Search

Chlorophyll content is an important physiological indicator reflecting the growth status of crops. Traditional methods for obtaining crop chlorophyll content are time-consuming and labor-intensive. The rapid development of UAV remote sensing platforms offers new possibilities for monitoring chlorophyll content in field crops. To improve the efficiency and accuracy of monitoring chlorophyll content in maize canopies, this study collected RGB, multispectral (MS), and SPAD data from maize canopies at the jointing, tasseling, and grouting stages, constructing a dataset with fused features. We developed maize canopy chlorophyll content monitoring models based on four machine learning algorithms: BP neural network (BP), multilayer perceptron (MLP), support vector regression (SVR), and gradient boosting decision tree (GBDT). The results showed that, compared to single-feature methods, the MS and RGB fused feature method achieved higher monitoring accuracy, with R² values ranging from 0.808 to 0.896, RMSE values between 2.699 and 3.092, and NRMSE values between 10.36% and 12.26%. The SVR model combined with MS–RGB fused feature data outperformed the BP, MLP, and GBDT models in monitoring maize canopy chlorophyll content, achieving an R² of 0.896, an RMSE of 2.746, and an NRMSE of 10.36%. In summary, this study demonstrates that by using the MS–RGB fused feature method and the SVR model, the accuracy of chlorophyll content monitoring can be effectively improved. This approach reduces the need for traditional methods of measuring chlorophyll content in maize canopies and facilitates real-time management of maize crop nutrition.

Strawberries are a commonly used agricultural product in the food industry. In the traditional production model, labor costs are high, and extensive picking techniques can result in food safety issues, like poor taste and fruit rot. In response to the existing challenges of low detection accuracy and slow detection speed in the assessment of strawberry fruit maturity in orchards, a CR-YOLOv9 multi-stage method for strawberry fruit maturity detection was introduced. The composite thinning network, CRNet, is utilized for target fusion, employing multi-branch blocks to enhance images by restoring high-frequency details. To address the issue of low computational efficiency in the multi-head self-attention (MHSA) model due to redundant attention heads, the design concept of CGA is introduced. This concept aligns input feature grouping with the number of attention heads, offering the distinct segmentation of complete features for each attention head, thereby reducing computational redundancy. A hybrid operator, ACmix, is proposed to enhance the efficiency of image classification and target detection. Additionally, the Inner-IoU concept, in conjunction with Shape-IoU, is introduced to replace the original loss function, thereby enhancing the accuracy of detecting small targets in complex scenes. The experimental results demonstrate that CR-YOLOv9 achieves a precision rate of 97.52%, a recall rate of 95.34%, and an mAP@50 of 97.95%. These values are notably higher than those of YOLOv9 by 4.2%, 5.07%, and 3.34%. Furthermore, the detection speed of CR-YOLOv9 is 84, making it suitable for the real-time detection of strawberry ripeness in orchards. The results demonstrate that the CR-YOLOv9 algorithm discussed in this study exhibits high detection accuracy and rapid detection speed. This enables more efficient and automated strawberry picking, meeting the public’s requirements for food safety.

We are concerned with the Riemann problem for the isentropic Euler equations of mixed type in the dark energy fluid. This system is non-strictly hyperbolic on the boundary curve of elliptic and hyperbolic regions. We obtain the unique admissible shock waves by utilizing the viscosity criterion. Assuming fixed left states are in the elliptic and hyperbolic regions, respectively, we construct the unique Riemann solution for the mixed-type models with the initial right state in some feasible regions. Finally, we present numerical simulations which are consistent with our theoretical results.

The lion-head goose is the only large goose species in China, and it is one of the largest goose species in the world. Lion-head geese have a strong tolerance for massive energy intake and show a priority of fat accumulation in liver tissue through special feeding. Therefore, the aim of this study was to investigate the impact of high feed intake compared to normal feeding conditions on the transcriptome changes associated with fatty liver development in lion-head geese. In this study, 20 healthy adult lion-head geese were randomly assigned to a control group (CONTROL, n = 10) and high-intake-fed group (CASE, n = 10). After 38 d of treatment, all geese were sacrificed, and liver samples were collected. Three geese were randomly selected from the CONTROL and CASE groups, respectively, to perform whole-transcriptome analysis to analyze the key regulatory genes. We identified 716 differentially expressed mRNAs, 145 differentially expressed circRNAs, and 39 differentially expressed lncRNAs, including upregulated and downregulated genes. GO enrichment analysis showed that these genes were significantly enriched in molecular function. The node degree analysis and centrality metrics of the mRNA–lncRNA–circRNA triple regulatory network indicate the presence of crucial functional nodes in the network. We identified differentially expressed genes, including HSPB9, Pgk1, Hsp70, ME2, malic enzyme, HSP90, FADS1, transferrin, FABP, PKM2, Serpin2, and PKS, and we additionally confirmed the accuracy of sequencing at the RNA level. In this study, we studied for the first time the important differential genes that regulate fatty liver in high-intake feeding of the lion-head goose. In summary, these differentially expressed genes may play important roles in fatty liver development in the lion-head goose, and the functions and mechanisms should be investigated in future studies.

Crop diseases pose a major threat to agricultural production, quality, and sustainable development, highlighting the importance of early disease risk prediction for effective disease control. Tea anthracnose can easily occur in Yunnan under high-temperature and high-humidity environments, which seriously affects the ecosystem of tea gardens. Therefore, the establishment of accurate, non-destructive, and rapid prediction models has a positive impact on the conservation of biodiversity in tea plantations. Because of the linear relationship between disease occurrence and environmental conditions, the growing environmental conditions can be effectively used to predict crop diseases. Based on the climate data collected by Internet of Things devices, this study uses LASSO-COX-NOMOGRAM to analyze the expression of tea anthracrum to different degrees through Limma difference analysis, and it combines Cox single-factor analysis to study the influence mechanism of climate and environmental change on tea anthracrum. Modeling factors were screened by LASSO regression, 10-fold cross-validation and Cox multi-factor analysis were used to establish the basis of the model, the nomogram prediction model was constructed, and a Shiny- and DynNOM-visualized prediction system was built. The experimental results showed that the AUC values of the model were 0.745 and 0.731 in the training set and 0.75 and 0.747 in the verification set, respectively, when the predicted change in tea anthracnose disease risk was greater than 30% and 60%, and the calibration curve was in good agreement with the ideal curve. The accuracy of external verification was 83.3% for predicting tea anthracnose of different degrees. At the same time, compared with the traditional prediction method, the method is not affected by the difference in leaf background, which provides research potential for early prevention and phenotypic analysis, and also provides an effective means for tea disease identification and harm analysis.

The changing global climate has significantly impacted the spread of plant pests. The cassava mealybug (Phenacoccus manihoti) is among the most dangerous quarantine pests affecting cassavas worldwide, causing substantial losses in agricultural production and food security across several regions. Although China is currently free of the cassava mealybug, its proximity to affected countries and extensive trade with these regions necessitate a detailed understanding of the pest’s distribution pattern and dynamic ecological niche changes. Using the Biomod2 model, we selected two historical climate scenarios and two future climate scenarios (SSP1-2.6 and SSP5-8.5) to investigate the distribution patterns, potential habitats, distribution centers, and dynamic ecological niches of cassava mealybugs in China. Key environmental variables influencing the distribution were identified, including bio4, bio8, bio12, bio18, and bio19. The potential habitat of cassava mealybugs is mainly located in several provinces in southern China. In the future, the suitable habitat is projected to expand slightly under the influence of climate change, maintaining the overall trend, but the distribution center of suitable areas will shift northward. Dynamic ecological niche prediction results indicate the potential for further expansion; however, the ecological niches may be unequal and dissimilar in the invaded areas. The predictions could serve as a valuable reference for early warning systems and management strategies to control the introduction of cassava mealybugs.

Mass timber construction has recently gained popularity due to its outstanding environmental benefits and building performance, which hold revolutionary potential for the construction industry. However, its impacts from the perspective of occupants have not been thoroughly explored. This study introduces an innovative empirical approach that explores the potential benefits of mass timber construction for individuals and organizations, with an emphasis on the workplace. We review the conceptual framework regarding how visual and physical exposure to timber construction materials and finishes have a positive effect on individuals and organizations at a broad level. We propose a more holistic mixed-method behavioral approach to studying occupant behavior and well-being by integrating self-reported questionnaires, objective biomarkers (heart rate variability and hair cortisol), and indoor environmental quality (IEQ) measures. Our study offers a novel research primer on the exploration of mass timber construction impacts and benefits for both office workers and construction workers. Participants from different office settings completed pre- and post-occupancy evaluation surveys to assess their experiences, including IEQ satisfaction, productivity, and health. Office workers were located in three different offices: a controlled laboratory environment, an open-plan office, and an open-plan space with a timber interior. The construction workers worked in a timber space for three months and then moved to work in a building with a concrete structure. The analysis included descriptive statistics, t-tests, ANOVA, and linear regression to compare differences between office settings and assess the relationship between environmental variables and overall satisfaction in IEQ, comfort, productivity, and health. In office workers, in terms of building image, thermal comfort, and artificial lighting, the data analysis revealed significant differences in occupants’ satisfaction levels between office settings. However, the low number of participants affected the results, and some factors were not found significant in relation to the office setting. Among tradespeople, there was no relationship between the building environment and productivity, health, or comfort. However, the results of hair cortisol testing indicated that working in a timber space can decrease the level of cortisol (stress) and have an impact on the productivity of workers. Such occupant’s perspective research is pivotal to informing policy makers, developers, business owners, construction professionals, timber industry stakeholders, environmentalists, and researchers in their decision-making processes. Fostering the future widespread adoption and advancement of mass timber construction.

Given the rapid advancements in kinetic pursuit technology, this paper introduces an innovative maneuvering strategy, denoted as LSRC-TD3, which integrates line-of-sight (LOS) angle rate correction with deep reinforcement learning (DRL) for high-speed unmanned aerial vehicle (UAV) pursuit–evasion (PE) game scenarios, with the aim of effectively evading high-speed and high-dynamic pursuers. In the challenging situations of the game, where both speed and maximum available overload are at a disadvantage, the playing field of UAVs is severely compressed, and the difficulty of evasion is significantly increased, placing higher demands on the strategy and timing of maneuvering to change orbit. While considering evasion, trajectory constraint, and energy consumption, we formulated the reward function by combining “terminal” and “process” rewards, as well as “strong” and “weak” incentive guidance to reduce pre-exploration difficulty and accelerate convergence of the game network. Additionally, this paper presents a correction factor for LOS angle rate into the double-delay deterministic gradient strategy (TD3), thereby enhancing the sensitivity of high-speed UAVs to changes in LOS rate, as well as the accuracy of evasion timing, which improves the effectiveness and adaptive capability of the intelligent maneuvering strategy. The Monte Carlo simulation results demonstrate that the proposed method achieves a high level of evasion performance—integrating energy optimization with the requisite miss distance for high-speed UAVs—and accomplishes efficient evasion under highly challenging PE game scenarios.

The Chinese mitten crab (Eriocheir sinensis), an economically important crustacean that is endemic to China, has recently experienced high-temperature stress. The high thermal tolerance of E. sinensis points to its promise in being highly productive in an aquacultural context. However, the mechanisms underlying its high thermal tolerance remain unknown. In this study, female E. sinensis that were heat exposed for 24 h at 38.5 °C and 33 °C were identified as high-temperature-stressed (HS) and normal-temperature-stressed (NS) groups, respectively. The hepatopancreas of E. sinensis from the HS and NS groups were used for transcriptome and proteomic analyses. A total of 2350 upregulated and 1081 downregulated differentially expressed genes (DEGs) were identified between the HS and NS groups. In addition, 126 differentially expressed proteins (DEPs) were upregulated and 35 were downregulated in the two groups. An integrated analysis showed that 2641 identified genes were correlated with their corresponding proteins, including 25 genes that were significantly differentially expressed between the two omics levels. Ten Gene Ontology terms were enriched in the DEGs and DEPs. A functional analysis revealed three common pathways that were significantly enriched in both DEGs and DEPs: fluid shear stress and atherosclerosis, leukocyte transendothelial migration, and thyroid hormone synthesis. Further analysis of the common pathways showed that MGST1, Act5C, HSP90AB1, and mys were overlapping genes at the transcriptome and proteome levels. These results demonstrate the differences between the HS and NS groups at the two omics levels and will be helpful in clarifying the mechanisms underlying the thermal tolerance of E. sinensis.

Dual active bridge (DAB) converters are widely used in DC microgrids because of their superior bidirectional energy flow regulation capability and characteristics, such as wide voltage gain and zero-voltage switching (ZVS). However, due to the inherent contradiction between the minimum backflow power and the ZVS of the power switches, the existing modulation methods are difficult to optimize and coordinate. Most of the studies increase the complexity of energy flow regulation in the optimization process. To solve the above problems, this paper proposes a collaborative modulation method of dual-side backflow power optimization and ZVS for DAB. The method constructs a dual-side backflow power optimization modulation strategy that is simple to control and uniform in all working conditions by analyzing the mathematical model of backflow power. Meanwhile, based on this optimized modulation strategy, a regulatory factor of phase-shift ratio is introduced to collaborate with the ZVS of the power switches, which reduces the backflow power while ensuring the ZVS of the primary and secondary sides. Finally, a 500W DAB prototype is built, and the experimental results verify the feasibility and effectiveness of the proposed modulation method.

Current challenges in visible and infrared image fusion include color information distortion, texture detail loss, and target edge blur. To address these issues, a fusion algorithm based on double-domain transform filter and nonlinear contrast transform feature extraction (DDCTFuse) is proposed. First, for the problem of incomplete detail extraction that exists in the traditional transform domain image decomposition, an adaptive high-pass filter is proposed to decompose images into high-frequency and low-frequency portions. Second, in order to address the issue of fuzzy fusion target caused by contrast loss during the fusion process, a novel feature extraction algorithm is devised based on a novel nonlinear transform function. Finally, the fusion results are optimized and color-corrected by our proposed spatial-domain logical filter, in order to solve the color loss and edge blur generated in the fusion process. To validate the benefits of the proposed algorithm, nine classical algorithms are compared on the LLVIP, MSRS, INO, and Roadscene datasets. The results of these experiments indicate that the proposed fusion algorithm exhibits distinct targets, provides comprehensive scene information, and offers significant image contrast.

Due to the Jacobian matrix rank reduction near singularities, applying numerical methods to study PMs’ motion stability at singularities is quite difficult. As a result, there is a scarcity of literature on the investigation of PMs’ dynamic behaviors near singularities and the influence of kinematic parameters on the motion stability of PMs. To address the research gap related to the above issues, based on the Gerschgorin perturbation method, Hurwitz exact approach, and the Lyapunov dynamic stability theory, the influence of kinematic parameters and external loads on a PM’s motion stability at singularities is studied for the first time. The theoretical analysis results reported in this paper reveal many previously undiscovered features beyond those derived from previous numerical methods, and indicate the limitations of some widely accepted statements. For example, increasing the angular speed of the movable platform can expand the range of the external loads that meet the motion stability at singular configurations. The prevailing notion in prior research that PMs are unable to support external loads in the direction of the gained DoF at singular configurations is only partially accurate. This pioneering research establishes a theoretical foundation for exploring a new real-time approach to avoid dynamic singularities by fully exploiting the influence mechanisms of kinematic parameters on PMs’ dynamic stability at singularities.

Rice bran is an important byproduct of the rice polishing process, rich in nutrients, but it is underutilized and often used as feed or discarded, resulting in a huge amount of waste. In this study, rice bran was fermented by Lactobacillus fermentum MF423 to obtain a product with high antioxidant activity. First, a reliable and efficient method for assessing the antioxidant capacity of the fermentation products was established using high-performance liquid chromatography (HPLC), which ensured the consistency of the batch fermentation. The fermented rice bran product (FLRB) exhibited significant antioxidant activity in cells, C. elegans, and hyperlipidemic mice. Transcriptome analysis of mouse livers showed that the expression of plin5 was upregulated in diabetic mice administered FLRB, thereby preventing the excessive production of free fatty acids (FFAs) and the subsequent generation of large amounts of reactive oxygen species (ROS). These studies lay the foundation for the application of rice bran fermentation products.

Background: Long COVID, characterized by a persistent symptom spectrum following SARS-CoV-2 infection, poses significant health, social, and economic challenges. This review aims to consolidate knowledge on its epidemiology, clinical features, and underlying mechanisms to guide global responses; Methods: We conducted a literature review, analyzing peer-reviewed articles and reports to gather comprehensive data on long COVID’s epidemiology, symptomatology, and management approaches; Results: Our analysis revealed a wide array of long COVID symptoms and risk factors, with notable demographic variability. The current understanding of its pathophysiology suggests a multifactorial origin yet remains partially understood. Emerging diagnostic criteria and potential therapeutic strategies were identified, highlighting advancements in long COVID management; Conclusions: This review highlights the multifaceted nature of long COVID, revealing a broad spectrum of symptoms, diverse risk factors, and the complex interplay of physiological mechanisms underpinning the condition. Long COVID symptoms and disorders will continue to weigh on healthcare systems in years to come. Addressing long COVID requires a holistic management strategy that integrates clinical care, social support, and policy initiatives. The findings underscore the need for increased international cooperation in research and health planning to address the complex challenges of long COVID. There is a call for continued refinement of diagnostic and treatment modalities, emphasizing a multidisciplinary approach to manage the ongoing and evolving impacts of the condition.

With the arrival of the Internet of Things era, the demand for tactile sensors continues to grow. However, traditional sensors mostly require an external power supply to meet real-time monitoring, which brings many drawbacks such as short service life, environmental pollution, and difficulty in replacement, which greatly limits their practical applications. Therefore, the development of a passive self-power supply of tactile sensors has become a research hotspot in academia and the industry. In this review, the development of self-powered tactile sensors in the past several years is introduced and discussed. First, the sensing principle of self-powered tactile sensors is introduced. After that, the main performance parameters of the tactile sensors are briefly discussed. Finally, the potential application prospects of the tactile sensors are discussed in detail.

Background: Metabolic changes in donkey meat during the early postmortem period have not been previously reported. Methods: The LC–MS-based metabolomics technique was conducted to understand the metabolic profiles and identify the key metabolites of donkey meat in the first 48 h postmortem. Results: The pH values showed a decreasing trend followed by an increasing trend. Shear force was the lowest at 4 h and the highest at 24 h (p < 0.05). For the metabolome, some candidate biomarker metabolites were identified, such as adenine, inosine, n-acetylhistidine, citric acid, isocitrate, and malic acid. Predominant metabolic pathways, such as citrate cycle (TCA cycle), alanine, aspartate and glutamate metabolism, and purine metabolism, were affected by aging time. Overabundant n-acetylhistidine was identified in LT, declined at 12 h postmortem aging, and then increased. This may explain the significantly lower pH at 12 h postmortem. Adenine was higher at 4 h postmortem, then declined. Decreased ADP may indicate a fast consumption of ATP and subsequent purine metabolism in donkey meat. Conclusions: The results of this study provided new insights into early postmortem aging of donkey meat quality.

In response to the challenge of low recognition rates for similar phenotypic symptoms of tea diseases in low-light environments and the difficulty in detecting small lesions, a novel adaptive method for tea disease severity detection is proposed. This method integrates an image enhancement algorithm based on an improved EnlightenGAN network and an enhanced version of YOLO v8. The approach involves first enhancing the EnlightenGAN network through non-paired training on low-light-intensity images of various tea diseases, guiding the generation of high-quality disease images. This step aims to expand the dataset and improve lesion characteristics and texture details in low-light conditions. Subsequently, the YOLO v8 network incorporates ResNet50 as its backbone, integrating channel and spatial attention modules to extract key features from disease feature maps effectively. The introduction of adaptive spatial feature fusion in the Neck part of the YOLOv8 module further enhances detection accuracy, particularly for small disease targets in complex backgrounds. Additionally, the model architecture is optimized by replacing traditional Conv blocks with ODConv blocks and introducing a new ODC2f block to reduce parameters, improve performance, and switch the loss function from CIOU to EIOU for a faster and more accurate recognition of small targets. Experimental results demonstrate that YOLOv8-ASFF achieves a tea disease detection accuracy of 87.47% and a mean average precision (mAP) of 95.26%. These results show a 2.47 percentage point improvement over YOLOv8, and a significant lead of 9.11, 9.55, and 7.08 percentage points over CornerNet, SSD, YOLOv5, and other models, respectively. The ability to swiftly and accurately detect tea diseases can offer robust theoretical support for assessing tea disease severity and managing tea growth. Moreover, its compatibility with edge computing devices and practical application in agriculture further enhance its value.

Pest target identification in agricultural production environments is challenging due to the dense distribution, small size, and high density of pests. Additionally, changeable environmental lighting and complex backgrounds further complicate the detection process. This study focuses on enhancing the recognition performance of tea pests by introducing a lightweight pest image recognition model based on the improved YOLOv8 architecture. First, slicing-aided fine-tuning and slicing-aided hyper inference (SAHI) are proposed to partition input images for enhanced model performance on low-resolution images and small-target detection. Then, based on an ELAN, a generalized efficient layer aggregation network (GELAN) is designed to replace the C2f module in the backbone network, enhance its feature extraction ability, and construct a lightweight model. Additionally, the MS structure is integrated into the neck network of YOLOv8 for feature fusion, enhancing the extraction of fine-grained and coarse-grained semantic information. Furthermore, the BiFormer attention mechanism, based on the Transformer architecture, is introduced to amplify target characteristics of tea pests. Finally, the inner-MPDIoU, based on auxiliary borders, is utilized as a replacement for the original loss function to enhance its learning capacity for complex pest samples. Our experimental results demonstrate that the enhanced YOLOv8 model achieves a precision of 96.32% and a recall of 97.95%, surpassing those of the original YOLOv8 model. Moreover, it attains an mAP@50 score of 98.17%. Compared to Faster R-CNN, SSD, YOLOv5, YOLOv7, and YOLOv8, its average accuracy is 17.04, 11.23, 5.78, 3.75, and 2.71 percentage points higher, respectively. The overall performance of YOLOv8 outperforms that of current mainstream detection models, with a detection speed of 95 FPS. This model effectively balances lightweight design with high accuracy and speed in detecting small targets such as tea pests. It can serve as a valuable reference for the identification and classification of various insect pests in tea gardens within complex production environments, effectively addressing practical application needs and offering guidance for the future monitoring and scientific control of tea insect pests.

In order to efficiently identify early tea diseases, an improved YOLOv8 lesion detection method is proposed to address the challenges posed by the complex background of tea diseases, difficulty in detecting small lesions, and low recognition rate of similar phenotypic symptoms. This method focuses on detecting tea leaf blight, tea white spot, tea sooty leaf disease, and tea ring spot as the research objects. This paper presents an enhancement to the YOLOv8 network framework by introducing the Receptive Field Concentration-Based Attention Module (RFCBAM) into the backbone network to replace C2f, thereby improving feature extraction capabilities. Additionally, a mixed pooling module (Mixed Pooling SPPF, MixSPPF) is proposed to enhance information blending between features at different levels. In the neck network, the RepGFPN module replaces the C2f module to further enhance feature extraction. The Dynamic Head module is embedded in the detection head part, applying multiple attention mechanisms to improve multi-scale spatial location and multi-task perception capabilities. The inner-IoU loss function is used to replace the original CIoU, improving learning ability for small lesion samples. Furthermore, the AKConv block replaces the traditional convolution Conv block to allow for the arbitrary sampling of targets of various sizes, reducing model parameters and enhancing disease detection. the experimental results using a self-built dataset demonstrate that the enhanced YOLOv8-RMDA exhibits superior detection capabilities in detecting small target disease areas, achieving an average accuracy of 93.04% in identifying early tea lesions. When compared to Faster R-CNN, MobileNetV2, and SSD, the average precision rates of YOLOv5, YOLOv7, and YOLOv8 have shown improvements of 20.41%, 17.92%, 12.18%, 12.18%, 10.85%, 7.32%, and 5.97%, respectively. Additionally, the recall rate (R) has increased by 15.25% compared to the lowest-performing Faster R-CNN model and by 8.15% compared to the top-performing YOLOv8 model. With an FPS of 132, YOLOv8-RMDA meets the requirements for real-time detection, enabling the swift and accurate identification of early tea diseases. This advancement presents a valuable approach for enhancing the ecological tea industry in Yunnan, ensuring its healthy development.

Many onshore oil fields currently adopt water flooding as a means to supplement reservoir energy. However, due to reservoir heterogeneity, significant differences in permeability exist not only between different reservoirs but also within the same reservoir across different planar orientations. After prolonged fluid flushing in the near-wellbore zone of injection wells, the resulting increased flow resistance between layers exacerbates inefficient and ineffective circulation. A considerable amount of remaining oil is left unexploited in untouched areas, significantly impacting the overall recovery. To investigate the multiscale plugging mechanisms of fracture-dominated pore channels in high-water-cut oil reservoirs and achieve efficient management of fractured large channels, this study explores the formation of the fracture-flushing zone-low saturation oil zone. A physical experimental model with fractures and high-intensity flushing is established to analyze changes in pore structure, mineral composition, residual oil distribution, and other characteristics at different positions near the fractures. The research aims to clarify the mechanism behind the formation of large channels with fracture structures. The results indicate that under high-intensity water flushing, cementing materials are washed away by the flowing water, clay particles are carried to the surface with the injected fluid, and permeability significantly increases, forming high-permeability zones with fracture structures. In the rock interior away from the fracture end, channels, corners, and clustered oil content noticeably decrease, while the content of film-like oil substantially increases, and clay minerals are not significantly washed away. Under strong flushing conditions, the number of residual clay particles near the fracture end is mainly influenced by flow velocity and flushing time; thus, the greater the flushing intensity, the faster the water flow, and the longer the flushing time, the fewer residual clay particles near the fracture end.

The development of the electrification of aircraft has prompted aviation hydraulic systems to shift from traditional centralized valve actuators (CVAs) to electro-hydrostatic actuators (EHAs). In this paper, aiming at the demand for a quantitative comparison of performance between CVAs and EHAs, CVA and EHA prototypes with the same power level and test platform were developed. Then, based on the power flow and dynamic models of the CVA and EHA, simulation and experimental comparative tests were conducted using different load spectrum test conditions and step response test conditions. The comparative test results showed that the efficiency of the EHA was better than that of the CVA, and the dynamic response of the CVA was better than that of the EHA. Finally, a power loss quantification and parameter sensitivity analysis were performed to reveal the impact of different parameters on the different power losses and to provide suggestions for improving the performance of CVAs and EHAs.

Zeolite microspheres have been successfully applied in commercial-scale separators such as oxygen concentrators. However, further enhancement of their applications is hampered by the post-synthetic shaping process that formulates the zeolite powder into packing-sized spherical bodies with various binders leading to active site blockage and suboptimal performance. Herein, binderless zeolite microspheres with a tunable broad size range from 2 µm to 500 µm have been developed with high crystallinity, sphericity over 92%, monodispersity with a coefficient of variation (CV) less than 5%, and hierarchical pore architecture. Combining precursor impregnation and steam-assisted crystallization (SAC), mesoporous silica microspheres with a wide size range could be successfully transformed into zeolite. For preserved size and spherical morphology, a judicious selection of the synthesis conditions is crucial to ensure a pure phase, high crystallinity, and hierarchical architecture. For the sub-2-µm zeolite microsphere, low-temperature prolonged aging was important so as to suppress external zeolization that led to a large, single macroporous crystal. For the large 500 µm sphere, ultrasound pretreatment and vacuum impregnation were crucial and facilitated spatially uniform gel matrix dispersion and homogenous crystallization. The obtained zeolite 5A microspheres exhibited excellent air separation performance, while the 4A microspheres displayed ammonium removal capabilities. This work provides a general strategy to overcome the existing limitations in fabricating binder-free technical bodies of zeolites for various applications.

Subsidence of the earth’s surface induced by mining activities has always been a critical concern in the relevant research fields. This subsidence disrupts the original geological structures and can lead to secondary geological hazards, environmental degradation, and threats to human lives and property. An in-depth investigation of this issue led to us using the three-dimensional finite-difference numerical simulation software FLAC3D 6.0 in this study. The research focuses on the Prang Copper Mine subsidence area in Yunnan Province, China, with a particular emphasis on the comprehensive analysis of the formation mechanisms of a large-scale crack appearing on the south side of the subsidence area. The study also includes a predictive analysis of the future development trends of this crack. The simulation results indicate that the crack formation was a consequence of the combined effects of uneven surface subsidence induced by underground structural interfaces and underground mining activities. As mining activities continued, the non-uniform subsidence of the surface intensified. The northward (Y-axis) displacement difference of characteristic points A and B on both sides of the crack continuously increased, signifying the widening of the crack. Mining activities also influence the displacement in the X-axis direction, potentially posing risks to support structures on either side of existing drainage channels. Therefore, effective control measures are warranted. Furthermore, this study highlights the possibility that new mining activities may further exacerbate subsidence on the south side of the subsidence area. This research provides valuable insights into the complexity of surface subsidence and its associated risks, offering guidance for mining activity planning and safety measures.

In forest ecosystems, most of the soil organic matter is derived from trees, as deadwood lignocellulose and wood-decaying basidiomycetes are the most important decomposers of lignin and cellulose. Fomes fomentarius is one of the most common white-rot fungi colonizing angiosperm trees and is often found in birch deadwood but seldom in pine deadwood. To reveal the mechanism through which F. fomentarius selects angiosperms as its preferred host trees, birch and pinewood sticks were selected for culturing for two months. The weight loss, cellulose and lignin degradation rates, activities of degrading enzymes, and transcriptome analyses of two degradation models were compared and analyzed. The results showed that F. fomentarius-degraded birchwood with higher efficiency than pinewood. A GO enrichment analysis found that more upregulated genes related to the top 30 terms showed a molecular function related to degradation, and most genes belonged to the CAZymes family in F. fomentarius-degraded birchwood. However, pinewood degradation did not show these phenomena. A KEGG pathway analysis also indicated that, for the same pathway, more upregulated genes were involved in birchwood degradation caused by F. fomentarius than in pinewood degradation.

Among broilers, the main pathogen that leads to swollen head syndrome (SHS) is the subgroup C avian metapneumovirus (aMPV-C). The aMPV-C infection can lead to an upsurge in the rate of soft-shell eggs, resulting in reduced egg production and seriously affecting the economy of the livestock industry. Therefore, a rapid method for aMPV-C detection needs to be invented. According to the N gene of aMPV-C, we designed the specific probe and primer and created a reverse transcription recombinase-aided amplification assay (RT-RAA) for the detection of aMPV-C. aMPV-C could be detected quickly and specifically by this method at 41 °C for 30 min. The sensitivity assay inferred that the minimum detection threshold of RT-RAA was 3.38 × 101 copies/μL. A specificity assay showed that the RT-RAA method did not cross-react with other subgroups (aMPV-A, aMPV-B, aMPV-D) or other viruses (H9N2, NDV, IBV, IBDV). Forty samples of known clinical background were tested by RT-RAA and RT-qPCR. The two approaches had a 100% correlation rate. In conclusion, this research successfully created an RT-RAA assay for aMPV-C.

High temperatures are important environmental stressors affecting the metabolism, growth, immunity, and mortality of Chinese mitten crabs (Eriocheir sinensis). In this study, Chinese mitten crabs were divided into two groups and exposed to temperatures of 35 °C (thermal stress group) or 25 °C (control group) for 24 h, and the transcriptome of the heart was analyzed. There were 4007 differentially expressed genes (DEGs) between the thermal stress and the control groups, including 2660 upregulated and 1347 downregulated genes. Heat shock proteins (HSPs) and transcription factors (TFs) were temperature-sensitive DEGs in Chinese mitten crabs. DEGs mainly focused on protein processing in the endoplasmic reticulum, ribosome biogenesis, glycine, serine, and threonine metabolism, protein export, and insect hormone biosynthesis pathways. A total of 28,916 SSRs and 59 TF families, including 851 TFs, were detected among all unigenes of E. sinensis transcripts. The qRT-PCR results for the HSPs and apoptotic DEGs from the heart exhibited the same trends as those in the E. sinensis transcriptome data. Results of light microscopy analyzing histological sections of the heart indicated that most myocardial fibers were lysed, and the number of nuclei and the connective tissue contents between the myocardial layers were both reduced following 35 °C exposure for 24 h.

Erythromycin, a macrolide antibiotic, is a prioritized pollutant that poses a high risk to environmental health. It has been detected in different environmental matrices and can cause undesired effects in aquatic organisms, particularly freshwater algae, which are primary producers. However, the impact of erythromycin on marine algae remains largely unexplored. Erythromycin has been reported to induce hormetic effects in the marine diatom Thalassiosira weissflogii (T. weissflogii). These effects are associated with the molecular pathways and biological processes of ribosome assembly, protein translation, photosynthesis, and oxidative stress. However, the alterations in the global gene expression have yet to be validated at the metabolic level. The present study used non-targeted metabolomic analysis to reveal the altered metabolic profiles of T. weissflogii under erythromycin stress. The results showed that the increased cell density was possibly attributed to the accumulation of steroidal compounds with potential hormonic action at the metabolic level. Additionally, slight increases in the mitochondrial membrane potential (MMP) and viable cells were observed in the treatment of 0.001 mg/L of erythromycin (an environmentally realistic level). Contrarily, the 0.75 and 2.5 mg/L erythromycin treatments (corresponding to EC20 and EC50, respectively) showed decreases in the MMP, cell density, and viable algal cells, which were associated with modified metabolic pathways involving ATP-binding cassette (ABC) transporters, the metabolism of hydrocarbons and lipids, thiamine metabolism, and the metabolism of porphyrin and chlorophyll. These findings suggest that metabolomic analysis, as a complement to the measurement of apical endpoints, could provide novel insights into the molecular mechanisms of hormesis induced by antibiotic agents in algae.

Surface engineering has been proved efficient and universally applicable in improving the performance of CeO2 in various fields. However, previous approaches have typically required high-temperature calcination or tedious procedures, which makes discovery of a moderate and facile modification approach for CeO2 an attractive subject. In this paper, porous CeO2 nanosheets with effective nitrogen-doping were synthesized via a low-temperature NH3/Ar plasma treatment and exhibited boosted hydrogen evolution reaction performance with low overpotential (65 mV) and long-term stability. The mechanism of the elevated performance was investigated by introducing Ar-plasma-treated CeO2 with no nitrogen-doping as the control group, which revealed the dominant role of nitrogen-doping by providing abundant active sites and improving charge transfer characteristics. This work illuminates further investigations into the surface engineering methodologies boosted by plasma and the relative mechanism of the structure–activity relationship.

The overuse and misuse of fertilizers have been causing duckweed outbreaks in irrigation ditches and paddy fields in many rice-growing areas. However, how duckweed coverage in a paddy field affects the rice yield and grain quality is under debate because duckweed may act as either a weed, competing with rice for mineral nutrients, or a “nutrient buffer”, providing significant ecological and economic benefits. To understand the effects of duckweed coverage throughout rice growth on the yield and quality of rice grains, an experiment with three Japonica rice cultivars was conducted with fertile lotus-pond bottom soil as a growth medium to provide sufficient mineral nutrients for both the duckweed and rice. Averaged across three rice cultivars, duckweed coverage decreased the panicle density but increased the spikelet density and grain weight, resulting in no significant change in the rice yield. Duckweed coverage had no impact on the processing and appearance quality in general, but significant duckweed-by-cultivar interactions were detected in the head rice percentage and grain chalkiness, indicating different sensitivities of different cultivars in response to the duckweed treatment. The decrease in breakdown and increase in setback values in the rapid visco analyzer (RVA) profile of rice flour suggested that duckweed coverage during rice growth worsened the cooking quality of the rice. However, no significant change in the palatability of the cooked rice was found. The most profound change induced by the duckweed was the nutritional quality; duckweed coverage increased the protein concentration but decreased the concentrations of Mg, Mn, Cu, and Zn in rice grains. This preliminary study suggested that duckweed coverage during rice growth has profound effects on the rice nutrient uptake and grain nutritional quality under the circumstances, and further research on the responses of the rice quality to the duckweed coverage in paddy fields in multiple locations and years is needed.

Camellia semiserrata is an important woody edible oil tree species in southern China that is characterized by large fruits and seed kernels with high oil contents. Increasing soil acidification due to increased use of fossil fuels, misuse of acidic fertilizers, and irrational farming practices has led to leaching of aluminum (Al) in the form of free Al3+, Al(OH)2+, and Al(OH)2+, which inhibits the growth and development of C. semiserrata in South China. To investigate the mechanism underlying C. semiserrata responses to Al stress, we determined the changes in photosynthetic parameters, antioxidant enzyme activities, and osmoregulatory substance contents of C. semiserrata leaves under different concentrations of Al stress treatments (0, 1, 2, 3, and 4 mmol/L Alcl3) using a combination of physiological and proteomics approaches. In addition, we identified the differentially expressed proteins (DEPs) under 0 (CK or GNR0), 2 mmol/L (GNR2), and 4 mmol/L (GNR4) Al stress using a 4D-label-free technique. With increasing stress concentration, the photosynthetic indexes of C. semiserrata leaves, peroxidase (POD), superoxide dismutase (SOD), catalase (CAT), soluble protein (SP), and soluble sugar (SS) showed an overall trend of increasing and then decreasing, and proline (Pro) and malondialdehyde (MDA) contents tended to continuously increase overall. Compared with the control group, we identified 124 and 192 DEPs in GNR2 and GNR4, respectively, which were mainly involved in metabolic processes such as photosynthesis, flavonoid metabolism, oxidative stress response, energy and carbohydrate metabolism, and signal transduction. At 2 mmol/L Al stress, carbon metabolism, amino sugar and nucleotide sugar metabolism, and flavonoid metabolism-related proteins were significantly changed, and when the stress was increased to 4 mmol/L Al, the cells accumulated reactive oxygen species (ROS) at a rate exceeding the antioxidant system scavenging capacity. To deal with this change, C. semiserrata leaves enhanced their glutathione metabolism, drug metabolism-cytochrome P450, metabolism of xenobiotics by cytochrome P450, and other metabolic processes to counteract peroxidative damage to the cytoplasmic membrane caused by stress. In addition, we found that C. semiserrata resisted aluminum toxicity mainly by synthesizing anthocyanidins under 2 mmol/L stress, whereas proanthocyanidins were alleviated by the generation of proanthocyanidins under 4 mmol/L stress, which may be a special mechanism by which C. semiserrata responds to different concentrations of aluminum stress.

Lycium barbarum seed meal contains a variety of bioactive compounds, but the use of L. barbarum seed meal in the food industry is rare. This study aimed to evaluate the effect of reducing sugars on the structural and flavor properties of the Maillard reaction products (MRPs) of the Lycium barbarum seed meal hydrolysate (LSH). The results showed that the flavors and tastes of the MRPs were affected by reducing sugars. In comparison to oligosaccharides, monosaccharides were more suitable for the development of MRPs with good sensory qualities. The structural characteristics of L. barbarum seed meal precursor MRPs were also affected by reducing sugars. The MRPs produced with the participation of monosaccharides had higher ultraviolet absorption and browning than the MRPs produced with oligosaccharides. The molecular weights of the MRPs were found to be 128–500 Da and 500–1000 Da. Compared to the MRPs made from other sugars, xylose-meridian products (X-MRPs) had a stronger meaty flavor. The mellowness and continuity of the MRPs made from monosaccharides were superior to those made from oligosaccharides. The MRPs formed by L. barbarum seed meal exhibited the characteristics of umami and meat flavor. MRPs with better flavors may be used to develop new types of seasoning salts.

This paper explores the application and advantages of remote sensing, machine learning, and mid-infrared spectroscopy (MIR) as a popular proximal sensing spectroscopy tool in the estimation of soil organic carbon (SOC). It underscores the practical implications and benefits of the integrated approach combining machine learning, remote sensing, and proximal sensing for SOC estimation and prediction across a range of applications, including comprehensive soil health mapping and carbon credit assessment. These advanced technologies offer a promising pathway, reducing costs and resource utilization while improving the precision of SOC estimation. We conducted a comparative analysis between MIR-predicted SOC values and laboratory-measured SOC values using 36 soil samples. The results demonstrate a strong fit (R² = 0.83), underscoring the potential of this integrated approach. While acknowledging that our analysis is based on a limited sample size, these initial findings offer promise and serve as a foundation for future research. We will be providing updates when we obtain more data. Furthermore, this paper explores the potential for commercialising these technologies in Australia, with the aim of helping farmers harness the advantages of carbon markets. Based on our study’s findings, coupled with insights from the existing literature, we suggest that adopting this integrated SOC measurement approach could significantly benefit local economies, enhance farmers’ ability to monitor changes in soil health, and promote sustainable agricultural practices. These outcomes align with global climate change mitigation efforts. Furthermore, our study’s approach, supported by other research, offers a potential template for regions worldwide seeking similar solutions.

Phosphorus is an essential nutrient for plant and animal growth, while excessive phosphorus discharges can cause eutrophication of water bodies, leading to deterioration of water quality and posing serious risks to biodiversity and human health. Hematite is abundant on the Earth’s surface and plays a key role in phosphorus cycle. In particular, hematite nanoparticles may adsorb organic or inorganic phosphorus, consequently affecting phosphorus fate and effects. Yet, how the intrinsic properties of hematite (e.g., crystal facet) affect its adsorption process of phosphorus remains unclear. Here, three hematite nanoparticles with different exposed crystal facets were controllably synthesized to investigate their adsorption of phosphate under different pH conditions. The results revealed that the efficiency of hematite for adsorbing the phosphate depends on the crystal facets of hematite in the order of {104} > {110} > {001}. The phosphate adsorption on the hematite surface involves inner-sphere complexation between the phosphate and surface hydroxyl groups of hematite. The facet-dependent adsorption affinity of phosphate to hematite is mainly determined by the content of singly coordinated hydroxyl groups of hematite. These findings are useful to evaluate the potential environmental risks of iron oxide nanoparticles as a medium to the biochemical cycle of P element.

Facility agriculture in China is facing the challenge of the excessive use of chemical fertilizers (nitrogen fertilizers), which hinder the development of sustainable and environmentally friendly agriculture. Optimizing nitrogen fertilizer allocation is essential to balance agricultural production and environmental concerns. The aim of this study was to determine the optimal organic fertilizer strategy for tomato cultivation under different irrigation methods. An experiment was conducted in a greenhouse, and two irrigation methods, surface irrigation (SI) and drip irrigation (SDI), were used during tomato growth. The fertilization treatments included urea alone (CK); 30%, 40%, and 50% chicken manure mixed with urea (FC1, FC2, FC3); and 50% cow manure and sheep manure mixed with urea (FB3, FS3). The results showed that the irrigation techniques and fertilization had significant effects on ammonia volatilization accumulation, soil mineral nitrogen content, and tomato yield and quality. Compared with the surface irrigation technique with the same amount of fertilizer application, the drip irrigation technique reduced the ammonia volatilization accumulation by a maximum of 76.40%. The SDIFC3 and SDIFB3 ammonia volatilization accumulation was as low as 5.24 (kg·hm−2) and 7.61 (kg·hm−2); the soil nitrate nitrogen content was reduced, and the tomato yield increased significantly by 17.11%. The SDIFC3 treatment achieved a maximum yield of 13,414 (kg·hm−2), increased the tomato vitamin C and soluble sugar contents by 19.13% and 8.97%, and lowered the titratable acid content by as much as 30.51%. Under drip irrigation fertilization conditions, the SDIFC3 treatment showed lower ammonia volatilization accumulation and the highest tomato yield and quality compared to CK and the same proportion of organic fertilizer substitutes with cow and sheep manure. The increase in the proportion of organic fertilizers replacing chemical fertilizers resulted in a gradual decrease in ammonia volatilization accumulation and a gradual increase in the tomato yield and various qualities. The soil mineral N content, on the other hand, was significantly affected by irrigation, fertilizer application, and water–fertilizer interaction effects, with a tendency for the content to increase and then decrease after each fertilizer application. The mineral N content was lower with drip irrigation compared to surface irrigation, especially in the 10–20 cm soil layer than in the 0–10 cm layer. Increasing drip irrigation and organic fertilizer substitution significantly increased the vitamin C and soluble sugar contents in the tomatoes, while decreasing the titratable acid content.

The ever-increasing food requirement with globally growing population demands advanced agricultural practices to improve grain yield, to gain crop resilience under unpredictable extreme weather, and to reduce production loss caused by insects and pathogens. To fulfill such requests, genome engineering technology has been applied to various plant species. To date, several generations of genome engineering methods have been developed. Among these methods, the new mainstream technology is clustered regularly interspaced short palindromic repeats (CRISPR) with nucleases. One of the most important processes in genome engineering is to deliver gene cassettes into plant cells. Conventionally used systems have several shortcomings, such as being labor- and time-consuming procedures, potential tissue damage, and low transformation efficiency. Taking advantage of nanotechnology, the nanoparticle-mediated gene delivery method presents technical superiority over conventional approaches due to its high efficiency and adaptability in different plant species. In this review, we summarize the evolution of plant biomolecular delivery methods and discussed their characteristics as well as limitations. We focused on the cutting-edge nanotechnology-based delivery system, and reviewed different types of nanoparticles, preparation of nanomaterials, mechanism of nanoparticle transport, and advanced application in plant genome engineering. On the basis of established methods, we concluded that the combination of genome editing, nanoparticle-mediated gene transformation and de novo regeneration technologies can accelerate crop improvement efficiently in the future.

The protein tyrosine phosphatase PTPN22 inhibits T cell activation by dephosphorylating some essential proteins in the T cell receptor (TCR)-mediated signaling pathway, such as the lymphocyte-specific protein tyrosine kinase (Lck), Src family tyrosine kinases Fyn, and the phosphorylation levels of Zeta-chain-associated protein kinase-70 (ZAP70). For the first time, we have successfully produced PTPN22 CS transgenic mice in which the tyrosine phosphatase activity of PTPN22 is suppressed. Notably, the number of thymocytes in the PTPN22 CS mice was significantly reduced, and the expression of cytokines in the spleen and lymph nodes was changed significantly. Furthermore, PTPN22 CS facilitated the positive and negative selection of developing thymocytes, increased the expression of the TCRαβ-CD3 complex on the thymus cell surface, and regulated their internalization and recycling. ZAP70, Lck, Phospholipase C gamma1(PLCγ1), and other proteins were observed to be reduced in PTPN22 CS mouse thymocytes. In summary, PTPN22 regulates TCR internalization and recycling via the modulation of the TCR signaling pathway and affects TCR expression on the T cell surface to regulate negative and positive selection. PTPN22 affected the development of the thymus, spleen, lymph nodes, and other peripheral immune organs in mice. Our study demonstrated that PTPN22 plays a crucial role in T cell development and provides a theoretical basis for immune system construction.

Intensive culturing of hybrid yellow catfish (Pseudobagrus vachellii ♂ × Tachysurus fulvidraco ♀) has increased their mortality. Astragalus membranaceus has been used as an immune stimulant and antioxidant in fish for several years. A. membranaceus was decocted and mixed with the diet. After feeding for 28 d, the hybrid yellow catfish were challenged with Aeromonas hydrophila. To better understand the function of A. membranaceus in the defense of hybrid yellow catfish against A. hydrophila, we analyzed the spleen transcriptome data and relative percentage survival (RPS). There were 396 differentially expressed genes (DEGs) between the A. membranaceus and control groups at 24 h after A. hydrophila stimulation, including 263 upregulated and 133 downregulated DEGs. A significant enrichment of DEGs was found in the A. membranaceus group when the GO enrichment terms in the spleen were analyzed. The qRTPCRresults for the five upregulated and two downregulated DEGs from the spleen, intestine, and liver were consistent with the transcriptome data. The relative percentage survival of A. membranaceus was 85.71% after the fish were challenged with A. hydrophila.

An active rotor with trailing-edge flaps (TEFs) is an effective active vibration control method for helicopters. Blade flapping dynamic characteristics have a significant effect on the active vibration control performance of an active rotor. In this study, an aeroelastic model is developed using the Hamilton principle, and a quasi-steady Theodorsen model for the airfoil with a TEF is utilized to calculate the aerodynamic loads induced by the dynamic deflection of TEFs. The accuracy of this model is validated through a comparison with the CAMRAD calculation and flight test results of a SA349/2 helicopter. Based on the modal orthogonality and the equilibrium equation of the blade flapping motion, the method of changing the blade flapping dynamic characteristics is obtained. Blade sectional characteristics are adjusted to study the effect of blade flapping dynamics on the vibration control authority of an active rotor. The simulation results demonstrate that if the modal frequency of second-order flap is tuned to close to the rotor passage frequency, the flapping dynamic characteristics are capable of enhancing the vibration control performance of the active rotor.

Due to the frailty of elderly individuals’ physical condition, falling can lead to severe bodily injuries. Effective fall detection can significantly reduce the occurrence of such incidents. However, current fall detection methods heavily rely on visual and multi-sensor devices, which incur higher costs and complex wearable designs, limiting their wide-ranging applicability. In this paper, we propose a fall detection method based on nursing aids integrated with multi-array flexible tactile sensors. We design a kind of multi-array capacitive tactile sensor and arrange the distribution of tactile sensors on the foot based on plantar force analysis and measure tactile sequences from the sole of the foot to develop a dataset. Then we construct a fall detection model based on a graph convolution neural network and long-short term memory network (GCN-LSTM), where the GCN module and LSTM module separately extract spatial and temporal features from the tactile sequences, achieving detection on tactile data of foot and walking states for specific time series in the future. Experiments are carried out with the fall detection model, the Mean Squared Error (MSE) of the predicted tactile data of the foot at the next time step is 0.0716, with the fall detection accuracy of 96.36%. What is more, the model can achieve fall detection on 5-time steps with 0.2-s intervals in the future with high confidence results. It exhibits outstanding performance, surpassing other baseline algorithms. Besides, we conduct experiments on different ground types and ground morphologies for fall detection, and the model showcases robust generalization capabilities.

Holotrichia parallela is an important plant pest. Comparative feeding experiments showed that the egg production, oviposition duration and survival rate of H. parallela beetles were significantly higher when they fed on elm leaves than when they fed on willow or purpus privet leaves. RNA sequencing was used to determine transcriptomic changes associated with oviposition. Comparative transcriptome analysis revealed that the beetles that fed on elm and willow had a total of 171 genes with differential expression. When the beetles fed on elm and purpus privet, 3568 genes had differential expression. The vitellogenesis, ovarian serine protease, odorant-binding proteins, acyl-CoA synthetase and follicle cell proteins were commonly upregulated genes in elm-fed beetles compared with those fed on willow/purpus privet leaves. The involvement of the follicle cell protein 3C gene in the regulation of oviposition was confirmed using RNA interference. The results provide insights into the molecular mechanisms underlying oviposition in H. parallela feeding on different host plants. This study also describes a method for identifying potentially effective genes for pest control.

The drop-dry deposition (DDD) is a simple chemical technique of thin film deposition, which can be applied to metal oxides. The deposition solution is an aqueous solution including a metal salt and an alkali. However, some metal ions react spontaneously with water and precipitate. This work is the first attempt to use complexing agents in DDD to suppress the precipitation. SnO2 thin films are fabricated using DDD with Na2S2O3 as a complexing agent and via annealing in air. The results of the Auger electron spectroscopy measurement show that the O/Sn composition ratio of the annealed films approached two, indicating that the annealed films are SnO2. The photoelectrochemical measurement results show that the annealed films are n-type. Co3O4/SnO2 heterojunction is fabricated using p-type Co3O4 films which are also deposited via DDD. The heterojunction has rectification and photovoltaic properties. Thus, for the first time, a metal oxide thin film was successfully prepared via DDD using a complexing agent, and oxide thin film solar cells are successfully prepared using only DDD.

Ammonia (NH3) is a crucial alkaline component in the atmosphere, with significant impacts on environmental and ecosystem health. However, our understanding of the long-term variability characteristics of NH3 emissions is still limited due to the scarcity of long-term continuous NH3 emission observation data. In this study, we investigated the global NH3 emission evolution pattern during the high-emission season (March–August) in historical (1850–2014) and future (2015–2060) periods, based on the simulated global NH3 emission and temperature data using the CESM2-WACCM model from CMIP6. We utilized cluster analysis, KNN regression simulation, and transfer matrix analysis to explore the emission characteristics. In the historical period, the analysis revealed that the high NH3 emission season is March–August, accounting for about 60% of annual emissions, with a significant increasing trend of NH3 emissions. The global average NH3 emissions in the last 164 years were about four times higher (28.06 mg m−2) than those in 1850 (5.52 mg m−2). Moreover, on the intercontinental scale, NH3 emissions from 1850 to 2014 March–August exhibited dynamic increases characterized differently across continents. Europe showed an increasing and then decreasing trend, Asia demonstrated a rapid increase, while South America, North America, and Africa exhibited medium increases, and Australia showed low increases. The global NH3 emissions experienced three distinct periods of low (1850–1964, slope = 0.059 mg m−2 y−1), high (1965–1988, slope = 0.389 mg m−2 y−1), and medium (1989–2014, slope = 0.180 mg m−2 y−1) rates of increase. Starting from the high rate of increase period, the hotspots of global NH3 emissions gradually shifted from Europe to East and South Asia. Looking ahead, our findings suggest that the global NH3 emission rate will tend to slow down under the Representative Concentration Pathway (RCP) 4.5 and RCP8.5 warming scenarios. However, compared with the medium-rate increasing period, the moderate and heavy NH3 emission areas under RCP4.5 and RCP8.5 scenarios will show a tendency to expand by 2060, with the proportion of area covered by heavy emissions increasing by 0.55% and 0.56%, respectively. In conclusion, our study highlights that NH3 pollution remains a significant environmental challenge in the future period, with Asia and Europe being the key areas requiring attention for NH3 emission reduction.

Literacy assessment is essential for effective literacy instruction and training. However, traditional paper-based literacy assessments are typically decontextualized and may cause stress and anxiety for test takers. In contrast, serious games and game environments allow for the assessment of literacy in more authentic and engaging ways, which has some potential to increase the assessment’s validity and reliability. The primary objective of this study is to examine the feasibility of a novel approach for stealthily assessing literacy skills using games in an intelligent tutoring system (ITS) designed for reading comprehension strategy training. We investigated the degree to which learners’ game performance and enjoyment predicted their scores on standardized reading tests. Amazon Mechanical Turk participants (n = 211) played three games in iSTART and self-reported their level of game enjoyment after each game. Participants also completed the Gates–MacGinitie Reading Test (GMRT), which includes vocabulary knowledge and reading comprehension measures. The results indicated that participants’ performance in each game as well as the combined performance across all three games predicted their literacy skills. However, the relations between game enjoyment and literacy skills varied across games. These findings suggest the potential of leveraging serious games to assess students’ literacy skills and improve the adaptivity of game-based learning environments.

While numerous studies have suggested that pyrolysis of the forestry residues to produce biochar and return it back for forestry use is an effective way for the utilization of forestry wastes, there have been few reports addressing the effect of pyrolysis temperature on its agronomic functions, especially the secondary metabolism of the medicinal tree. In this study, two kinds of biochar were pyrolyzed using poplar sawdust at 350 °C (C350) and 600 °C (C600), respectively, then applied as auxiliary substrate material for the cultivation of Cyclocarya paliurus seedlings in a greenhouse to study their effects on soil properties, seedling growth and the accumulation of main secondary metabolites in C. paliurus leaves. The results showed that biochar application definitely ameliorated most of the soil properties, and this improvement was further enhanced as the pyrolysis temperature of the biochar increased. Compared with the basic substrate (CK), the growth of C. paliurus seedlings increased under C600 and decreased under C350, but the application of two biochars had no significant effect on the content of the main secondary metabolites in the leaves. In contrast, the seedling growth with the direct application of poplar sawdust (C0) was significantly inhibited, while the content of secondary metabolites in the leaves increased significantly. In terms of the yield of leaf secondary metabolites, the application of biochar with high pyrolysis temperature (C600) had the highest yield, while the direct application of poplar sawdust had the lowest yield. In addition, the contents of several metabolite monomers were higher under C0 than these of the other three treatments. The results indicated a certain trade-off between the primary growth and the synthesis of leaf secondary metabolites in C. paliurus affected by different biochars and poplar sawdust application, while the accumulation of leaf secondary metabolites was generally dependent on leaf biomass.

We assessed interactions between the astrocytic volume-regulated anion channel (VRAC) and aquaporin 4 (AQP4) in the supraoptic nucleus (SON). Acute SON slices and cultures of hypothalamic astrocytes prepared from rats received hyposmotic challenge (HOC) with/without VRAC or AQP4 blockers. In acute slices, HOC caused an early decrease with a late rebound in the neuronal firing rate of vasopressin neurons, which required activity of astrocytic AQP4 and VRAC. HOC also caused a persistent decrease in the excitatory postsynaptic current frequency, supported by VRAC and AQP4 activity in early HOC; late HOC required only VRAC activity. These events were associated with the dynamics of glial fibrillary acidic protein (GFAP) filaments, the late retraction of which was mediated by VRAC activity; this activity also mediated an HOC-evoked early increase in AQP4 expression and late subside in GFAP-AQP4 colocalization. AQP4 activity supported an early HOC-evoked increase in VRAC levels and its colocalization with GFAP. In cultured astrocytes, late HOC augmented VRAC currents, the activation of which depended on AQP4 pre-HOC/HOC activity. HOC caused an early increase in VRAC expression followed by a late rebound, requiring AQP4 and VRAC, or only AQP4 activity, respectively. Astrocytic swelling in early HOC depended on AQP4 activity, and so did the early extension of GFAP filaments. VRAC and AQP4 activity supported late regulatory volume decrease, the retraction of GFAP filaments, and subside in GFAP-VRAC colocalization. Taken together, astrocytic morphological plasticity relies on the coordinated activities of VRAC and AQP4, which are mutually regulated in the astrocytic mediation of HOC-evoked modulation of vasopressin neuronal activity.

This paper investigates a novel robust adaptive dynamic surface control scheme based on the barrier Lyapunov function (BLF), online composite learning, disturbance observer, and improved saturation function. It is mainly designed for a class of skid-to-turn (STT) interceptor integrated guidance and control (IGC) design problems under multi-source uncertainties, state constraints, and input saturation. The serial-parallel estimation model used in this study estimates the system states and provides “critic” information for the neural network and disturbance observer; then, these three are combined to realize online composite learning of the multiple uncertainties of the system and improve the interception accuracy. In addition, the state and input constraints are resolved by adopting the BLF and the improved saturation function, while the design of the auxiliary system ensures stability. Finally, a series of simulation results show that the proposed IGC scheme with a direct-hit intercept strategy achieves a satisfactory effect, demonstrating the validity and robustness of the scheme.

There is a growing focus on the production of biochar from organic wastes and its forestry use. However, it is unclear how applying biochar pyrolyzed at different temperatures influences both soil bacterial and fungal communities. In this study, two kinds of biochar were pyrolyzed at 350 °C and 600 °C, from poplar sawdust, and then applied as an auxiliary substrate material (15% addition by volume) for the container seedling cultivation of Cyclocarya paliurus, a native timber and medically used tree species, to compare the response of the soil’s microbial traits. The results showed that after 5 months of cultivation, the addition of the biochar application improved the soil’s physical and chemical properties to a certain extent by decreasing its bulk density and increasing its field water-holding capacity, pH and organic carbon content. The soil’s pH and content of organic C, available N and available P were significantly higher in the substrate with the addition of the biochar pyrolyzed at 600 °C (Bioc600) than in that of the substrate with added pyrolyzed biochar at 350 °C (Bioc350). The biochar application also enhanced the soil’s microbial N (MBN) but showed no changes in the MBC/MBN ratio. The biochar application had no effect on the diversity and composition of the soil’s bacterial community, but showed a certain effect on its fungal community composition and had different influences between low and high pyrolysis temperatures. The relative abundance of several of the dominant fungal phyla and genera increased with the addition of biochar due to the increase in the soil’s pH when compared to the CK (no biochar or sawdust addition). When compared with Bioc350, the dominant phylum Basidiomycota and genus Vanrija further increased in abundance with Bioc600. These findings reveal the consistent effects of biochar pyrolysis temperature and demonstrate the different regulatory roles of biochar application in soil bacterial and fungal communities, providing valuable information on how biochar can effectively be used as a substrate for seedling cultivation.

Mammography interpretation is challenging with high error rates. This study aims to reduce the errors in mammography reading by mapping diagnostic errors against global mammographic characteristics using a radiomics-based machine learning approach. A total of 36 radiologists from cohort A (n = 20) and cohort B (n = 16) read 60 high-density mammographic cases. Radiomic features were extracted from three regions of interest (ROIs), and random forest models were trained to predict diagnostic errors for each cohort. Performance was evaluated using sensitivity, specificity, accuracy, and AUC. The impact of ROI placement and normalization on prediction was investigated. Our approach successfully predicted both the false positive and false negative errors of both cohorts but did not consistently predict location errors. The errors produced by radiologists from cohort B were less predictable compared to those in cohort A. The performance of the models did not show significant improvement after feature normalization, despite the mammograms being produced by different vendors. Our novel radiomics-based machine learning pipeline focusing on global radiomic features could predict false positive and false negative errors. The proposed method can be used to develop group-tailored mammographic educational strategies to help improve future mammography reader performance.

Quasicrystals (QCs) are representatives of a novel kind of material exhibiting a large number of remarkable specific properties. However, QCs are usually brittle, and crack propagation inevitably occurs in such materials. Therefore, it is of great significance to study the crack growth behaviors in QCs. In this work, the crack propagation of two-dimensional (2D) decagonal QCs is investigated by a fracture phase field method. In this method, a phase field variable is introduced to evaluate the damage of QCs near the crack. Thus, the crack topology is described by the phase field variable and its gradient. In this manner, it is unnecessary to track the crack tip, and therefore remeshing is avoided during the crack propagation. In the numerical examples, the crack propagation paths of 2D QCs are simulated by the proposed method, and the effects of the phason field on the crack growth behaviors of QCs are studied in detail. Furthermore, the interaction of the double cracks in QCs is also discussed.