Search Results (3,108)

Cobalt, a rare element in the Earth’s crust, is widely used in industries due to its hardness and antioxidant properties. It also plays a vital role in physiological functions, being a key component of vitamin B₁₂. However, excessive cobalt intake can cause health issues. Detecting cobalt ions, especially Co²⁺, in food is crucial due to potential contamination from various sources. Fluorescent probes offer high sensitivity, selectivity, a rapid response, and ease of use, making them ideal for the accurate and efficient recognition of Co²⁺ in complex samples. In this context, a highly selective fluorescent probe, 2,2′-((3-(1H-benzo[d]imidazol-2-yl)-1,2-phenylene) bis(oxy)) bis(N-(quinolin-8-yl) acetamide) (DQBM-B), was synthesized using chloroacetyl chloride, 8-aminoquinoline, 2,3-dihydroxybenzaldehyde, and benzidine as raw materials for the recognition of Co²⁺. Probe DQBM-B can exhibit fluorescence alone in DMF. However, as the concentration of Co²⁺ increased, Photoinduced Electron Transfer (PET) occurred, which quenched the original fluorescence of the probe. Probe DQBM-B shows better selectivity for Co²⁺ than other ions with high sensitivity (detection limit: 3.56 μmol L⁻¹), and the reaction reaches equilibrium within 30 min. Full article

Straw removal is a method used to reduce cadmium (Cd) concentration in contaminated farmland. Experiments in Hunan Province tested different stubble heights (0, 15, 30, 45 cm) in three Cd-polluted paddy fields with different contamination levels. The results showed that lower stubble heights resulted in larger straw biomass and more Cd removed from the field, while the residual biomass and Cd returned to the field decreased accordingly. At stubble heights of 0, 15, 30, and 45 cm, the removed straw biomass accounted for 100%, 69.19%, 48.84%, and 28.17% of the total straw biomass, respectively. The corresponding Cd removal amounts were 12.89, 7.18, 4.18, and 1.83 g ha⁻¹, which constituted 100%, 54.06%, 29.85%, and 12.54% of the total Cd accumulation in straw for the season, respectively. According to the fitted curve, the biomass of returned and removed straw was equal at a stubble height of 31 cm, while at 23 cm, the Cd return and removal amounts were balanced. Rice varieties Huanghuazhan and Nongxiang 42 had better Cd removal but risked grain Cd exceeding limits. Since Cd concentration in straw determines removal efficiency, varieties with high straw Cd accumulation and low grain Cd are more suitable for remediation, rather than high-Cd-accumulating types. Full article

This paper presents a theoretical model of a thin, penny-shaped piezoelectric actuator bonded to an isotropic thermo-elastic substrate under coupled electrical-thermal-diffusive loading. The problem is assumed to be axisymmetric, and the peeling stress of the film is neglected in accordance with membrane theory, yielding a simplified equilibrium equation for the piezoelectric film. By employing potential theory and the Hankel transform technique, the surface strain of the substrate is analytically derived. Under the assumption of perfect bonding, a governing integral equation is established in terms of interfacial shear stress. The solution to this integral equation is obtained numerically using orthotropic Chebyshev polynomials. The derived results include the interfacial shear stress, stress intensity factors, as well as the radial and hoop stresses within the system. Finite element analysis is conducted to validate the theoretical predictions. Furthermore, parametric studies elucidate the influence of material mismatch and actuator geometry on the mechanical response. The findings demonstrate that, the performance of the piezoelectric actuator can be optimized through judicious control of the applied electrical-thermal-diffusive loads and careful selection of material and geometric parameters. This work provides valuable insights for the design and optimization of piezoelectric actuator structures in practical engineering applications. Full article

Ischemic stroke is a serious disease that frequently occurs in the elderly and is characterized by a complex pathophysiology and a limited number of effective therapeutic agents. Salvianolic acid A (SAL-A) is a natural product derived from the rhizome of Salvia miltiorrhiza, which possesses diverse pharmacological activities. This study aims to investigate the effect and mechanisms of SAL-A in inhibiting ferroptosis to improve ischemic stroke. Brain injury, oxidative stress and ferroptosis-related analysis were performed to evaluate the effect of SAL-A on ischemic stroke in photochemical induction of stroke (PTS) in mice. Lipid peroxidation levels, antioxidant protein levels, tissue iron content, nuclear factor erythroid 2-related factor 2 (Nrf2), and mitochondrial morphology changes were detected to explore its mechanism. SAL-A significantly attenuated brain injury, reduced malondialdehyde (MDA) and long-chain acyl-CoA synthase 4 (ACSL4) levels. In addition, SAL-A also amplified the antioxidative properties of glutathione (GSH) when under glutathione peroxidase 4 (GPX4), and the reduction in ferrous ion levels. In vitro, brain microvascular endothelial cells (b.End.3) exposed to oxygen-glucose deprivation/reoxygenation (OGD/R) were used to investigate whether the anti-stroke mechanism of SAL-A is related to Nrf2. Following OGD/R, ML385 (Nrf2 inhibitor) prevents SAL-A from inhibiting oxidative stress, ferroptosis, and mitochondrial dysfunction in b.End.3 cells. In conclusion, SAL-A inhibits ferroptosis to ameliorate ischemic brain injury, and this effect is mediated through Nrf2. Full article

Soil salinization is a major constraint to global crop productivity, highlighting the need to identify salt tolerance genes and their molecular mechanisms. Here, we integrated mRNA and miRNA profile analyses to investigate the molecular basis of salt tolerance of an elite Brassica napus cultivar S268. Time-course RNA-seq analysis revealed dynamic transcriptional reprogramming under 215 mM NaCl stress, with 212 core genes significantly enriched in organic acid degradation and glyoxylate/dicarboxylate metabolism pathways. Combined with weighted gene co-expression network analysis (WGCNA) and RT-qPCR validation, five candidate genes (WRKY6, WRKY70, NHX1, AVP1, and NAC072) were identified as the regulators of salt tolerance in rapeseed. Haplotype analysis based on association mapping showed that NAC072, ABI5, and NHX1 exhibited two major haplotypes that were significantly associated with salt tolerance variation under salt stress in rapeseed. Integrated miRNA-mRNA analysis and RT-qPCR identified three regulatory miRNA-mRNA pairs (bna-miR160a/BnaA03.BAG1, novel-miR-126/BnaA08.TPS9, and novel-miR-70/BnaA07.AHA1) that might be involved in S268 salt tolerance. These results provide novel insights into the post-transcriptional regulation of salt tolerance in B. napus, offering potential targets for genetic improvement. Full article

In intelligent vehicular networks, the accuracy of semantic segmentation in road scenes is crucial for vehicle-mounted artificial intelligence to achieve environmental perception, decision support, and safety control. Although deep learning methods have made significant progress, two main challenges remain: first, the difficulty in balancing global and local features leads to blurred object boundaries and misclassification; second, conventional convolutions have limited ability to perceive irregular objects, causing information loss and affecting segmentation accuracy. To address these issues, this paper proposes a global–local collaborative attention module and a spider web convolution module. The former enhances feature representation through bidirectional feature interaction and dynamic weight allocation, reducing false positives and missed detections. The latter introduces an asymmetric sampling topology and six-directional receptive field paths to effectively improve the recognition of irregular objects. Experiments on the Cityscapes, CamVid, and BDD100K datasets, collected using vehicle-mounted cameras, demonstrate that the proposed method performs excellently across multiple evaluation metrics, including mIoU, mRecall, mPrecision, and mAccuracy. Comparative experiments with classical segmentation networks, attention mechanisms, and convolution modules validate the effectiveness of the proposed approach. The proposed method demonstrates outstanding performance in sensor-based semantic segmentation tasks and is well-suited for environmental perception systems in autonomous driving. Full article

Adipose tissue inflammation contributes to obesity-induced insulin resistance. However, increasing evidence shows that high BMI (obesity) is not an accurate predictor of poor metabolic health in individuals. The molecular mechanisms regulating the metabolically activated M1 macrophage phenotype in the adipose tissues leading to insulin resistance remain largely unknown. Although the Janus Kinase (Jak)/signal transducer and activator of transcription 3 (Stat3) signaling in myeloid cells are known to promote the M2 phenotype in tumors, we demonstrate here that the Jak2/Stat3 pathway amplifies M1-mediated adipose tissue inflammation and insulin resistance under metabolic challenges. Ablating Jak2 in the myeloid compartment reduces insulin resistance in obese mice, which is associated with a decrease in infiltration of adipose tissue macrophages (ATMs). We show that the adoptive transfer of Jak2-deficient myeloid cells improves insulin sensitivity in obese mice. Furthermore, the protection of obese mice with myeloid-specific Stat3 deficiency against insulin resistance is also associated with reduced tissue infiltration by macrophages. Jak2/Stat3 in the macrophage is required for the production of pro-inflammatory cytokines that promote M1 macrophage polarization in the adipose tissues of obese mice. Moreover, free fatty acids (FFAs) activate Stat3 in macrophages, leading to the induction of M1 cytokines. Silencing the myeloid cell Stat3 with an in vivo siRNA targeted delivery approach reduces metabolically activated pro-inflammatory ATMs, thereby alleviating obesity-induced insulin resistance. These results demonstrate Jak2/Stat3 in myeloid cells is required for obesity-induced insulin resistance and inflammation. Moreover, targeting Stat3 in myeloid cells may be a novel approach to ameliorate obesity-induced insulin resistance. Full article

Carbon cap-and-trade is a practical policy in guiding manufacturers to produce economic and environmental production plans. However, previous studies on carbon cap-and-trade are from a macro level to guide manufacturers to make production plans, rather than from a perspective of specific production scheduling, which leads to a lack of theoretical guidance for manufacturers to develop reasonable production scheduling schemes for specific production orders. This article investigates a specific scheduling problem in a flexible job-shop environment that considers the carbon cap-and-trade policy, aiming to provide guidance for specific production scheduling (i.e., resource allocation). In the proposed problem, carbon emissions have an upper limit. A penalty will be generated if the emissions overpass the predetermined cap. To satisfy the carbon emission cap, the manufacturer can trade carbon credits or adopt outsourcing strategy, that is, outsourcing partial orders to partners at the expense of outsourcing costs. To solve the proposed model, a novel and efficient memetic algorithm (NEMA) is proposed. An initialization method and four local search operators are developed to enhance the search ability. Numerous experiments are conducted and the results validate that NEMA is a superior algorithm in both solution quality and efficiency. Full article

Agricultural soil contaminated with mercury (Hg) poses a serious threat to ecosystems and human health. Although adding an appropriate amount of selenium (Se) can reduce the toxicity and mobility of Hg in soil, Se alone is prone to leaching into groundwater through soil runoff. Therefore, Se-enriched compound fertilizers were developed, and their remediation effect on Hg-contaminated agricultural soil was determined. The Se-enriched compound fertilizers were prepared by combining an organic fertilizer (vinegar residue, biochar, and potassium humate), inorganic fertilizer (urea, KH₂PO₄, ZnSO₄, and Na₂SeO₃), and a binder (attapulgite and bentonite). A material proportioning experiment showed that the optimal granulation rate, organic matter content, and compressive strength were achieved when using 15% attapulgite (Formulation 1) and 10% bentonite (Formulation 2). An analysis of Se-enriched compound fertilizer particles showed that the two Se-enriched compound fertilizers complied with the standard for organic–inorganic compound fertilizers (China GB 18877-2002). Compared with the control, Formulation 1 and Formulation 2 significantly reduced the Hg content in bulk and rhizosphere soil following diethylenetriaminepentaacetic acid (DTPA) extraction by 40.1–47.3% and 53.8–56.0%, respectively. They also significantly reduced the Hg content in maize seedling roots and shoots by 26.4–29.0% and 57.3–58.7%, respectively, effectively limiting Hg uptake, transport, and enrichment. Under the Formulation 1 and Formulation 2 treatments, the total and DTPA-extractable Se contents in soil and maize seedlings were significantly increased. This study demonstrated that Se-enriched compound fertilizer effectively remediates Hg-contaminated agricultural soil and can promote the uptake of Se by maize. The results of this study are expected to positively contribute to the sustainable development of the agro-ecological environment. Full article

In recent years, cross-domain few-shot learning (CDFSL) has demonstrated remarkable performance in hyperspectral image classification (HSIC), partially alleviating the distribution shift problem. However, most domain adaptation methods rely on similarity metrics to establish cross-domain class matching, making it difficult to simultaneously account for intra-class sample size variations and inherent inter-class differences. To address this problem, existing studies have introduced a class weighting mechanism within the prototype network framework, determining class weights by calculating inter-sample similarity through distance metrics. However, this method suffers from a dual limitation: susceptibility to noise interference and insufficient capacity to capture global class variations, which may lead to distorted weight allocation and consequently result in alignment bias. To solve these issues, we propose a novel class-discrepancy dynamic weighting-based cross-domain FSL (CDDW-CFSL) framework. It integrates three key components: (1) the class-weighted domain adaptation (CWDA) method dynamically measures cross-domain distribution shifts using global class mean discrepancies. It employs discrepancy-sensitive weighting to strengthen the alignment of critical categories, enabling accurate domain adaptation while maintaining feature topology; (2) the class mean refinement (CMR) method incorporates class covariance distance to compute distribution discrepancies between support set samples and class prototypes, enabling the precise capture of cross-domain feature internal structures; (3) a novel multi-dimensional feature extractor that captures both local spatial details and continuous spectral characteristics simultaneously, facilitating deep cross-dimensional feature fusion. The results in three publicly available HSIC datasets show the effectiveness of the CDDW-CFSL. Full article

Deadwood is essential for the forest ecosystem productivity and stability. A growing body of evidence indicates that deadwood-inhabiting microbes are effective decomposition agents, yet little is known about how changes in microbial communities during the initial deadwood decay. In a small forest area, we performed dense sampling from the top, middle, and bottom portions of two representative Pinus massoniana cultivars logs to track deadwood xylem microbiota shift during the initial deadwood decay. We found xylem mycobiota varied dramatically during the initial deadwood decay. Deadwood microbes might largely originate from the endophytic microbes of living trees during the initial deadwood decay. Notably, bark type is an important driving factor for xylem mycobiota changes during the initial deadwood decay. Ten upregulated metabolites were screened out by a univariate analysis approach. Moreover, our correlation analysis suggests that enriched microbes at class level was significantly correlated with the upregulated metabolites during the initial deadwood decay. Our work provides new insights into the process of mycobiota and metabolite changes during the initial deadwood decay. Full article

To address the challenges of high complexity in modeling the correlation of multi-dimensional stochastic variables and the difficulty of solving long-term scheduling models in continuous action spaces in multi-energy complementary systems, this paper proposes a long-term optimization scheduling method based on Deep Deterministic Policy Gradient (DDPG). First, an improved C-Vine Copula model is used to construct the multi-dimensional joint probability distribution of water, wind, and solar energy, and Latin Hypercube Sampling (LHS) is employed to generate a large number of water–wind–solar coupling scenarios, effectively reducing the model’s complexity. Then, a long-term optimization scheduling model is established with the goal of maximizing the absorption of clean energy, and it is converted into a Markov Decision Process (MDP). Next, the DDPG algorithm is employed with a noise dynamic adjustment mechanism to optimize the policy in continuous action spaces, yielding the optimal long-term scheduling strategy for the water–wind–solar multi-energy complementary system. Finally, using a water–wind–solar integrated energy base as a case study, comparative analysis demonstrates that the proposed method can improve the renewable energy absorption capacity and the system’s power generation efficiency by accurately quantifying the uncertainties of water, wind, and solar energy and precisely controlling the continuous action space during the scheduling process. Full article

Biochar, a porous carbonaceous material derived from the pyrolysis of biomass under oxygen-limited conditions, offers several advantages for environmental remediation, including a high specific surface area, ease of preparation, and abundant raw material sources. However, the application of pristine biochar is limited by its inherent physicochemical shortcomings, such as a lack of active functional groups and limited elemental compositions. To overcome these limitations, metal-modified biochars have garnered increasing attention. In particular, iron-manganese (Fe-Mn) modification significantly enhances the adsorption capacity, redox potential, and microbial activity of biochar, owing to the synergistic interactions between Fe and Mn. Iron-manganese-modified biochar (FM-BC) has demonstrated effective removal of heavy metals, organic matter, phosphate, and nitrate through mechanisms including mesoporous adsorption, redox reactions, complexation, electrostatic interactions, and precipitation. Moreover, FM-BC can improve soil physicochemical properties and support plant growth, highlighting its promising potential for broader environmental application. This review summarizes the preparation methods, environmental remediation mechanisms, and practical applications of FM-BC and discusses future directions in mechanism elucidation, biomass selection, and engineering implementation. Overall, FM-BC, with its tunable properties and multifunctional capabilities, emerges as a promising and efficient material for addressing complex environmental pollution challenges. Full article

Large language models (LLMs) pose significant challenges to content authentication, as their sophisticated generation capabilities make distinguishing AI-produced text from human writing increasingly difficult. Current detection methods suffer from limited information capture, poor rate–distortion trade-offs, and vulnerability to adversarial perturbations. We present CurveMark, a novel dual-channel detection framework that combines probability curvature analysis with dynamic semantic watermarking, grounded in information-theoretic principles to maximize mutual information between text sources and observable features. To address the limitation of requiring prior knowledge of source models, we incorporate a Bayesian multi-hypothesis detection framework for statistical inference without prior assumptions. Our approach embeds imperceptible watermarks during generation via entropy-aware, semantically informed token selection and extracts complementary features from probability curvature patterns and watermark-specific metrics. Evaluation across multiple datasets and LLM architectures demonstrates 95.4% detection accuracy with minimal quality degradation (perplexity increase < 1.3), achieving 85–89% channel capacity utilization and robust performance under adversarial perturbations (72–94% information retention). Full article

Our work introduces a facile and efficient metal-free [3+3] annulation approach for the synthesis of polysubstituted pyridines via the reaction between β-enaminonitriles and β,β-dichloromethyl peroxides. This strategy operates under mild conditions, demonstrating broad substrate scope and excellent functional group tolerance. Mechanistic investigations suggest that the reaction proceeds through a Kornblum–De La Mare rearrangement followed by S_NV-type C-Cl bond cleavage and intramolecular cyclization/condensation. By circumventing the need for transition metal catalysts or radical initiators, our method offers practical utility in organic synthesis and provides a new avenue for the rapid construction of complex pyridine scaffolds. Full article