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Acoustic metamaterials (AMs) composed of periodic artificial structures have extraordinary sound wave manipulation capabilities compared with traditional acoustic materials, and they have attracted widespread research attention. The sound insulation performance of thin-walled structures commonly used in engineering applications with restricted space, for example, vehicles’ body structures, and the latest studies on the sound insulation of thin-walled metamaterial structures, are comprehensively discussed in this paper. First, the definition and math law of sound insulation are introduced, alongside the primary methods of sound insulation testing of specimens. Secondly, the main sound insulation acoustic metamaterial structures are summarized and classified, including membrane-type, plate-type, and smart-material-type sound insulation metamaterials, boundaries, and temperature effects, as well as the sound insulation research on composite structures combined with metamaterial structures. Finally, the research status, challenges, and trends of sound insulation metamaterial structures are summarized. It was found that combining the advantages of metamaterial and various composite panel structures with optimization methods considering lightweight and proper wide frequency band single evaluator has the potential to improve the sound insulation performance of composite metamaterials in the full frequency range. Relative review results provide a comprehensive reference for the sound insulation metamaterial design and application.

The body of a high-speed train is a composite structure composed of different materials and structures. This makes the design of a noise-reduction scheme for a car body very complex. Therefore, it is important to clarify the key factors influencing sound insulation in the composite structure of a car body. This study uses machine learning to evaluate the key factors influencing the sound insulation performance of the composite floor of a high-speed train. First, a comprehensive feature database is constructed using sound insulation test results from a large number of samples obtained from laboratory acoustic measurements. Subsequently, a machine learning model for predicting the sound insulation of a composite floor is developed based on the random forest method. The model is used to analyze the sound insulation contributions of different materials and structures to the composite floor. Finally, the key factors influencing the sound insulation performance of composite floors are identified. The results indicate that, when all material characteristics are considered, the sound insulation and surface density of the aluminum profiles and the sound insulation of the interior panels are the three most important factors affecting the sound insulation of the composite floor. Their contributions are 8.5%, 7.3%, and 6.9%, respectively. If only the influence of the core material is considered, the sound insulation contribution of layer 1 exceeds 15% in most frequency bands, particularly at 250 and 500 Hz. The damping slurry contributed to 20% of the total sound insulation above 1000 Hz. The results of this study can provide a reference for the acoustic design of composite structures.

Exopolysaccharides (EPSs) are carbohydrate polymers that are synthesized and secreted into the extracellular during the growth of microorganisms. Bacillus thuringiensis (Bt) is a type of entomopathogenic bacterium, that produces various insecticidal proteins and EPSs. In our previous study, the EPSs produced by Bt strains were first found to enhance the toxicity of insecticidal crystal proteins against Plutella xylostella. However, the response of the intestinal bacterial communities of P. xylostella under the action of EPSs is still unelucidated. In this study, 16S rRNA amplicon sequencing was used to characterize the intestinal bacterial communities in P. xylostella treated with EPSs alone, Cry1Ac protoxin alone, and both the Cry1Ac protoxin and EPSs. Compared with the control group, alpha diversity indices, the Chao1 and ACE indices were significantly altered after treatment with EPSs alone, and no significant difference was observed between the groups treated with Cry1Ac protoxin alone and Cry1Ac protoxin + EPSs. However, compared with the gut bacterial community feeding on Cry1Ac protoxin alone, the relative abundance of 31 genera was significantly changed in the group treated with Cry1Ac protoxin and EPSs. The intestinal bacteria, through the oral of Cry1Ac protoxin and EPSs, significantly enhanced the toxicity of the Cry1Ac protoxin towards the axenic P. xylostella. In addition, the relative abundance of the 16S rRNA gene in the chloroplasts of Brassica campestris decreased after adding EPSs. Taken together, these results show the vital contribution of the gut microbiota to the Bt strain-killing activity, providing new insights into the mechanism of the synergistic insecticidal activity of Bt proteins and EPSs.

In the age of intelligence, information technology (IT) education has become the focus of attention in the education sector. However, traditional teaching methods fall short in motivating students and fostering higher-order thinking and have difficulty providing a personalized learning experience. Although AI chatbots can provide instant feedback as an innovative teaching tool, it is still challenging to fully enhance learner engagement. Based on this, this study developed a digital game-based AI chatbot system to enhance students’ learning experience through digital game-based learning strategies. This study utilized a quasi-experimental design with the experimental group using a digital game-based AI chatbot and the control group using a traditional AI chatbot. A comparison was made between the two groups concerning student learning performance in IT courses, higher-order thinking (including problem-solving, computational thinking, and creativity), learning motivation, and flow experience. In addition, the behavioral patterns of high-achieving and low-achieving students in the experimental group were analyzed. The results showed that the experimental group was significantly better than the control group in academic performance, problem-solving, computational thinking, learning motivation, and flow experience, but there was no significant difference in creativity tendency. Behavioral pattern analysis showed that high-achieving students in the experimental group showed more systematic learning strategies, while low-achieving students relied more on immediate feedback and external help, but both high- and low-achieving groups were able to actively talk to the AI chatbot and actively explore problem-solving strategies in the digital game. Therefore, AI chatbots based on digital games can be effectively used in IT courses to help students construct knowledge and develop higher-order thinking.

In conventional engineering education, issues such as the discrepancy between virtual and real environments, rigid practical operations, lack of reflective support, and a disconnect between online and offline learning prevail. Digital twin technology, with its high fidelity and real-time interaction features, presents an innovative instructional aid for engineering education. This study developed a digital twin learning system to assist instructors in implementing project-based teaching models in landscaping technology courses. To assess the effectiveness of this system, a quasi-experiment was designed. Seventy students from a vocational high school majoring in landscaping technology in China were recruited as participants. These students were divided into two groups, each consisting of 35 students, with the same teaching pace. The experimental group utilized the system to supplement the instructor’s teaching of landscaping courses, while the control group received instruction through traditional methods. The experiment lasted for eight weeks, comprising a total of 16 classes. Ultimately, the results indicated that students in the experimental group significantly outperformed those in the control group in critical thinking, cognitive load, learning experience, and academic performance. Additionally, this research examined the acceptance of learners toward using the digital twin learning system and its influencing factors based on the Technology Acceptance Model, aiming to provide insights into enhancing engineering education courses teaching effectiveness and targeted technological development.

Cashmere goats possess two types of hair follicles, with the secondary hair follicles producing valuable cashmere fiber used for textiles. The growth of cashmere exhibits a seasonal pattern arising from photoperiod change. Transcription factors play crucial roles during this process. The transcription factor, cold-shock domain, containing C2 (Csdc2) plays a crucial role in modulating cell proliferation and differentiation. Our preceding research indicated that the expression of Csdc2 changes periodically during anagen to telogen. However, the mechanisms of Csdc2 in regulating SHF growth remain unclear. Here, we found that the knockdown of Csdc2 inhibits the proliferation of dermal papilla cells. ChIP-Seq analysis showed that Csdc2 had a unique DNA binding motif in SHFs. Through conjoint analysis of ChIP-Seq and RNA-Seq, we revealed a total of 25 candidate target genes of Csdc2. Notably, we discovered a putative Csdc2 binding site within roundabout guidance receptor 2 (Robo2) on chromosome 1 of the goat genome. Furthermore, qRT-PCR and dual-luciferase reporter assay confirmed Csdc2’s positive regulatory influence on Robo2. These findings expand the research field of hair follicle transcriptional regulatory networks, offering insights into molecular breeding strategies to enhance cashmere production in goats.

Selecting an appropriate empirical interatomic potential is essential for accurately describing interatomic interactions and simulating the friction and wear of graphene. Four empirical potentials—Tersoff, REBO, AIREBO, and LCBOP—were employed in molecular dynamics simulations to study the wear process of graphene at the atomic scale. The frictional process of graphene was found to be divisible into three distinct phases: elastic deformation, plastic deformation, and wear. Using a progressively increasing load method, the critical load for each phase of graphene under four different empirical potentials was identified. Furthermore, the formation of Stone–Wales (SW) defects, bond distribution, bond breaking and healing, and wrinkle formation were analyzed in detail. Finally, a comparison was made with previous experimental results regarding friction coefficient and wear morphology.

Clematis Florida (CF) is a folk medicinal herb in the southeast of China, which is traditionally used for treating osteoarticular diseases. However, the mechanism of its action remains unclear. The present study used network pharmacology and experimental validation to explore the mechanism of CF in the treatment of rheumatoid arthritis (RA). Liquid chromatography–mass spectrometry (LC-MS/MS) identified 50 main compounds of CF; then, their targets were obtained from TCMSP, ETCM, ITCM, and SwissTargetPrediction databases. RA disease-related targets were obtained from DisGeNET, OMIM, and GeneCards databases, and 99 overlapped targets were obtained using a Venn diagram. The protein–protein interaction network (PPI), the compound–target network (CT), and the compound–potential target genes–signaling pathways network (CPS) were constructed and analyzed. The results showed that the core compounds were screened as oleanolic acid, oleic acid, ferulic acid, caffeic acid, and syringic acid. The core therapeutic targets were predicted via network pharmacology analysis as PTGS2 (COX-2), MAPK1, NF-κB1, TNF, and RELA, which belong to the MAPK signaling pathway and NF-κB signaling pathway. The animal experiments indicated that topical application of CF showed significant anti-inflammatory activity in a mouse model of xylene-induced ear edema and had strong analgesic effect on acetic acid-induced writhing. Furthermore, in the rat model of adjuvant arthritis (AA), topical administration of CF was able to alleviate toe swelling and ameliorate joint damage. The elevated serum content levels of IL-6, COX-2, TNF-α, IL-1β, and RF caused by adjuvant arthritis were reduced by CF treatment. Western blotting tests showed that CF may regulate the ERK and NF-κB pathway. The results provide a new perspective for the topical application of CF for treatment of RA.

With the increasing complexity of applications, many delay-sensitive and compute-intensive services have posed significant challenges to mobile devices. Addressing how to efficiently allocate heterogeneous network resources to meet the computing and delay requirements of terminal services is a pressing issue. In this paper, a new cooperative twin delayed deep deterministic policy gradient and deep-Q network (TD3-DQN) algorithm is introduced to minimize system latency by optimizing computational offloading and caching placement asynchronously. Specifically, the task-partitioning technique divides computing tasks into multiple subtasks, reducing the response latency. A DQN intelligent algorithm is presented to optimize the offloading path to edge servers by perceiving network resource status. Furthermore, a TD3 approach is designed to optimize the cached content in the edge servers, ensuring dynamic popularity content requirements are met without excessive offload decisions. The simulation results demonstrate that the proposed model achieves lower latency and quicker convergence in asymmetrical cloud–edge collaborative networks compared to other benchmark algorithms.

A few studies have explored the relationship between air pollution exposure and the risk of birth defects; however, the ozone-related (O3) effects on preconception and first-trimester exposures are still unknown. In this time-stratified case-crossover study, conditional logistic regressions were applied to explore the associations between O3 exposure and the risk of birth defects in Chongqing, China, and stratified analyses were constructed to evaluate the modifiable factors. A total of 6601 cases of birth defects were diagnosed, of which 56.16% were male. O3 exposure was associated with an increased risk of birth defects, and the most significant estimates were observed in the first month before pregnancy: a 10 ug/m3 increase of O3 was related to an elevation of 4.2% [95% confidence interval (CI), 3.4–5.1%]. The associations between O3 exposure and congenital malformations and deformations of the musculoskeletal system were statistically significant during almost all exposure periods. Pregnant women with lower education and income, and from rural areas, were more susceptible to O3 exposure, with the strongest odds ratios (ORs) of 1.066 (95%CI, 1.046–1.087), 1.086 (95%CI, 1.034–1.140), and 1.053 (95%CI, 1.034–1.072), respectively. Our findings highlight the health risks of air pollution exposure and raise awareness of pregnant women’s vulnerability and the susceptibility window period.

Soybean insect pests can seriously affect soybean yield, so efficient and accurate detection of soybean insect pests is crucial for soybean production. However, pest detection in complex environments suffers from the problems of small pest targets, large inter-class feature similarity, and background interference with feature extraction. To address the above problems, this study proposes the detection algorithm SP-YOLO for soybean pests based on YOLOv8n. The model utilizes FasterNet to replace the backbone of YOLOv8n, which reduces redundant features and improves the model’s ability to extract effective features. Second, we propose the PConvGLU architecture, which enhances the capture and representation of image details while reducing computation and memory requirements. In addition, this study proposes a lightweight shared detection header, which enables the model parameter amount computation to be reduced and the model accuracy to be further improved by shared convolution and GroupNorm. The improved model achieves 80.8% precision, 66.4% recall, and 73% average precision, which is 6%, 5.4%, and 5.2%, respectively, compared to YOLOv8n. The FPS reaches 256.4, and the final model size is only 6.2 M, while the number of computational quantities of covariates is basically comparable to that of the original model. The detection capability of SP-YOLO is significantly enhanced compared to that of the existing methods, which provides a good solution for soybean pest detection. SP-YOLO provides an effective technical support for soybean pest detection.

As the depth of coal seams increases and the demand for coal grows, the deformation and failure of roadways and coal pillars also intensify. To address the instability of roadways, it is crucial to study the appropriate dimensions for coal pillars. This paper focuses on the 1513 working face of the No. 5 coal seam at Anyang Coal Mine to address the issue of insufficient basis for determining coal pillar width. Through field observations and physical similarity simulations, this study examines the overlying strata failure patterns above coal pillars post-mining and the bearing structure formed by key layers. The relationship between these factors is analyzed using theoretical analysis and physical similarity simulations. A mechanical model of the coal pillar and the “hinged-hinged” overlying strata failure structure is established, analyzing the fracture characteristics of the overlying strata and the bearing structure of the coal pillar to determine the optimal coal pillar width under these conditions. The results indicate that when the coal pillar width is optimized from 25 m to 14 m, the overlying bearing layer is disrupted by the mining activities at the working face, with the lower key layer forming a “hinged” structure upon fracture. As mining progresses, the height of the overlying strata fractures gradually increases, causing the upper key layer to also fracture and form a “hinged-hinged” structure between the high and low bearing layers. According to the “three zones” development law, the height of overlying strata failure does not continue to increase indefinitely, and the coal pillar is affected by the “hinged-hinged” structure of the bearing strata. A mechanical model of the coal pillar bearing structure is established based on the fracture combination structure of the bearing strata. By calculating the load on the “hinged-hinged” structure of the overlying strata, the appropriate coal pillar width is determined to be 15 m. Theoretical calculations, physical similarity simulation experiments, and field applications show that without changing the support conditions, the deformation of the roadway is greater when the coal pillar is narrowed, compared to its original width. The maximum deformation, located 60 m ahead of the working face, increased by 41–42%, while deformation in other areas was relatively minor. This validates the reasonableness of the determined coal pillar width.

With the development of bridge engineering, U-shaped girder bridges have been applied in numerous bridge designs due to their structural characteristics. However, the U-shaped girder bridge is sensitive to solar radiation effects, leading to uneven temperature distributions that can affect the service performance of the structure. Thus, this study proposes an analysis method for the temperature distribution of U-shaped girder bridges and develops a prediction model to estimate temperature gradients. First, an improved ASHRAE clear sky model is proposed to calculate the structural shadow areas under sunlight, which provides a basis for the numerical simulation of U-shaped girder bridges under solar radiation effect. Then, a three-dimensional finite element model of the U-shaped girder bridge is established, and its correctness is verified by comparing with the actual temperature data. The temperature distribution of the U-shaped girder bridge under solar radiation is simulated using the verified model to obtain the maximum temperature difference and temperature variation characteristics. Finally, a prediction model for the temperature gradient is developed using nonlinear fitting approaches, and its accuracy is confirmed through comparison with actual data. The results indicate that the temperature distribution of the U-shaped girder bridge has minor changes along the longitudinal direction, while there are significant changes in the transverse distribution; the temperature distribution exhibits nonlinear changes in the height direction of the two side webs and the lateral direction of the bottom slab, with the maximum temperature difference reaching 17 °C; the fitting effect of the prediction model is very good, the correlation coefficients of the fitting curve and the actual data are all greater than 88%, providing a basis for the analysis of the temperature effects on U-shaped girder bridges and its application in design specifications.

This paper investigated the inhibitory effect of Sesamol (Ses) on Pestalotiopsis neglecta. The potential inhibitory mechanisms were explored by observing changes in cell morphology, measuring alterations in cell membrane-related indices, as well as energy metabolism-related indices and changes in enzyme activities related to virulence. The results show that Ses completely inhibited the growth of P. neglecta at 600 μg/mL (minimum inhibitory concentration and minimum fungicidal concentration), with an EC50 of 142 ± 13.22 μg/mL. As observed with scanning electron microscopy (SEM) and transmission electron microscopy (TEM), Ses treatment resulted in the breakage and crumpling of P. neglecta cell membrane and organelle lysis. Ergosterol content and the total lipid in P. neglecta treated with 300 μg/mL Ses was 91.52% and 54% of that in the control groups, respectively. In addition, spores were stained, increased leakage of intracellular constituents at 260 nm, and decreased extracellular pH. This suggests damage to the cell membrane integrity and permeability. Furthermore, Ses decreased the ATP levels and key enzymes in the tricarboxylic acid (TCA) cycle, indicating interference with the fungal energy metabolism. Moreover, the activities of polygalacturonase (PG) and endoglucanase (EG) of P. neglecta treated with 300 μg/mL of Ses were only 28.20% and 29.13% of that in the control groups, respectively, indicating that Ses can reduce the virulence of P. neglecta. In conclusion, our results show that Ses should be considered as a potential plant-derived fungicide due to its ability to disrupt the morphology of P. neglecta, damage cell membrane integrity and permeability in P. neglecta, interfere with energy metabolism, and reduce its virulence, ultimately affecting the fungal growth.

Quantitative assessment and trade-off/synergy analysis of land use multifunctions can effectively identify regional conflicts and dominant functions, providing decision support for promoting sustainable socio-economic and land use development. However, current research in this field still faces challenges due to coarse scale of studies and limited availability of accurate data. Taking Harbin City as a research case, this research employed an improved mutation level method, Pearson correlation analysis, and a multi-scale geographically weighted regression model to comprehensively investigate the profiling of land use multifunctions and their trade-off /synergy relationships. The comparative advantage theory was adopted to identify dominant functional zones using the NRCA index at a grid scale, in order to achieve a territorial spatial functional zoning delineation. The results showed that there were intricate trade-off/synergy relationships among production–living–ecology functions. Moreover, the types and intensity of trade-off/synergy evolved continuously with socio-economic development and regional resource endowment disparities. Due to its exceptional resource endowment, the agricultural dominated, urban dominated, and ecological dominated functional areas accounted for a significant proportion of 29%, 7%, and 26%, respectively. However, owing to the intricate trade-offs/synergies inherent in land use multifunctions, only a mere 2% (agricultural), 1% (urban), and 1% (ecological) of the area were identified as Optimization Guidance Zones. Conversely, Remediation Improvement Zones constituted the largest share at 63% of the total area, with agricultural, urban, and ecological Remediation Improvement Zones accounting for approximately 33%, 12%, and 18%, respectively. Based on the results of the type and intensity of trade-off/synergy among production–living–ecological functions, as well as the dominant zones and the integrated territorial spatial functional zoning delineation, this article provided targeted recommendations for the sustainable development of the region. These recommendations took into account both resource endowment and socio-economic development characteristics specific to the study area. The study aims to contribute to related research gaps, while providing valuable insights for other regional studies.

The utilization efficiency of conventional pesticides is relatively low in agricultural production, resulting in excessive application and environmental pollution. The efficient utilization of pesticides is crucial for promoting sustainable agriculture, and the development of nanopesticides presents a promising solution to the challenges associated with traditional pesticides. In order to explore an efficient application method for indendicarb (IDC), a star polymer nanocarrier (SPc) was employed to design and construct an efficient nanodelivery system for IDC. In this study, the morphology and physicochemical properties of the complex were determined, and its bioactivity and control efficacy were assessed using leaf-dipping and field spraying methods. The results show that IDC could be spontaneously incorporated into the hydrophobic core of SPc via hydrophobic association. This assembly disrupted the self-aggregated structure of IDC and significantly reduced its particle size to nanoscale. Furthermore, IDC emulsifiable concentrate (IDC EC) demonstrated improved adhesion to plant leaves with the aid of SPc, increasing retention from 8.083 to 10.418 mg/cm2. The LC50 (1d) of IDC EC against Plutella xylostella (Linnaeus) and Pieris rapae (Linnaeus) decreased by 6.784 and 1.931 times, respectively, with the addition of SPc. The inclusion of SPc increased the control effect of IDC EC by up to 8.28% (7d, 3000×) for P. xylostella and 12.53% (3d, 8000×) for P. rapae. This reveals that the IDC EC + SPc formulation exhibits superior insecticidal activity against these two highly destructive insect pests. This study successfully developed a novel nanodelivery system for the efficient application of IDC, which has the potential to reduce over-application and promote sustainable agricultural practices.

The increasing linguistic and cultural diversity in K-12 schools necessitates preparing preservice teachers [PSTs] to address the unique needs of Emergent Bilinguals [EBs]. This study examined elementary preservice teachers’ (PSTs) lesson plans in English Language Arts, social studies, math, and science, focusing on integrating pedagogical translanguaging strategies. The research questions were the following: (1) What translanguaging practices are evident in PSTs’ lesson plans? (2) How do PSTs integrate these practices into their instructional planning for EBs? Fifty-six PSTs enrolled in a second language methodology course at a large urban research university participated in this study. This course, taken in their senior year or during student teaching, aimed to equip PSTs with skills for delivering linguistically and culturally appropriate instructions and assessments for EBs. For the final assignment, 56 lesson plans were analyzed using deductive and inductive coding methods with the Dedoose 4.2 software. The findings reveal that while PSTs effectively identified key vocabulary and used multimodal resources to support EBs, they need more training in leveraging EBs’ home languages, strategic grouping, multilingual collaboration, and translanguaging assessments in their instructional planning.

Organic iodine is a new trace element additive that is highly efficient in regulating cell growth, function, and metabolism. This study demonstrated that organic iodine improves the growth performance and gut health of Fujian yellow rabbits. A total of 160 healthy rabbits of similar weight were randomly divided into four groups, which were treated with organic iodine (0, 0.5, 1.0, and 1.5 mg/kg) for 60 days. Our results indicated that organic iodine improved the growth performance, including significantly increased BW, ADG, and ADFI, and decreased F/G notably. Organic iodine improved the content of T3, T4, IgM, IgA, and IgM in serum, and intestinal mucosal immunity (IL-1α, IL-2, and sIgA). Organic iodine supplementation ameliorated gut morphometry and morphology, such as higher villus height and lower crypt depth. Organic iodine increased the amount of goblet cells significantly. The 0.5 mg/kg organic iodine most increased the activities of amylase, cellulase, and trypsin in caecum. Organic iodine induced more active caecum fermentation, higher NH3-N, acetic acid, propionic acid, and butyric acid, while lowering PH. In conclusion, organic iodine improved the growth performance and gut morphometry and morphology, and increased caecum enzyme activities, active caecum fermentation, and intestinal immunity of Fujian yellow rabbits.

This study was designed to compare the antioxidant, antitumor and anti-inflammatory effects of essential oils from the bark and flower of Magnolia officinalis Rehd. et Wils. Distillation extraction and steam distillation were used to extract EOs from the bark and flower. The results showed that the contents of EOs of SDE-F and SDE-B were much higher than that of SD-F and SD-B. EOs from the bark were rich in eudesmol (especially α-eudesmol) and exhibited a stronger antioxidant effect than the flower. The anti-tumor effects of SD-B and SD-F on HepG2 and MDA-MB-231 cells were better than that of SDE-B and SDE-F. The inhibitory rates of SD-B and SD-F on MDA-MB-231 cells were 59.21% and 48.27%, exceeding that of positive control 5-fluorouracil (47.04%) at 50 μg/mL. All four EOs exhibited excellent anti-inflammatory activities through the regulation of nitric oxide production and pro-inflammation cytokines in LPS-induced RAW 264.7 cells and they also remarkably suppressed the mRNA expressions of nitric oxide synthase, IL-6 and TNF-α at the concentration higher than that of positive control dexamethasone. These results indicated significant differences in the composition, and anti-inflammatory and anti-tumor activities of EOs extracted by different methods and provided a theoretical basis for their development and utilization.

The objective of this study is the development of innovative nanocurcumin-based formulations designed for the treatment and prevention of oxidative stress and diabetes. Nanocurcumin was obtained through a micronization process and subsequently encapsulated within biopolymers derived from corn starch and fenugreek mucilage, achieving encapsulation rates of 75% and 85%, respectively. Subsequently, the encapsulated nanocurcumin was utilized in the formulation of sugar-free syrups based on Stevia rebaudiana Bertoni. The stability of the resulting formulations was assessed by monitoring particle size distribution and zeta potential over a 25-day period. Dynamic light scattering (DLS) revealed a particle size of 119.9 nm for the fenugreek mucilage-based syrup (CURF) and 117 nm for the corn starch-based syrup (CURA), with polydispersity indices PDIs of 0.509 and 0.495, respectively. The dissolution rates of the encapsulated nanocurcumin were significantly enhanced, showing a 67% improvement in CURA and a 70% enhancement in CURF compared with crude curcumin (12.82%). Both formulations demonstrated excellent antioxidant activity, as evidenced by polyphenol quantification using the 2.2-diphenyl 1-pycrilhydrazyl (DPPH) assay. In the evaluation of antidiabetic activity conducted on Wistar rats, a substantial reduction in fasting blood sugar levels from 392 to 187 mg/mL was observed. The antioxidant properties of CURF in reducing oxidative stress were clearly demonstrated by a macroscopic observation of the rats’ livers, including their color and appearance.

The V-myb myeloblastosis viral oncogene homolog (MYB) family participate in various bioprocesses including development and abiotic stress responses. In the present study, we first report a 1R SHAQKYF-class MYB, MaMYBR30, in mulberry. Subcellular localization and sequence analysis indicated MaMYBR30 is located in the nucleus and belongs to a CCA-like subgroup with a conserved SHAQKYF motif. Expression profile analysis showed that MaMYBR30 is expressed in leaves and can be induced by drought and salt stress. The down-regulation of MaMYBR30 using virus-induced gene silence (VIGS) in mulberry and the overexpression of MaMYBR30 in Arabidopsis were induced to explore the function of MaMYBR30. The functional characterization of MaMYBR30 in vivo indicated that MaMYBR30 can positively regulate the resistance of mulberry to drought while negatively regulating the resistance of mulberry to salt stress. In addition, MaMYBR30 also affects flower development and reproductive growth, especially after exposure to salt stress. Weighted gene co-expression network analysis (WGCNA) primarily revealed the possible genes and signal pathways that are regulated by MaMYBR30. Our results also imply that complex molecular mechanisms mediated by MaMYBR30, including crosstalk of ion toxicity, phytohormone signal transduction, flowering development, and epigenetic modification, need to be further explored in the future.

The traditional detection methods of steel surface defects have some problems, such as a lack of feature extraction ability, sluggish detection speed, and subpar detection performance. In this paper, a YOLOv8-based DDI-YOLO model is suggested for effective steel surface defect detection. First, on the Backbone network, the extended residual module (DWR) is fused with the C2f module to obtain C2f_DWR, and the two-step approach is used to carry out the effective extraction of multiscale contextual information, and then fusing feature maps formed from the multiscale receptive fields to enhance the capacity for feature extraction. Also based on the above, an extended heavy parameter module (DRB) is added to the structure of C2f_DWR to make up for the lack of C2f’s ability to capture small-scale pattern defects between training to enhance the training fluency of the model. Finally, the Inner-IoU loss function is employed to enhance the regression accuracy and training speed of the model. The experimental results show that the detection of DDI-YOLO on the NEU-DET dataset improves the mAP by 2.4%, the accuracy by 3.3%, and the FPS by 59 frames/s compared with the original YOLOv8n.Therefore, this paper’s proposed model has a superior mAP, accuracy, and FPS in identifying surface defects in steel.

Currently, an increasing number of Chinese urban citizens are participating in daily physical exercise. Existing research has shown that physical exercise can increase life satisfaction. However, some studies also suggest that the relationship between the two is unstable. The purposes of this study are to examine physical exercise and to test its correlation with life satisfaction of urban residents in China. Data are obtained from the 2018 China Family Panel Studies, and we focus on urban residents. Our overall sample size is 7423 people, including 3641 females (49.05%) and 3782 males (50.95%), with an average age of 49.55 years old. Because the dependent variables are continuous variables, the multiple linear regression model is used for data analysis. We find that the life satisfaction of Chinese urban residents is on the high side. Our core discovery is that there is a significant positive relationship between the frequency and duration of physical exercise and life satisfaction. Our further discovery is that the frequency of physical exercise affects life satisfaction by influencing popularity and positive emotions. Similarly, the duration of physical exercise affects life satisfaction by influencing popularity and positive emotions. Whether it is the frequency or the duration of physical exercise, it can reflect the residents’ attention to physical exercise. Physical exercise habits not only promote physical health by strengthening physical fitness but also promote mental health by alleviating depression and promote social communication by increasing social activities in the Chinese context. All of these can improve people’s life satisfaction. Our research suggests that the improvement in life satisfaction not only needs the abundance of external material conditions but also needs the individual to improve their physical and mental health through physical exercise.

Osteonecrosis of the femoral head (ONFH) is a refractory orthopedic condition characterized by bone cell ischemia, necrosis, bone trabecular fracture, and clinical symptoms such as pain, femoral head collapse, and joint dysfunction that can lead to disability. The disability rate of ONFH is very high, which imposes a significant economic burden on both families and society. Steroid-associated osteonecrosis of the femoral head (SANFH) is the most common type of ONFH. However, the pathogenesis of SANFH remains unclear, and it is an urgent challenge for orthopedic surgeons to explore it. In this paper, the pathogenesis of SANFH and its related signaling pathways were briefly reviewed to enhance comprehension of the pathogenesis and prevention of SANFH.

Direct upgrading of methane into value-added products is one of the most significant technologies for the effective transformation of hydrocarbon feedstocks in the chemical industry. Both oxidative and non-oxidative methane conversion are broadly useful approaches, though the two reaction pathways are quite distinguished. Oxidative coupling of methane (OCM) has been widely studied, but suffers from the low selectivity to C2 hydrocarbons because of the overoxidation leading to undesired byproducts. Therefore, non-oxidative coupling of methane is a worthy alternative approach to be developed for the efficient, direct utilization of methane. Recently, heterogeneous catalysts comprising singly dispersed metal sites, such as single-atom catalysts (SAC) and surface organometallic catalysts (SOMCat), have been proven to be effectively active for direct coupling of methane to product hydrogen and C2 products. In this context, this review summarizes recent discoveries of these novel catalysts and provides a perspective on promising catalytic processes for methane transformation via non-oxidative coupling.

This study addresses the issue of nonfragile state estimation for memristor-based fractional-order neural networks with hybrid randomly occurring delays. Considering the finite bandwidth of the signal transmission channel, quantitative processing is introduced to reduce network burden and prevent signal blocking and packet loss. In a real-world setting, the designed estimator may experience potential gain variations. To address this issue, a fractional-order nonfragile estimator is developed by incorporating a logarithmic quantizer, which ultimately improves the reliability of the state estimator. In addition, by combining the generalized fractional-order Lyapunov direct method with novel Caputo–Wirtinger integral inequalities, a lower conservative criterion is derived to guarantee the asymptotic stability of the augmented system. At last, the accuracy and practicality of the desired estimation scheme are demonstrated through two simulation examples.

This study analyzed ancient DNA from the remains of horses unearthed from the Shihuyao tombs. These were found to date from the Han and Tang Dynasties in Xinjiang (approximately 2200 to 1100 years ago). Two high-quality mitochondrial genomes were acquired and analyzed using next-generation sequencing. The genomes were split into two maternal haplogroups, B and D, according to a study that included ancient and contemporary samples from Eurasia. A close genetic affinity was observed between the horse of the Tang Dynasty and Akhal-Teke horses according to the primitive horse haplotype G1. Historical evidence suggests that the ancient Silk Road had a vital role in their dissemination. Additionally, the matrilineal history of the Akhal-Teke horse was accessed and suggested that the early domestication of the breed was for military purposes.

In recent years, the prevalence of and mortality associated with cardiovascular diseases have been rising in most countries and regions. AF is the most common arrhythmic condition, and there are several treatment options for AF. Pulmonary vein isolation is an effective treatment for AF and is the cornerstone of current ablation techniques, which have one major limitation: even when diagnosed and treated at a facility that specializes in ablation, patients have a greater chance of recurrence. Therefore, there is a need to develop better ablation techniques for the treatment of AF. This article first compares the current cryoablation (CBA) and radiofrequency ablation (RFA) techniques for the treatment of AF and discusses the utility and advantages of the development of pulsed-field ablation (PFA) technology. The current research on PFA is summarized from three perspectives, namely, simulation experiments, animal experiments, and clinical studies. The results of different stages of experiments are summarized, especially during animal studies, where pulmonary vein isolation was carried out effectively without causing injury to the phrenic nerve, esophagus, and pulmonary veins, with higher safety and shorter incision times. This paper focuses on a review of various a priori and clinical studies of this new technique for the treatment of AF.

Storm surge is the most serious marine disaster in China, and the inundation characteristics of storm surge are the key indicators of disaster severity. Especially in the context of relative sea level rise (RSLR), it is very important to rapidly and accurately estimate the inundation characteristics of storm surge for the risk assessment and emergency management of storm surge disasters. Taking Taizhou city, Zhejiang Province, as the study area, this paper constructed an RSLR scenario library considering absolute sea level rise, land subsidence and storm surge water increase. The scenario library includes 72 scenarios, consisting of a combination of four absolute sea level rise scenarios, three land subsidence scenarios, three timescales (2030, 2050 and 2100) and two storm surge water increase scenarios. Then, an improved passive inundation method was used to predict and analyze the inundation characteristics of storm surge under each scenario. This improved method combines the advantages of the accurate active inundation method and the rapid passive inundation method, and is suitable for rapid and accurate estimation of the storm surge inundation characteristics, which can meet the needs of a storm surge disaster risk assessment and emergency response. The prediction and analysis results show that a minor RSLR can also cause a large-scale inundation in coastal areas of Taizhou. When the value of RSLR exceeds the critical value (0.6 m), it may significantly increase the expansion of the inundation area of storm surge. At a relative sea level rise of 1.57 m (extreme scenario in 2100), the inland storm surge inundation of low-risk areas may become high-risk areas. Finally, the quantitative measures for preventing storm surge disasters were put forward according to the current situation of the coast in Taizhou. Without considering storm surge and superimposed general surge, the existing 20-year return period standard seawall can effectively protect against storm surge under various scenarios. In the case of maximum water increase, it is expected that effective protection will remain until 2030, but the standard of the seawall defense will need to be improved in 2050 and 2100.

Cadmium (Cd) pollution has been rapidly increasing due to the global rise in industries. Cd not only harms the ecological environment but also endangers human health through the food chain and drinking water. Therefore, the remediation of Cd-polluted soil is an imminent issue. In this work, ryegrass and a strain of Cd-tolerant bacterium were used to investigate the impact of inoculated bacteria on the physiology and biochemistry of ryegrass and the Cd enrichment of ryegrass in soil contaminated with different concentrations of Cd (4 and 20 mg/kg). The results showed that chlorophyll content increased by 24.7% and 41.0%, while peroxidase activity decreased by 56.7% and 3.9%. In addition, ascorbic acid content increased by 16.7% and 6.3%, whereas glutathione content decreased by 54.2% and 6.9%. The total Cd concentration in ryegrass increased by 21.5% and 10.3%, and the soil’s residual Cd decreased by 86.0% and 44.1%. Thus, the inoculation of Cd-tolerant bacteria can improve the antioxidant stress ability of ryegrass in Cd-contaminated soil and change the soil’s Cd form. As a result, the Cd enrichment in under-ground and above-ground parts of ryegrass, as well as the biomass of ryegrass, is increased, and the ability of ryegrass to remediate Cd-contaminated soil is significantly improved.

Tracking multiple targets in the presence of unknown number of targets, missed detection, clutter, and noise is a challenging problem. To cope with this problem, a novel approach for generating the potential birth targets was developed, a mathematical model for multiple hypotheses was established, and an adaptive multi-hypothesis marginal Bayes filter is herein proposed in terms of the established mathematical model for multiple hypotheses and the novel birth approach. This filter delivers the existence probabilities of targets and their probability density functions. It uses multiple hypotheses to solve the data association problem to form the existence probabilities of targets and their probability density functions. To obviate the requirement for prior birth models, this filter uses the observations from two consecutive time steps to establish the birth models adaptively. Its tracking performance was tested by comparing it with other adaptive filters, showing that the proposed filter is robust, and it can obtain higher tracking accuracy than other filters.

This study introduces an advanced ensemble methodology employing lightweight neural network models for identifying severe convective clouds from FY-4B geostationary meteorological satellite imagery. We have constructed a FY-4B based severe convective cloud dataset by a combination of algorithms and expert judgment. Through the ablation study of a model ensembling combination of multiple specialized lightweight architectures—ENet, ESPNet, Fast-SCNN, ICNet, and MobileNetV2—the optimal EFNet (ENet- and Fast-SCNN-based network) not only achieves real-time processing capabilities but also ensures high accuracy in severe weather detection. EFNet consistently outperformed traditional, heavier models across several key performance indicators: achieving an accuracy of 0.9941, precision of 0.9391, recall of 0.9201, F1 score of 0.9295, and computing time of 18.65 s over the test dataset of 300 images (~0.06 s per 512 × 512 pic). ENet shows high precision but misses subtle clouds, while Fast-SCNN has high sensitivity but lower precision, leading to misclassifications. EFNet’s ensemble approach balances these traits, enhancing overall predictive accuracy. The ensemble method of lightweight models effectively aggregates the diverse strengths of the individual models, optimizing both speed and predictive performance.

In order to recover the antimony from wastewater, a custom-designed fluidized-bed reactor (FBR) was employed to treat antimony-containing wastewater. By single-factor experiments, the effects of the solution pH, the molar ratio of [TA]/[Sb3+], the seed size and dosage, the up-flow velocity (U), and the hydraulic retention time (HRT) on antimony recovery were investigated based on the antimony removal and granulation efficiency. The optimum conditions for antimony recovery were obtained at pH 9.0, the molar ratio of [TA]/[Sb3+] of 2, 6 g/L of 13–38 μm Sb2O3 as the fluidized seed, and the U and HRT of 42 m/h and 40 min, respectively; the antimony removal and granulation efficiency reached 95% and 91%, respectively. The granular products were analyzed by an X-ray polycrystalline diffractometer (XRD) and scanning electron microscopy (SEM) as cubic Sb2O3, widely used in various industries. The fluidized-bed reactor was operated continuously for 7 days, during which the antimony removal and granulation efficiency were stable at 96% and 93%, respectively. This study demonstrated the feasibility of the fluidized-bed granulation process for the recovery of antimony from wastewater. It provides a novel approach for retrieving and managing antimony-containing wastewater.

With the development of the petroleum industry, the demand for materials for oilfield equipment is becoming increasingly stringent. The strength increase brought about by time strengthening is limited in meeting the needs of equipment development. The GH3625 alloy with different strength levels can be obtained through cold deformation and heat treatment processes. A study should be carried out to further develop the potential mechanical properties of GH3625. In this study, the GH3625 alloy was cold drawn with different reductions in area (0–30%) and heat treated, and its mechanical properties were tested. The microstructure of the alloy during deformation and heat treatment was characterized by methods such as optical microscopy (OM), scanning electron microscopy (SEM), and transmission electron microscopy (TEM) based on the principles of physical metallurgy. The strength increase caused by dislocation strengthening was calculated from the dislocation density, tested by X-ray diffraction (XRD). The calculated value was compared to the measured value, elucidating the strengthening effect of cold deformation and heat treatment. The results showed that the yield strength and yield ratio of the cold-drawn alloy significantly reduced after aging at 650 °C and 760 °C. Heat treatment can make a cold-deformed material recover, ablate dislocations, and greatly reduce the dislocation density in the microstructure of the GH3625 alloy, which was the main factor in the decrease in yield strength. The work-hardening gradient of the cold-drawn material varied greatly with different reductions in area. When the reduction in area was small (10%), the hardness gradient was obvious. When it increased to 30%, the alloy was uniformly strengthened as the deformation was transmitted to the axis. This study can provide more mechanical performance options for GH3625 alloy structural components in the petrochemical industry.

This study delves into the formation, transformation, and impact on coating performance of MgZn2 and Mg2Zn11 phases in low-aluminum Zn-Al-Mg alloy coatings, combining thermodynamic simulation calculations with experimental verification methods. A thermodynamic database for the Zn-Al-Mg ternary system was established using the CALPHAD method, and this alloy’s non-equilibrium solidification process was simulated using the Scheil model to predict phase compositions under varying cooling rates and coating thicknesses. The simulation results suggest that the Mg2Zn11 phase might predominate in coatings under simulated production-line conditions. However, experimental results characterized using XRD phase analysis show that the MgZn2 phase is the main phase existing in actual coatings, highlighting the complexity of the non-equilibrium solidification process and the decisive effect of experimental conditions on the final phase composition. Further experiments confirmed that cooling rate and coating thickness significantly influence phase composition, with faster cooling and thinner coatings favoring the formation of the metastable phase MgZn2.

In the field of remote sensing image analysis, the issue of cloud interference in high-resolution images has always been a challenging problem, with traditional methods often facing limitations in addressing this challenge. To this end, this study proposes an innovative solution by integrating radiative feature analysis with cutting-edge deep learning technologies, developing a refined cloud segmentation method. The core innovation lies in the development of FFASPPDANet (Feature Fusion Atrous Spatial Pyramid Pooling Dual Attention Network), a feature fusion dual attention network improved through atrous spatial convolution pooling to enhance the model’s ability to recognize cloud features. Moreover, we introduce a probabilistic thresholding method based on pixel radiation spectrum fusion, further improving the accuracy and reliability of cloud segmentation, resulting in the “FFASPPDANet+” algorithm. Experimental validation shows that FFASPPDANet+ performs exceptionally well in various complex scenarios, achieving a 99.27% accuracy rate in water bodies, a 96.79% accuracy rate in complex urban settings, and a 95.82% accuracy rate in a random test set. This research not only enhances the efficiency and accuracy of cloud segmentation in high-resolution remote sensing images but also provides a new direction and application example for the integration of deep learning with radiative algorithms.

A novel type of titanium matrix composite (TMC) with a uniform network microstructure has been successfully fabricated by adjusting particle-size mismatch (Φ). This study can also improve the understanding of the effects of particle size on microstructure and mechanical properties, particularly in titanium matrix composites reinforced with graphite flakes (GNFs). Microstructural analysis reveals the absence of noticeable defects, and significant grain refinements have been realized. The experimental results indicate that the yield strength of the mismatched composite is improved by 24.75% compared to that of normal composites. The micro-hardness also exhibits a 10.3% increase. These enhancements can be attributed to the introduction of particle-size mismatch, the refinement of the microstructure, and the deflection of interface cracks. The presence of distorted GNF lattices in the interface micro-region of the composites primarily results from the appropriate sizing of different particles.

Coral islands and reefs are formed by the cementation of the remains of shallow water reef-building coral polyps and other reef dwelling organisms in tropical oceans. They can be divided into coral islands, coral sandbanks, coral reefs, and coral shoals, of which, Coral shoals are located below the depth datum and are not exposed even at low tide, and sometimes are distributed at water depths exceeding 30 m. Satellite images with wide spatial–temporal coverage have played a crucial role in coral island and reef monitoring, and remote sensing data with multiple platforms, sensors, and spatial and spectral resolutions are employed. However, the accurate detection of coral shoals remains challenging mainly due to the depth effect, that is, coral shoals, especially deeper ones, have very similar spectral characteristics to the sea in optical images. Here, an optical remote sensing detection method is proposed to rapidly and accurately detect the coral shoals using a deep belief network (DBN) from optical satellite imagery. The median filter is used to filter the DBN classification results, and the appropriate filtering window is selected according to the spatial resolution of the optical images. The proposed method demonstrated outstanding performance by validating and comparing the detection results of the Yinli Shoal. Moreover, the expected results are obtained by applying this method to other coral shoals in the Xisha Islands, including the Binmei Shoal, Beibianlang, Zhanhan Shoal, Shanhudong Shoal, and Yongnan Shoal. This detection method is expected to provide the coral shoals’ information rapidly once optical satellite images are available and cloud cover and tropical cyclones are satisfactory. The further integration of the detection results of coral shoals with water depth and other information can effectively ensure the safe navigation of ships.

The oriental fruit fly, Bactrocera dorsalis (Hendel), poses a significant threat to the global fruit industry, causing damage to diverse fruits like citrus, mango, and guava. Chemical pesticides have limited effectiveness, and pesticide residues and pesticide resistance are pressing issues. Therefore, it is essential to develop environmentally friendly pest control methods to address this problem. Behavior-modifying chemicals, including male attractants and intersex protein baits, play a critical role in the control of B. dorsalis. The mature host fruit serves as both an oviposition site and food source under natural conditions, making it a potential attraction source for oriental fruit flies. Orange, Citrus sinensis, is a main host of B. dorsalis, and commercial orange juice is a common attractant for the egg laying of B. dorsalis. Although it can both attract and elicit oviposition behaviors in B. dorsalis adults, its active components are still unclear. This study utilized analytical chemistry, behavioral tests, and electrophysiology to identify the active components of commercial orange juice that attract B. dorsalis, with the aim of providing a reference for the development of behavior-modifying chemical-based techniques to control B. dorsalis. Five compounds with a high abundance were identified via a GC-MS, including D-Limonene, butanoic acid ethyl ester, β-myrcene, linalool, and α-terpineol. Behavioral and electrophysiological experiments uncovered that D-Limonene was the active substance that was the main attractant in the mixture of these five substances, evoking a strong electrophysiological response in adult B. dorsalis. D-Limonene strongly attracts adult B. dorsalis only when they are sexually mature, and the attraction is not rhythmic. Olfaction plays a leading role in the attraction of D-Limonene to adult B. dorsalis, and Orco−/− mediates the perception of D-Limonene by B. dorsalis. Overall, D-Limonene is one of the key attractant compounds for B. dorsalis in the volatile compounds of commercial orange juice, offering possible support for the development of behavior-modifying chemical-based technology to control B. dorsalis in the future.

In the atmospheric system of Mars, vertical interactions are crucial, yet quantitative studies addressing this issue remain scarce. Based on simulations using the Mars PCM-LMDZ, we present the first frequency-domain quantitative analysis of the vertical interactions among Martian atmospheric dust, zonal circulation, and the migrating diurnal tide (DW1), employing Partial Directed Coherence (PDC) techniques to quantify the strength of associations between different variables. Our findings reveal a chain of influence where sub-seasonal-scale dust signals in the troposphere, through the Doppler effect of middle atmospheric zonal winds, transmit modulated energy to the DW1 in the upper mesosphere, thereby facilitating interlayer atmospheric interactions. The radiative heating from dust activities enhances the residual mean meridional circulation, which, under the influence of the Coriolis force, further accelerates the westerlies. Although gravity wave activity also contributes to the acceleration of the westerlies, its forcing generally remains below 5 m/s, which is relatively weak compared to the impact of intense dust activities in the warm scenario experiments (approximately 20 m/s). Overall, this study quantifies the interactions among atmospheric layers by means of PDC technology and analytically demonstrates how dust energy is transferred to mesospheric tides by shaping the zonal winds in between.

Mars atmospheric dynamics are crucial for understanding its climate and weather patterns, especially over plateaus. Previous studies have explored localized atmospheric heating mechanisms over Mars plateaus only to a little extent. The local atmospheric heating dynamics over the Tharsis plateau, especially during global dust storms (GDSs), have not been quantitatively analyzed before. Based on reanalysis datasets, our analysis reveals that the central highlands of Tharsis experience ~130 K diurnal temperature fluctuations, driven by intense daytime convective activity. Surface temperature and near-surface air temperatures show fluctuations approximately 25 K and 20 K higher than those at similar latitudes, respectively. We quantify a super-adiabatic lapse rate around noon that suggests strong atmospheric instability, previously unquantified in this region. By dusk, the atmosphere stabilizes, presenting a homogenized condition. At aphelion, sensible heating and adiabatic terms control the atmospheric heating, while, at perihelion, radiative and sensible heating predominate. Notably, the onset of GDS significantly alters this dynamic, reducing the ground–air temperature gap from 17 K to 5 K and enhancing diabatic heating (adiabatic cooling) in the mid-to-lower (mid-to-upper) troposphere, with increases in radiative components up to 60 W/m2.

Cordyceps militaris is one of the commercially cultivated mushrooms, valued for its medicinal and nutritional benefits. However, the fruiting body development mechanism has remained elusive. Chitin synthases (CHSs) are ubiquitous enzymes involved in the regulation of fungal growth, development and virulence. In this study, a total of eight CmChs genes were identified. Chromosomal localization analysis revealed an uneven distribution of CmCHSs across the C. militaris genome. Based on the phylogenetic analysis, 100 CHSs from Cordyceps sensu lato, encompassing C. militaris, were categorized into three divisions and seven classes, shedding light on their evolutionary relationships. There was no significant difference in the number of CHSs between ascomycetes and basidiomycetes in general (p = 0.067), as well as between pathogenic and saprotrophic fungi in general (p = 0.151 and 0.971 in Ascomycota and Basidiomycota fungi, respectively). This underscored the essential and conserved nature of these CHSs across various fungal lifestyles and ecological niches. The different transcript patterns of the eight CmChss during key life cycle stages, such as conidia germination, infection, and fruiting body development, indicated that each CHS gene may have a distinct role during specific stages of the life cycle. In conclusion, these findings indeed lay the groundwork for a further exploration of the functional roles of CHSs in the regulatory mechanism of fruiting body development in C. militaris.

To explore and compare the failure modes, deformation behaviors, and load-bearing capacities of single-edge notched (SEN) beams strengthened with carbon fiber-reinforced polymer (CFRP) and steel bars, static and dynamic three-point bending tests on both types of concrete beams have been carried out in this study. During the static tests, the electro-hydraulic servo machine served as a loading device to apply pressure to CFRP beams and reinforced concrete (RC) beams. During the impact experiments, different impact velocities were imparted by adjusting the drop hammer’s height. Thus, information regarding crack propagation, energy absorption, and deformation was obtained. The results from the static tests showed that the RC beams predominantly experienced shear failure. In contrast, the CFRP beams primarily exhibited bending–shear failure, attributed to the relatively weaker bond strength between the bars and the concrete. Impact tests were conducted at three different velocities in this study. As the impact velocity increased, both types of concrete beams transitioned from bending failure to bending–shear failure. At the lowest velocity, the difference in energy absorption between beams reinforced with different materials was insignificant during the bending process. However, at the highest velocity, CFRP beams absorbed less energy than RC beams. The study of structures’ impact failure modes and their mechanical characteristics offers valuable references for the anti-collision design and protection of structures.

Community structure is a very interesting attribute and feature in complex networks, which has attracted scholars’ attention and research on community detection. Many single-objective optimization algorithms have been migrated and modified to serve community detection problems. Due to the limitation of resolution, the final algorithm implementation effect is not ideal. In this paper, a multi-objective community detection method based on a pigeon-inspired optimization algorithm, MOPIO-Net, is proposed. Firstly, the PIO algorithm is discretized in terms of the solution space representation, position, and velocity-updating strategies to adapt to discrete community detection scenarios. Secondly, by minimizing the two objective functions of community score and community fitness at the same time, the community structure with a tight interior and sparse exterior is obtained. Finally, for the misclassification caused by boundary nodes, a mutation strategy is added to improve the accuracy of the final community recognition. Experiments on synthetic and real networks verify that the proposed algorithm is more accurate in community recognition compared to 11 benchmark algorithms, confirming the effectiveness of the proposed method.

Southwestern China is receiving excessive chemical fertilizers to meet the challenges of continuous cropping. These practices are deteriorating the soil environment and affecting tobacco (Nicotiana tabacum L.) yield and quality adversely. A novel microbially enriched biochar-based fertilizer was synthesized using effective microorganisms, tobacco stalk biochar and basal fertilizer. A field-scale study was conducted to evaluate the yield response of tobacco grown on degraded soil amended with our novel biochar-based microbial fertilizer (BF). Four treatments of BF (0%, 1.5%, 2.5% and 5%) were applied in the contaminated field to grow tobacco. The application of BF1.5, BF2.5 and BF5.0 increased the available water contents by 9.47%, 1.18% and 2.19% compared to that with BF0 respectively. Maximum growth of tobacco in terms of plant height and leaf area was recorded for BF1.5 compared to BF0. BF1.5, BF2.5 and BF5.0 increased SPAD by 13.18–40.53%, net photosynthetic rate by 5.44–60.42%, stomatal conductance by 8.33–44.44%, instantaneous water use efficiency by 55.41–93.24% and intrinsic water use efficiency by 0.09–24.11%, while they decreased the intercellular CO2 concentration and transpiration rate by 3.85–6.84% and 0.29–47.18% relative to BF0, respectively (p < 0.05). The maximum increase in tobacco yield was recorded with BF1.5 (23.81%) compared to that with BF0. The present study concludes that the application of BF1.5 improves and restores the degraded soil by improving the hydraulic conductivity and by increasing the tobacco yield.

Bacillus thuringiensis (Bt) secretes the nutritional insecticidal protein Vip3Aa11, which exhibits high toxicity against the fall armyworm (Spodoptera frugiperda). The Bt HD270 extracellular polysaccharide (EPS) enhances the toxicity of Vip3Aa11 protoxin against S. frugiperda by enhancing the attachment of brush border membrane vesicles (BBMVs). However, how EPS-HD270 interacts with Vip3Aa11 protoxin in vivo and the effect of EPS-HD270 on the toxicity of activated Vip3Aa11 toxin are not yet clear. Our results indicated that there is an interaction between mannose, a monosaccharide that composes EPS-HD270, and Vip3Aa11 protoxin, with a dissociation constant of Kd = 16.75 ± 0.95 mmol/L. When EPS-HD270 and Vip3Aa11 protoxin were simultaneously fed to third-instar larvae, laser confocal microscopy observations revealed the co-localization of the two compounds near the midgut wall, which aggravated the damage to BBMVs. EPS-HD270 did not have a synergistic insecticidal effect on the activated Vip3Aa11 protein against S. frugiperda. The activated Vip3Aa11 toxin demonstrated a significantly reduced binding capacity (548.73 ± 82.87 nmol/L) towards EPS-HD270 in comparison to the protoxin (34.96 ± 9.00 nmol/L). Furthermore, this activation diminished the affinity of EPS-HD270 for BBMVs. This study provides important evidence for further elucidating the synergistic insecticidal mechanism between extracellular polysaccharides and Vip3Aa11 protein both in vivo and in vitro.

Workload (WL) measurement is a crucial foundation for human–machine collaboration, particularly in high-stress multitasking environments such as manned vehicle operations during emergencies, where operators often experience High Workload (HWL) levels, increasing the risk of human error. To address this challenge, this study introduces a novel WL measurement method that combines Task Demand Load (TDL) and Subject Load Capacity (SLC) to quantitatively assess operator workload. This method was validated through experiments with 45 subjects using the Environmental Control and Atmospheric Regeneration (ECAR) system. The statistical results showed that as the designed WL levels increased, the Average Workload (AWL), the NASA-TLX score, and the work time percentage increased significantly, while the task accuracy and the fixation duration decreased significantly. These results also revealed the impact of WL levels on human responses (such as subjective feeling, work performance, and eye movement). In addition, very strong correlations were found between AWL measurements and NASA-TLX scores (r = 0.75, p < 0.01), task accuracy (r = −0.73, p < 0.01), and work time percentage (r = 0.97, p < 0.01). Overall, these results proved the effectiveness of the proposed method for measuring WL. On this basis, this study defined WL thresholds by integrating task accuracy with AWL calculations, providing a framework for the dynamic management of task allocation between humans and machines to maintain operators within optimal WL ranges.

Low-coverage line-belt-pattern protective forests offer significant advantages in terms of wind and sand control measures. It is important to study the windbreak effectiveness of sand-fixing forests with different spacing for the construction and optimization of plant shelterbelt configurations. The effect of plant spacing on the flow field around a row of trees was investigated using the k-ε turbulence model coupled with the porous media model. In order to accurately simplify the complex and stochastic plant constitutive features, we simplify the plant canopy to a circular platform geometry, which introduces a porous media model, and the plant trunk is simulated as a solid cylinder. The simulation results show that windbreaks only affect wind profiles up to 1.25-times the height of the tree; on the leeward side of the canopy, large-spaced shelterbelts provide greater protection in the near-wake zone, while small-spaced shelterbelts are more effective at reducing velocity in the re-equilibration zone. The flow field recovery properties of the trunk and canopy indicate that the canopy wake zone is longer. In this study, we also quantitatively analyze the relationship between average wind protection effectiveness as a function of plant spacing and streamwise distance from the leeward side of the canopy, and the given parameterized scheme shows a power exponential relationship between wind protection effectiveness and plant spacing and a logarithmic relationship with streamwise distance. This scheme can provide a predictive assessment of the effects during the implementation of the plant shelterbelt.

This paper examines the tractability of multivariate approximation problems under the normalized error criterion for a zero-mean Gaussian measure in an average-case setting. The Gaussian measure is associated with a covariance kernel, which is represented by the tensor product of one-dimensional kernels corresponding to Euler and Wiener integrated processes with non-negative and nondecreasing smoothness parameters {rd}d∈N. We give matching sufficient and necessary conditions for various concepts of tractability in terms of the asymptotic properties of the regularity parameters, except for (s, 0)-WT.

Mesoscale eddies are common in global oceans, playing crucial roles in ocean dynamics, ocean circulation, and heat transport, and their vertical structures can affect the water layers from tens to thousands of meters. In this study, we integrated sea surface height and sea surface temperature data into deep learning methods to study the mesoscale eddy subsurface temperature structure and to explore the relationship between sea surface data and eddy subsurface layers. In this study, we introduce Dual_EddyNet, a deep learning algorithm designed to invert the subsurface temperature structure of mesoscale eddies. Using this algorithm, we explore the impact of the sea surface height and sea surface temperature on the subsurface temperature structure inversion of mesoscale eddies. Furthermore, we compare different data fusion strategies, namely single-stream neural networks and dual-stream neural networks, to validate the effectiveness of the dual-stream model. To capture the interrelations among surface data and integrate feature information across various dimensions, we introduce the Triplet Attention Mechanism. The experimental results demonstrate that the proposed Dual_EddyNet performs well in reconstructing the three-dimensional structure of mesoscale eddies in the South China Sea (within a depth of 1000 m), with an inversion accuracy of 91.44% for cyclonic eddies and 95.25% for anticyclonic eddies. This algorithm provides a new method for inverting the subsurface temperatures of mesoscale eddies, and can not only be directly deployed in systems, embedded in ship moving platforms, etc., but can also provide a data reference for assimilations and numerical simulations, demonstrating its rich application potential.