left superior marginal gyrus were enhanced after rTMS treatment. Moreover, the changes in the left sgACC-left MCC FC were associated with an improvement in depressive symptoms in early improvers. This study showed that rTMS treatment in adolescents with MDD causes changes in brain activities and sgACC-based FC, which may provide basic neural biomarkers for rTMS clinical trials.

A nano-dual-phase powder with ultra-fine grain size was synthesized by the liquid precursor method at 1200 °C. A series of single-phase high-entropy ceramic powders ((Ti, Zr, Hf, Nb)B2, (Ti, Zr, Hf, Nb, Ta)B2, (Ti, Zr, Hf, Nb, Mo)B2, (Ti, Zr, Hf, Nb, Ta, Mo)B2) with high purity (C content less than 0.9 wt% and O content less than 0.7 wt%) and ultrafine (average grain sizes of 340–570 nm) were successfully synthesized at 1800 °C. The sample of (TiZrHfNbTa)B2 exhibited a hexagonal close-packed (HCP) structure, and the metal elements were uniformly distributed at the nanoscale, microscale, and macroscale. This method did not apply to the preparation of all high-entropy ceramic powders and was unfavorable for the formation of single-phase high-entropy borides when the size difference factor exceeded 3.9%. The present work provides a guide for the development of ceramic-based composites through precursor impregnation pyrolysis.

The 1.55 μm waveband integrated external cavity tunable diode lasers have excellent merits such as their small volume, low cost, low power consumption, wide tuning range, narrow linewidth, large side mode suppression ratio, and high output power. These merits have attracted many applications for the lasers, such as in wavelength division multiplexing, passive optical networks, mobile backhaul, and spectral sensing technology. In this paper, firstly, the basic structure and principle of integrated external cavity tunable diode lasers are introduced, and then two main integrated structures of 1.55 μm waveband external cavity tunable diode lasers are reviewed and compared in detail, namely the hybrid integrated structure and monolithic integrated structure of 1.55 μm waveband integrated external cavity tunable diode lasers. Finally, the research progress in 1.55 μm waveband integrated external cavity tunable diode lasers in the last decade are summarised, and the advantages and disadvantages of 1.55 μm waveband integrated external cavity tunable diode lasers are analysed. The results show that, with the transformation of optical communication into more complex modulation formats, it is necessary to integrate miniature 1.55 μm waveband external cavity tunable diode lasers. Low-cost integrated 1.55 μm waveband external cavity tunable diode lasers are expected to be used in the next generation of optical transceivers in small-factor modules.

Ginsenoside Rg5, a relatively uncommon secondary ginsenoside, exhibits notable pharmacological activity and is commonly hypothesized to originate from the dehydration of Rg3. In this work, we compared different conversion pathways using Rb1, R-Rg3 and S-Rg3 as the raw material under simple acid catalysis. Interestingly, the results indicate that the conversion follows this reaction activity order Rb1 > S-Rg3 > R-Rg3, which is contrary to the common understanding of Rg5 obtained from Rg3 by dehydration. Our experimental results have been fully confirmed by theoretical calculations and a NOESY analysis. The DFT analysis reveals that the free energies of S-Rg3 and R-Rg3 in generating carbocation are 7.56 mol/L and 7.57 mol/L, respectively, which are significantly higher than the free energy of 1.81 mol/L when Rb1 generates the same carbocation. This finding aligns with experimental evidence suggesting that Rb1 is more prone to generating Rg5 than Rg3. The findings from the nuclear magnetic resonance (NMR) analysis suggest that the fatty chains (C22–C27) in R-Rg3 and S-Rg3 adopt a Gauche conformation and an anti conformation with C16–C17 and C13–C17, respectively, due to the relatively weak repulsive van der Waals force. Therefore, the configuration of R-Rg3 is more conducive to the formation of intramolecular hydrogen bonds between 20C–OH and 12C–OH, whereas S-Rg3 lacks this capability. Consequently, this also explains the fact that S-Rg3 is more prone to dehydration to generate Rg5 than R-Rg3. Additionally, our research reveals that the synthetic route of Rg5 derived from protopanaxadiol (PPD)-type ginsenosides (including Rb1, Rb2, Rb3, Rc and Rd) exhibits notable advantages in terms of efficacy, purity and yield when compared to the pathway originating from Rg3. Moreover, this study presents a highly effective and practical approach for the extensive synthesis of Rg5, thereby facilitating the exploration of its pharmacological properties and potential application in drug discovery.

The North China Plain is an important area for agricultural economic development in China. But water shortages, severe groundwater over-exploitation and drought problems make it difficult to exercise the topographic resource advantages of the plain. Therefore, the precise monitoring of soil moisture is of great significance for the rational use of water resources. Soil characteristics vary in natural farmland ecosystems, crops are constrained by multiple compound stresses and the precise extraction of soil moisture stress is a difficult and critical problem. The long time series was decomposed via complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to obtain different intrinsic mode function (IMF) components, and the statistical descriptors of each component were calculated to realize the precise discrimination of soil moisture stress. A quantitative evaluation model of soil moisture was established, and the different noise addition ratios and modeling types were set respectively to investigate the optimal inversion model. The results showed that: (1) The reconstruction error of the CEEMDAN was small and almost 0; it had a high reconstruction accuracy and was more suitable for the decomposition of the long time series. The first two components, IMF1 and IMF2, were soil moisture stress subsequences, and it could effectively reflect the moisture stress situation. (2) The inversion model performed well when ε was 0.05 and the model type was quadratic, with a coefficient of determination R2 of 0.98, which gave a better fit and less error. (3) The overall soil moisture content in the study area was low, basically in the range of 6.9% to 15.7%, with the central part, especially the south-central part, being the most affected by soil moisture stress, and the overall impact of soil moisture stress showed a decreasing trend from February to May. The utilization of CEEMDAN further enhances the accuracy of soil moisture inversion in agricultural fields, realizing the effective application of remote sensing observation technology and time-frequency analysis technology in the field of soil moisture research.

A novel GH2 (glycoside hydrolase family 2) β-galactosidase from Marinomonas sp. BSi20584 was successfully expressed in E. coli with a stable soluble form. The recombinant enzyme (rMaBGA) was purified to electrophoretic homogeneity and characterized extensively. The specific activity of purified rMaBGA was determined as 96.827 U mg−1 at 30 °C using ONPG (o-nitrophenyl-β-D-galactopyranoside) as a substrate. The optimum pH and temperature of rMaBGA was measured as 7.0 and 50 °C, respectively. The activity of rMaBGA was significantly enhanced by some divalent cations including Zn2+, Mg2+ and Ni2+, but inhibited by EDTA, suggesting that some divalent cations might play important roles in the catalytic process of rMaBGA. Although the enzyme was derived from a cold-adapted strain, it still showed considerable stability against various physical and chemical elements. Moreover, rMaBGA exhibited activity both toward Galβ-(1,3)-GlcNAc and Galβ-(1,4)-GlcNAc, which is a relatively rare occurrence in GH2 β-galactosidase. The results showed that two domains in the C-terminal region might be contributed to the β-1,3-galactosidase activity of rMaBGA. On account of its fine features, this enzyme is a promising candidate for the industrial application of β-galactosidase.

Double bandgap photonic crystals (PCs) exhibit significant potential for applications in various color display-related fields. However, they show low color saturation and inadequate color modulation capabilities. This study presents a viable approach to the fabrication of double bandgap photonic inks diffracting typical secondary colors and other composite colors by simply mixing two photonic nanochains (PNCs) of different primary colors as pigments in an appropriate percentage following the conventional RGB color matching method. In this approach, the PNCs are magnetically responsive and display three primary colors that can be synthesized by combining hydrogen bond-guided and magnetic field (H)-assisted template polymerization. The as-prepared double bandgap photonic inks present high color saturation due to the fixed and narrow full-width at half-maxima of the parent PNCs with a suitable chain length. Furthermore, they can be used to easily produce a flexible double bandgap PC film by embedding the PNCs into a gel, such as polyacrylamide, facilitating fast steady display performance without the requirement of an external magnetic field. This research not only presents the unique advantages of PNCs in constructing multi-bandgap PCs but also establishes the feasibility of utilizing PNCs in practical applications within the fields of anti-counterfeiting and flexible wearable devices.

Graphene has attracted extensive attention in various fields due to its intriguing properties. In this work, nanocomposite films based on poly(lactic acid) (PLA and PLLA) polymers filled with graphene oxide (GO) were developed. The impact of treating GO with the anionic surfactant dioctyl sulfosuccinate sodium salt (AOT) on the properties of the resulting nanocomposites was investigated. To determine the morphological, optical, and structural properties of the obtained materials, physicochemical analyses were performed, including scanning electron microscopy (SEM), atomic force microscopy (AFM), Fourier-transform infrared spectroscopy (FT-IR), and X-ray diffraction (XRD) analysis. Additionally, the thermal properties and wettability of neat polymers and nanocomposites were thoroughly investigated using differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), and contact angle analysis. It was observed that GO was well dispersed throughout the PLA and PLLA matrix, leading to stronger interface bonding. The results demonstrate that the untreated and treated GO improved the crystallinity and thermal stability properties of the PLA and PLLA. However, the AOT-treated GO has significantly higher performance compared to the untreated GO in terms of crystallinity, melting temperature (increased by ~15 °C), and wettability (the contact angle decreased by ~30°). These findings reveal the high performance of the developed novel composite, which could be applied in tissue engineering as a scaffold.

Considering the current limitations and restricted scope of existing experiments, as well as the absence of corresponding numerical simulation verifications and comparisons, and the lack of actual case studies of variable mass flow calculation and comparison, this study focuses on high production oilfields in the Mideast and South China Sea. The objective is to investigate single-phase and multiphase variable mass flow through numerical and experimental simulations. The study develops linear regression equations to establish the relationship between the mixture pressure drop caused by side flow and the velocities of the main flow, as well as the ratio between side and main flow velocities. Actual calculations using these equations are provided. The comprehensive analysis reveals that, for a fixed total flow rate, an increase in the side versus main injection velocity ratio leads to an increase in pressure loss before and after the injection hole. In single-phase flow, the friction factor for side hole flow is generally higher than that for only axial main flow, with the same total flow rate. In multiphase flow, when the gas-liquid ratio (GLR) is relatively large, the side flow has minimal impact on pressure drop, while at lower GLR values, the side flow significantly increases the pressure drops. When predicting the pressure drop for single-phase variable mass flow in horizontal wellbores, it is appropriate to consider only the mixture pressure drop caused by the closest hole to the calculation section, assuming the injection hole flow rates are approximately equal. In terms of predicting the productivity of single-phase variable mass flow, it is crucial to consider the mixture pressure drop. Neglecting the mixture pressure drop can lead to relatively larger productivity prediction results, with potential production rate errors exceeding 50%. The accuracy of the prediction is influenced by the ratio of mixture pressure drop to production pressure differential, and the pressure along the external zone of the screen pipe is higher when considering the mixture pressure drop compared to when it is neglected. Additionally, the flow rate along the external zone of the screen pipe becomes more non-uniform when the mixture pressure drop is considered. Furthermore, the findings from the single-phase and multiphase flow experiments suggest that significant deviations in production rates may occur in scenarios with low gas-liquid ratio (GLR), highlighting the need for further investigation in this area.

We have experimentally implemented a photonics-aided large-capacity fiber-mm-wave wireless communication system employing a simple dual-polarized single-input single-output (SISO) wireless based on polarization multiplexing at the W-band. To compare the performance of different algorithms, 18G-baud, and 35G-baud 16-level quadrature-amplitude-modulation (16QAM), probabilistically shaped 16QAM (PS-16QAM), 64QAM and PS-64QAM signal using different carrier phase recovery (CPR) algorithms are transmitted in the system. Moreover, we compare the Viterbi–Viterbi (VV), improved new algorithm based on VV (NVV), blind phase search (BPS), and two-stage BPS algorithms’ computational complexity to better compare different algorithms. Using the experiment result, we can demonstrate that the BPS algorithm is about half a magnitude better than the NVV algorithm for PS-QAM signals, while the NVV algorithm has the lowest computational complexity. Additionally, we also achieve error-free wireless transmission at a net data rate of 324.1 Gb/s with the bit error ratio (BER) below the forward-error correction (FEC) threshold of 1 × 10−2 assuming soft-decision forward-error correction (SD-FEC) when using the BPS algorithm.

The soluble solids content (SSC) of pears is mainly composed of sugars, organic acids, and other soluble substances and is one of the important indices used to measure the sweetness and quality of pear juice. The SSC of pears is mainly composed of sugars, organic acids, amino acids, esters, alcohols, phenols, flavonoids, and other compounds, and different groups within these compounds have different characteristic absorption peaks corresponding to different characteristic wavelengths. Traditional methods such as genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) models used for screening characteristic wavelengths are mainly based on statistical methods, and characteristic wavelengths are selected by finding the wavelengths related to the changes in the concentration of the target analytes. By ignoring the molecular structure and chemical properties of the target analytes and disregarding the influence of the groups of the compounds in the target analytes on the spectral characteristics, wavelengths that are not related to the target analytes may be selected, thus affecting the accuracy of the analytical results. In this paper, a partial least squares (PLS) model was established based on the characteristic wavelengths of CARS, GA, and LASSO algorithms, and the best least absolute shrinkage and selection operator (LASSO) was selected and compared with the characteristic wavelengths selected by group weighted fusion (GWF). The LASSO regression was validated by 10-fold cross-validation to select the appropriate regularization parameter, and the 33 characteristic wavelengths correlated with the SSC of pears were selected in the full spectral range, and the 9 characteristic wavelengths corresponding to the group response were weighted and fused and input into the PLS regression model. Using an established model, the coefficient of determination (R2) and the root mean square error (RMSE) of the calibration set were 0.992 and 0.177%, respectively, and the R2 and RMSE of the test set were 0.998 and 0.128%, respectively. The R2 of our LASSO–GWF–PLS prediction model was improved from 0.975 to 0.998, indicating that the LASSO–GWF–PLS method has very good prediction ability for detection of SSC in pears.

The intelligent diagnosis of premium threaded connections (PTCs) is vital for ensuring the robust and leak-proof performance of tubing under high-temperature, high-pressure, acidic gas conditions. However, achieving accurate diagnostic results necessitates a substantial number of PTCs curves under diverse make-up conditions, presenting considerable challenges in practical industrial detection. In this study, we introduce an end-to-end classification model, which combines an asynchronously optimized two-dimensional deep convolutional generative adversarial network (AoT-DCGAN) and a two-dimensional convolutional neural network (T-CNN), designed to enhance the classification performance under small sample size. Our proposed method first leverages AoT-DCGAN to identify the distribution patterns of the original samples and generate synthetic counterparts. Concurrently, we implement a novel weight optimization strategy, termed asynchronous optimization (AO), to alleviate the issue of gradient vanishing during the generator’s optimization phase. Following this, a novel T-CNN model is devised and trained on the enlarged dataset to automate the classification of PTCs curves. The performance evaluation of our method, based on recall, specificity, F1-score, precision values, and confusion matrices at varying data augmentation ratios, demonstrates that the model’s classification capabilities are enhanced as the dataset size escalates, peaking at a dataset size of 1200. Moreover, given the same training set, the T-CNN model outperforms traditional machine learning and deep learning models, achieving classification accuracies of up to 95.9%, 95.5%, and 96.7% for the AC, ATI, and NDT curves, respectively. Lastly, it was confirmed that applying asynchronous optimization in the DCGAN training process results in a more consistent decline in the loss function.

Surfaces with light-trapping structures are widely used in solar cells to enhance light capturing and to transform efficiency. The study of light-trapping character is important for light-trapping structures in solar cells. In the present study, the light-trapping character for the regular hemisphere pit arrays (RHPAs) in solar cells was intensively investigated in terms of reducing light reflection, suppressing light escape, and increasing the length of the optical path. Results show that the RHPAs can decrease surface reflectivity by ~54% compared with the plane structure, and can reflect ~33% of the light that has not been absorbed back into the absorption layer of the solar cell. The total optical path of the cell with the RHPAs structure remarkably increased from 2ω to 4ω. To verify the theoretical research conclusions, we produced the glass structure samples with different aspect ratios by using micro/nanometer-processing technology. The reflection ratios for silicon wafers covered by plane and RHPAs glass samples were tested. The test results were compared with the theoretical calculation results, which showed consistency.

Cucurbita species are widely used as rootstocks for cucumber, watermelon, and other plants for the restriction of toxic Na+ transport from root to shoot. Previous studies have found distinct salt tolerance strategies between Cucurbita moschata and Cucurbita maxima; the former accumulates a large amount of Na+ in the root, while the latter accumulates Na+ in the shoot. To further study the mechanism of Na+ transport in plants, four reciprocal grafts were made between MB (C. moschata) and JHL (C. maxima), denoted as MB/MB, JHL/JHL, MB/JHL, and JHL/MB (scion/rootstock). The results showed that using MB as the rootstock effectively reduced the accumulation of Na+ in the scion. Conversely, JHL rootstock tended to transfer more Na+ to the scion. To clarify this phenomenon, the velocities of Na+ flows on the root surface, stem, and vein of grafting combinations were measured using non-invasive micro-test technology. Compared with the seedlings using JHL as rootstock, the grafted combination with MB as rootstock had a higher root Na+ efflux and lower Na+ fluxes in the stem and vein. qRT-PCR analyses revealed the critical roles of salt overly sensitive 1 and high-affinity potassium as components of the mechanism enabling Na+ exclusion from the root and Na+ unloading from the stem xylem. Compared with the seedlings using MB as rootstocks, the JHL-grafted plants showed more rapid stomatal closure and decreased transpiration rate in the first three hours after salt stress but maintained a higher level under prolonged salt treatment (120 h). The tissue tolerances of JHL and MB were assessed using the isolated leaves under NaCl to exclude the influence of the root and stem. The results showed that the salinity inflicted more serious damage to MB leaves than to JHL leaves. qRT-PCR analyses indicated that the intracellular Na+/H+ transporter in the leaf vein was involved in this process. All these findings indicated that C. moschata and C. maxima adopted different strategies for regulating Na+ transport, and grafting can be used as a tool to create more salt-tolerant plants.

Tea diseases are one of the main causes of tea yield reduction, and the use of computer vision for classification and diagnosis is an effective means of tea disease management. However, the random location of lesions, high symptom similarity, and complex background make the recognition and classification of tea images difficult. Therefore, this paper proposes a tea disease IterationVIT diagnosis model that integrates a convolution and iterative transformer. The convolution consists of a superimposed bottleneck layer for extracting the local features of tea leaves. The iterative algorithm incorporates the attention mechanism and bilinear interpolation operation to obtain disease location information by continuously updating the region of interest in location information. The transformer module uses a multi-head attention mechanism for global feature extraction. A total of 3544 images of red leaf spot, algal leaf spot, bird’s eye disease, gray wilt, white spot, anthracnose, brown wilt, and healthy tea leaves collected under natural light were used as samples and input into the IterationVIT model for training. The results show that when the patch size is 16, the model performed better with an IterationVIT classification accuracy of 98% and F1 measure of 96.5%, which is superior to mainstream methods such as VIT, Efficient, Shuffle, Mobile, Vgg, etc. In order to verify the robustness of the model, the original images of the test set were blurred, noise- was added and highlighted, and then the images were input into the IterationVIT model. The classification accuracy still reached over 80%. When 60% of the training set was randomly selected, the classification accuracy of the IterationVIT model test set was 8% higher than that of mainstream models, with the ability to analyze fewer samples. Model generalizability was performed using three sets of plant leaf public datasets, and the experimental results were all able to achieve comparable levels of generalizability to the data in this paper. Finally, this paper visualized and interpreted the model using the CAM method to obtain the pixel-level thermal map of tea diseases, and the results show that the established IterationVIT model can accurately capture the location of diseases, which further verifies the effectiveness of the model.