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We investigate the space-time decay rates of solutions to the 3D Cauchy problem of the compressible Oldroyd-B system with diffusive properties and without viscous dissipation. The main novelties of this paper involve two aspects: On the one hand, we prove that the weighted rate of $ k $-th order spatial derivative (where $ 0\leq k\leq3 $) of the global solution $ (\rho, u, \eta, \tau) $ is $ t^{-\frac{3}{4}+\frac{k}{2}+\gamma} $ in the weighted Lebesgue space $ L^2_{\gamma} $. On the other hand, we show that the space-time decay rate of the $ m $-th order spatial derivative (where $ m \in\left [0, 2\right] $) of the extra stress tensor of the field in $ L^2_{\gamma } $ is $ (1+t)^{-\frac{5}{4}-\frac{m}{2}+\gamma} $, which is faster than that of the velocity. The proofs are based on delicate weighted energy methods and interpolation tricks.

Graphene and its derivatives are widely used in tissue-engineering scaffolds, especially in the form of hydrogels. This is due to their biocompatibility, electrical conductivity, high surface area, and physicochemical versatility. They are also used in tissue engineering. Tissue engineering is suitable for 3D printing applications, and 3D printing makes it possible to construct 3D structures from 2D graphene, which is a revolutionary technology with promising applications in tissue and organ engineering. In this review, the recent literature in which graphene and its derivatives have been used as the major components of hydrogels is summarized. The application of graphene and its derivative-based hydrogels in tissue engineering is described in detail from different perspectives.

Colorectal cancer (CRC) is one of the most common malignant tumors of the digestive tract. In recent years, the age at which CRC is diagnosed has been trending younger on a global scale. Obesity, as a worldwide epidemic, is on the rise in most countries due to changes in dietary habits and lifestyles. Concurrently, with increased screening efforts and growing public awareness of health, the incidence of CRC in older adults is gradually declining, while the incidence in younger and middle-aged individuals is steadily increasing. This seems to indicate a strong association between overweight and obesity and a younger age of colorectal cancer diagnosis. Currently, with advancements in research on the relationship between early life (childhood and adolescence) body weight and CRC, new insights have been gained regarding the role of high BMI (overweight and obesity) in the occurrence and development of CRC, as well as its impact at different early life stages. Adolescence appears to be a particularly critical period. Therefore, this review will summarize the relationship between early life high BMI and CRC, aiming to provide a reference for clinical and basic research as well as CRC prevention. Significance This study on the relationship between early life high BMI and the development of CRC reveals the role of high BMI during childhood and adolescence in the occurrence and progression of CRC. It suggests the importance of restoring normal weight or reducing weight in individuals with high BMI early in life for the prevention of colorectal cancer.

As renewable energy continues to grow, more inverter-based distributed generators are being integrated into power grids, in which virtual synchronous generator (VSG) is widely adopted to regulate system voltage and stabilize frequency. However, grid voltage imbalances can cause serious oscillations in active power and system frequency, thus severely limiting the effectiveness of VSGs. Consequently, this study proposes an adaptive control strategy to enhance the dynamic performance of VSG under an imbalanced grid voltage. Compared to conventional VSG control methods, the proposed approach enables dynamic adjustment of the inertia and damping coefficients based on the output angular frequency oscillations, so as to shorten the settling time and reduce active power overshoot. The error-driven adaptive mechanism forms a closed-loop control, which effectively keeps the system stable regardless of parameter variations. Additionally, it combines with balanced output current (BOC), constant active power (CAP), and constant reactive power (CRP) control modes to further improve effectiveness. Finally, verifications have been conducted in the MATLAB/SIMULINK and hardware-in-the-loop (HIL) environment to demonstrate the superiority and feasibility of the proposed method.

The distribution characteristics of the microbial community in rhizosphere soils of different altitudinal gradients were explored to uncover ecological factors affecting microbial community composition. In this study, the community variations of bacteria and fungi in the rhizosphere soil of Chrysanthemum indicum L. were analyzed. Samples were distributed along an altitudinal gradient of 300–1500 m above sea level in the Fuling watershed of the Three Gorges Reservoir area, China. The analysis was conducted using Illumina MiSeq high-throughput sequencing and bioinformatics analyses. Through correlation analysis with ecological factors, the altitude distribution pattern and driving factors of soil microbial diversity in the mountainous and hilly region of Chongqing were explored. According to the results, the richness and diversity of rhizosphere soil bacteria increased with altitude, while fungi were the richest and most diverse at an altitude of 900 m. The composition of the microbial community differed among different altitudes. Actinobacteria, Proteobacteria, Acidobacteriota, Chloroflexi, Bacteroidota, Ascomycota, unclassified_k_Fungi, Basidiomycota, and Mortierellomycota dominated the microbial community in rhizosphere soil. Correlation analysis showed that the distribution of rhizosphere soil microbial communities correlated with soil ecological factors at different altitudes. Moisture, pH, total nitrogen, total potassium, available potassium, urease, and catalase were significantly positively correlated with rhizosphere soil bacterial α-diversity, while their correlations with fungi were not significant. Variation partition analysis showed that the combined effects of soil physical and chemical factors, enzyme activity, and microbial quantity regulated bacterial community structure and composition. Their combined contributions (19.21%) were lower than the individual effects of soil physical and chemical factors (48.49%), enzyme activity (53.24%), and microbial quantity (60.38%). The effects of ecological factors on fungal communities differed: While the soil physical and chemical factors (44.43%) alone had a clear effect on fungal community structures, their combined contributions had no apparent effect. The results of this study not only contribute to a deeper understanding of the impact mechanism of altitude gradient on the diversity of rhizosphere soil microbial communities, but also provide a scientific basis for the protection and management of mountainous and hilly ecosystems. It lays a foundation for the future exploration of the relationship between microbial communities and plant–soil interactions.

This study assessed the presence of potentially toxic elements (PTEs) in China’s northeastern black soil belt, an area with limited prior research. We collected 304 soil samples (0–20 cm) from Gonghe Town, Hailun City, and analyzed the PTE contamination degree using the single-factor pollution index and Nemerow pollution index. The results demonstrated that the mean concentrations of arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), mercury (Hg), nickel (Ni), lead (Pb), and zinc (Zn) were 11.16, 0.11, 65.29, 22.56, 0.03, 27.07, 26.09, and 66.01 mg/kg, respectively. Source apportionment was conducted via correlation analysis, principal component analysis, and positive matrix factorization, identifying four main sources: natural (33.2%), irrigation (29.5%), fuel (23.4%), and fertilizer (13.2%). The ecological risk index indicated a slight ecological risk, while the human health risk showed that non-carcinogenic risks were negligible and carcinogenic risks were acceptable. Our findings emphasize the need to prioritize controlling PTEs from fertilizer, particularly cadmium, and to a lesser extent, irrigation and fuel sources, focusing on As, Pband Hg. This research provides critical insights for policymakers aiming to manage PTE contamination in black soils.

Abstract Mechanical metamaterials are often designed with particular properties for specific load‐bearing functions. Alternatively, this study aims to create a class of active lattice metamaterials, dubbed self‐activated solids, that can learn desired stiffness tensors from the elastic deformations they experienced, a crucial feature to improve the performance, efficiency, and functionality of materials. Artificial adaptive matters that combine sensory, control, and actuation elements can offer appealing solutions. However, challenges still remain: The designs will rely on accurate off‐line and global computations, as well as intricate coordination among individual elements. Here, a simple online and local learning strategy is initiated based on contrastive Hebbian learning to gradually guide self‐activated solids to possess sought‐after stiffness tensors autonomously and reversibly. During learning, the bond stiffness of the active lattice varies depending only on its local strain. The numerical tests show that the self‐activated solid can not only achieve the desired bulk, shear, and coupling moduli but also manifest uni‐mode and bi‐mode extremal materials by itself after experiencing the corresponding elastic deformations. Further, the self‐activated solid can also achieve the desired time‐varying moduli when exposed to temporally different loads. The design is applicable to any lattice geometries and is resistant to damage and instabilities. The material design approach and the physical learning strategy suggested can benefit the design of autonomous materials, physical learning machines, and adaptive robots.

Potatoes, as a high-nitrogen (N)-demand crop, are strongly influenced by both the quantity and form of N supply. Previous studies have demonstrated that applying nitrate N prior to tuber formation and ammonium N post-tuber formation can substantially enhance potato yields and improve N fertilizer use efficiency. However, the ammonium N introduced into the soil undergoes nitrification, creating challenges in aligning the N supply form with the needs of potatoes. This study explored novel N regulation strategies aimed at augmenting potato yields and improving N fertilizer use efficiency. Two field experiments were conducted from 2020 to 2022. Experiment 1 involved four N gradients, namely no N, 150 kg N ha−1, 300 kg N ha−1, and 450 kg N ha−1. Soil samples were collected regularly to determine the transformation patterns of soil ammonium N during potato growth. Experiment 2 included three N management practices: farmer practice (Con), ''nitrate followed by ammonium'' with nitrification inhibitor (N–NI), and optimization (the soil ammonium N transformation-based split application of N fertilizer, Opt). The potato yield and N fertilizer use efficiency were compared to assess the performance of the optimized strategy. The results showed that 90 % of the ammonium N transformed 20 days after the basal dressing of N. When N fertilizer was applied as top dressing during the tuber formation and bulking stages, more than 90 % of ammonium N was transformed after 10 days. The optimized strategy resulted in a 20 % increase in potato yield, a 20 % increase in N fertilizer partial factor productivity, and a 12–20 % reduction in residual inorganic N in the 0–60 cm soil layer. This suggests that ammonium N applied as base fertilizer exhibits a relatively slow transformation rate, while applying ammonium N as top dressing during the tuber formation and bulking stages accelerates the transformation rate. The split application of ammonium N based on soil ammonium N transformation patterns can improve the alignment between the N supply form with the specific demands of potatoes.

Wearable sensors for sweat trace metal monitoring have the challenges of effective sweat collection and the real-time recording of detection signals. The existing detection technologies are implemented by generating enough sweat through exercise, which makes detecting trace metals in sweat cumbersome. Generally, it takes around 20 min to obtain enough sweat, resulting in dallied and prolonged detection signals that cannot reflect the endogenous fluctuations of the body. To solve these problems, we prepared a multifunctional hydrogel as an electrolyte and combined it with a flexible patch electrode to realize real-time monitoring of sweat Zn2+. Such hydrogel has magnetic and porous properties, and the porous structure of hydrogel enables a fast absorption of sweat, and the magnetic property of the addition of fabricated Fe3O4 NPs not only improves the conductivity but also ensures the adjustable internal structures of the hydrogel. Such a sensing platform for sweat Zn2+ monitoring shows a satisfied linear relationship in the concentration range of 0.16–16 µg/mL via differential pulsed anodic striping voltammetry (DPASV) and successfully detects the sweat Zn2+ of four volunteers during exercise and resting, displaying a promising path for commercial application.

Friction and wear are the main failure sources of face seals. When the surfaces of sealing rings exhibit greater roughness, the level of friction might increase and lead to sealing failure. Therefore, in this paper, based on the elastic contact hypothesis of rough and wavy surfaces and the influence of temperature on the elastic modulus of materials, a thermoelastic contact lubrication model of a gas-lubricated end seal is established. The novelty and advantage of this study is that it takes the effect of surface roughness into consideration during thermoelastic analysis of gas-lubricated seals. The film pressure, temperature, contact force and deformation of a gas spiral groove-faced seal are numerically determined. The influence of surface roughness on the contact distribution, deformation and temperature of the end-face seal at different speeds and pressures is analyzed. The film thickness increases as the rotational speed increases from 1 rpm to 2000 rpm, while the contact pressure sharply decreases from 0.25 kPa to 0. The analysis shows that the roughness contact mainly happens on the inner side of the rings due to convergent distortion of the seal faces, which easily causes partial wear of the seal faces. Moreover, it can also be found that the spiral grooves on the sealing surface can produce obvious hydrodynamic pressure effect due to the function of shear speed when the speed increases to 2000 rpm, while the film temperature increases from 293.3 K to about 306 K. The greater surface roughness results in a larger temperature rise under low-rotational-speed and lower-seal-pressure conditions, which further increases the risk of severe wear or even failure of the seal faces.

Landslides are common hazardous geological events, and accurate and efficient landslide identification methods are important for hazard assessment and post-disaster response to geological disasters. Deep learning (DL) methods based on remote sensing data are currently widely used in landslide identification tasks. The recently proposed segment anything model (SAM) has shown strong generalization capabilities in zero-shot semantic segmentation. Nevertheless, SAM heavily relies on user-provided prompts, and performs poorly in identifying landslides on remote sensing images. In this study, we propose a SAM-based cross-feature fusion network (SAM-CFFNet) for the landslide identification task. The model utilizes SAM’s image encoder to extract multi-level features and our proposed cross-feature fusion decoder (CFFD) to generate high-precision segmentation results. The CFFD enhances landslide information through fine-tuning and cross-fusing multi-level features while leveraging a shallow feature extractor (SFE) to supplement texture details and improve recognition performance. SAM-CFFNet achieves high-precision landslide identification without the need for prompts while retaining SAM’s robust feature extraction capabilities. Experimental results on three open-source landslide datasets show that SAM-CFFNet outperformed other comparative models in terms of landslide identification accuracy and achieved an intersection over union (IoU) of 77.13%, 55.26%, and 73.87% on the three datasets, respectively. Our ablation studies confirm the effectiveness of each module designed in our model. Moreover, we validated the justification for our CFFD design through comparative analysis with diverse decoders. SAM-CFFNet achieves precise landslide identification using remote sensing images, demonstrating the potential application of the SAM-based model in geohazard analysis.

The plant hormone jasmonic acid plays an important role in plant growth and development, participating in many physiological processes, such as plant disease resistance, stress resistance, organ development, root growth, and flowering. With the improvement in living standards, people have higher requirements regarding the quality of vegetables. However, during the growth process of vegetables, they are often attacked by pests and diseases and undergo abiotic stresses, resulting in their growth restriction and decreases in their yield and quality. Therefore, people have found many ways to regulate the growth and quality of vegetable crops. In recent years, in addition to the role that JA plays in stress response and resistance, it has been found to have a regulatory effect on crop quality. Therefore, this study aims to review the jasmonic acid accumulation patterns during various physiological processes and its potential role in vegetable development and quality formation, as well as the underlying molecular mechanisms. The information provided in this manuscript sheds new light on the improvements in vegetable yield and quality.

Plant species loss, driven by global changes and human activities, can have cascading effects on other trophic levels, such as arthropods, and alter the multitrophic structure of ecosystems. While the relationship between plant diversity and arthropod communities has been well-documented, few studies have explored the effects of species composition variation or plant functional groups. In this study, we conducted a long-term plant removal experiment to investigate the impact of plant functional group loss (specifically targeting tall grasses and sedges, as well as tall or short forbs) on arthropod diversity and their functional groups. Our findings revealed that the removal of plant functional groups resulted in increased arthropod richness, abundance and the exponential of Shannon entropy, contrary to the commonly observed positive correlation between plant diversity and consumer diversity. Furthermore, the removal of different plant groups had varying impacts on arthropod trophic levels. The removal of forbs had a more pronounced impact on herbivores compared to graminoids, but this impact did not consistently cascade to higher-trophic arthropods. Notably, the removal of short forbs had a more significant impact on predators, as evidenced by the increased richness, abundance, the exponential of Shannon entropy, inverse Simpson index and inverse Berger-Parker index of carnivores and abundance of omnivores, likely attributable to distinct underlying mechanisms. Our results highlight the importance of plant species identity in shaping arthropod communities in alpine grasslands. This study emphasizes the crucial role of high plant species diversity in controlling arthropods in natural grasslands, particularly in the context of plant diversity loss caused by global changes and human activities.

Early discovery and monitoring of the active deformation areas of potential landslides are important for geohazard risk prevention. The objective of the study is to propose a one-step strategy for automatically mapping the active deformation areas of potential landslides from a Sentinel-1 SAR dataset. First, we built a generalized convolutional neural network (CNN) based on activity and topographic characteristics. Second, we conducted a comparative analysis of the performance of various multi-channel combiners for detecting the active deformation areas of the potential landslides. Third, we verified the transferability of the pretrained CNN model for an unknown region. We found that by incorporating topographic characteristics into a generalized convolutional neural network, we were able to enhance the accuracy of identifying the active deformation areas of potential landslides, rapidly mapping these areas. The methodology is robust and efficient, and it has the capability to automatically detect the active deformation areas of potential landslides, even in unknown or unfamiliar regions. This product can facilitate automated pipelines, updating and mapping active deformation areas for final users who are not InSAR experts. This implementation can be used for providing support to risk management activities.

Landslide susceptibility assessment (LSA) is an essential tool for landslide hazard warning. The selection of earthquake-related factors is pivotal for seismic LSA. In this study, Newmark displacement (Dn) is employed as the earthquake-related factor, providing a detailed representation of seismic characteristics. On the algorithmic side, a dual-channel convolutional neural network (CNN) model is built, and the last classification layer is replaced with two machine learning (ML) models to facilitate the extraction of deeper features related to landslide development. This research focuses on Beichuan County in Sichuan Province, China. Fifteen landslide predisposing factors, including hydrological, geomorphic, geological, vegetation cover, anthropogenic, and earthquake-related features, were extensively collected. The results demonstrate some specific issues. Dn outperforms conventional earthquake-related factors such as peak ground acceleration (PGA) and Arias intensity (Ia) in capturing seismic influence on landslide development. Under the same conditions, the OA improved by 5.55% and AUC improved by 0.055 compared to the PGA; the OA improved by 3.2% and AUC improved by 0.0327 compared to the Ia. The improved CNN outperforms ML models. Under the same conditions, the OA improved by 4.69% and AUC improved by 0.0467 compared to RF; the OA improved by 4.47% and AUC improved by 0.0447 compared to SVM. Additionally, historical landslides validate the reasonableness of the landslide susceptibility maps. The proposed method exhibits a high rate of overlap with the historical landslide inventory. The proportion of historical landslides in the very high and high susceptibility zones exceeds 87%. The method not only enhances accuracy but also produces a more fine-grained susceptibility map, providing a reliable basis for early warning of seismic landslides.

Orange juice is highly popular among consumers for its unique taste, flavor, and nutritional value. However, orange juice is susceptible to browning, resulting in loss of quality. The browning of orange juice is complex, especially non-enzymatic browning (NEB), and its main mechanism has not been reported. In this study, the browning of concentrated orange juice (COJ) was determined to be NEB during storage by detecting polyphenol oxidase and peroxidase activities. A simulation system based on the main components of COJ was established to explore the main mechanisms of NEB. The browning products (5-HMF, A294) and browning indexes (BI values, ΔE) of the simulation system showed that the contribution of the Maillard reaction (0.085) to browning was slightly greater than ascorbic acid degradation (0.078) and 28 times greater than caramelization reaction (0.003). This suggests that ascorbic acid degradation and the Maillard reaction are the main factors to NEB of COJ, while the caramelization reaction and phenolic oxidation can be ignored. Moreover, the browning in the early stage of storage was mainly caused by the degradation of ascorbic acid, and the later stage was Maillard reaction. In summary, the browning of COJ during storage is mainly caused by ascorbic acid degradation and Maillard reaction with different stages of action. Developing browning inhibition strategies for COJ should focus on the main browning factors at different stages to regulate browning more precisely and protect the quality. [ABSTRACT FROM AUTHOR]

Inverse sliding spotlight synthetic aperture radar (SAR) is not as high as sliding spotlight SAR in azimuth resolution. Its azimuth resolution is constant, and it cannot meet the needs of multiple different azimuth resolutions. In order to solve this problem, a spaceborne inverse sliding spotlight SAR for nonuniform scanned scene is proposed. The design of the proposed inverse sliding spotlight SAR includes two parts: the design of the adaptive azimuth beam steering law and the design of the imaging algorithm. In the first part, the design of the adaptive azimuth beam steering law is based on multiple specific azimuth resolution requirements and the parameters of scanned scene. In the second part, the design of the imaging algorithm for the proposed inverse sliding spotlight SAR consists of three steps: filtering processing, phase compensation and upsampling processing, and image formation. Compared with the conventional inverse sliding spotlight SAR, the proposed inverse sliding spotlight SAR can achieve different azimuth resolution requirements for scanning targets at different positions in the scanned scene. Finally, the correctness of the proposed inverse sliding spotlight SAR is verified by simulation experiment and UAV SAR experiment. [ABSTRACT FROM AUTHOR]

Building advanced catalyst supports to improve the sluggish oxygen reduction reaction (ORR) kinetics is essential to enhance fuel cell performance. However, constructing carbon supports with ideal pore structures that can reduce the mass transport resistance of oxygen while stabilising small nanoparticles (NPs) remains a challenge. Herein, we report a facile cooperative template strategy to construct a porous carbon (p-ZIF-8-C), which features a high specific surface area (1029.7 m² g-1) and a unique mesoporous distribution (5-15 nm). When serving as a catalyst support, Pt NPs with an average size of ~2.3 nm are obtained in the as-synthesized Pt/p-ZIF-8-C catalyst. Besides exhibiting a half-wave potential up to 0.915 V and excellent durability, Pt/p-ZIF-8-C achieves a significantly enhanced performance in H2-air fuel cells with a Pt loading of 0.1 mgPt cm-2 in the cathode (maximum power density of 801 mW cm-2 and 1315 mA cm-2 at 0.6 V). This work offers guidance for the development of advanced supports in the future. [ABSTRACT FROM AUTHOR]