Search Results (95)

Magnetotelluric (MT) sounding is a crucial technique in mineral exploration. However, MT data are highly susceptible to various types of noise. Traditional data processing methods, which rely on the assumption of signal stationarity, often result in severe distortion when suppressing non-stationary noise. In this study, we propose a novel, adaptive, and less parameter-dependent signal decomposition method for MT signal denoising, based on time–frequency domain analysis and the application of modal decomposition. The method uses Variational Mode Decomposition (VMD) to adaptively decompose the MT signal into several intrinsic mode functions (IMFs), obtaining the instantaneous time–frequency energy distribution of the signal. Subsequently, robust statistical methods are introduced to extract the independent components of each IMF, thereby identifying signal and noise components within the decomposition results. Synthetic data experiments show that our method accurately separates high-amplitude non-stationary interference. Furthermore, it maintains stable decomposition results under various parameter settings, exhibiting strong robustness and low parameter dependency. When applied to field MT data, the method effectively filters out non-stationary noise, leading to significant improvements in both apparent resistivity and phase curves, indicating its practical value in mineral exploration. Full article

With increasing urban economic development, some industrial parks and residential areas are being situated adjacent to each other, creating a potential risk of soil and groundwater contamination from the wastewater and solid waste produced by enterprises. This contamination poses a threat to the health of nearby residents. Currently, groundwater pollution prevention and control zoning in China primarily targets groundwater environmental pollution risks and does not consider the health risks associated with groundwater exposure in industry–city integration areas. Therefore, a scientific assessment of environmental risks in industry–city integration areas is essential for effectively managing groundwater pollution. This study focuses on the high frequency and rapid pace of human activities in industry–city integration areas. It combines health risk assessment and groundwater pollution simulation results with traditional groundwater pollution control classification outcomes to develop a groundwater pollution risk zoning framework specifically suited to these integrated areas. Using this framework, we systematically assessed groundwater pollution risks in a representative industry–city integration area in the middle reaches of the Yangtze River in China and delineated groundwater pollution risk zones to provide a scientific basis for local groundwater environmental management. The assessment results indicate that the total area of groundwater pollution risk control zones is 30.37 km², accounting for 19.06% of the total study area. The first-level control zone covers 5.38 km² (3.38% of the total area), while the secondary control zone spans 24.99 km² (15.68% of the total area). The first-level control zone is concentrated within industrial clusters, whereas the secondary control zone is widely distributed throughout the region. In comparison to traditional assessment methods, the zoning results derived from this study are more suitable for industry–city integration areas. This study also provides groundwater management recommendations for such areas, offering valuable insights for groundwater control in integrated industrial–residential zones. Full article

The cytokinin response regulator (ARR) gene is essential for cytokinin signal transduction, which plays a crucial role in plant growth and development. However, the functional mechanism of ARR genes in cotton leaf abscission remains incompletely understood. In this study, a total of 86 ARR genes were identified within the genome of Gossypium hirsutum. These genes were categorized into four distinct groups based on their phylogenetic characteristics, supported by analyses of gene structures and conserved protein motifs. The GhARR genes exhibited an uneven distribution across 25 chromosomes, with three pairs of tandem duplication events observed. Both segmental and tandem duplication events significantly contributed to the expansion of the ARR gene family. Furthermore, numerous putative cis-elements were identified in the promoter regions, with hormone and stress-related elements being common among all 86 GhARRs. Transcriptome expression profiling screening results demonstrated that GhARRs may play a mediating role in cotton’s response to TDZ (thidiazuron). The functional validation of GhARR16, GhARR43, and GhARR85 using virus-induced gene silencing (VIGS) technology demonstrated that the silencing of these genes led to pronounced leaf wilting and chlorosis in plants, accompanied by a substantial decrease in petiole fracture force. Overall, our study represents a comprehensive analysis of the G. hirsutum ARR gene family, revealing their potential roles in leaf abscission regulation. Full article

Layered transition metal oxide (LTMO) cathode materials for sodium-ion batteries (SIBs) have attracted extensive attention due to their unique structural stability and excellent electrochemical performance. However, their poor stability in air has significantly impeded their practical application, as exposure to moisture and carbon dioxide can lead to Na⁺ loss, phase transitions, and decreased electrochemical performance. This paper reviews the application of high-entropy strategies in sodium-ion LTMO cathode materials, focusing on the optimization of air stability and electrochemical performance through approaches including high-entropy cation regulation, P2/O3 dual-phase synergistic structures, and fluorine ion doping. Studies have shown that high-entropy design can effectively inhibit phase transitions, alleviate Jahn–Teller distortion, enhance oxygen framework stability, and markedly enhance the cycle life and rate performance of materials. Furthermore, future research directions are proposed, including the use of advanced characterization techniques to reveal failure mechanisms, the integration of machine learning to optimize material design, and the development of high-performance mixed-phase structures. High-entropy strategies provide new perspectives for the development of SIBs cathode materials with enhanced air stability, potentially promoting the practical application of SIBs in large-scale energy storage systems. Full article

This study proposes and numerically demonstrates a novel nonreciprocal electromagnetic metasurface by integrating a highly nonlinear liquid metamaterial (LMM) with a simple two-dimensional silicon dielectric grating. The transmission characteristics of the proposed structure were investigated using a full-vector finite-element method. We demonstrated that the proposed subwavelength-thickness metasurface achieves a transmission coefficient contrast of up to 0.96 between forward and backward propagation. Highly nonlinear LMMs, when employed as nonreciprocal media, significantly lower the radiation power needed to induce a nonlinear response compared to natural materials. Furthermore, we numerically analyzed the effects of the grating’s structural parameters, LMM thickness, and packing fraction on transmittance. The proposed design holds promise for applications in optical isolators. Full article

CuCrZr/GH4169 multi-material structures combine the high thermal conductivity of copper alloys with the high strength of nickel-based superalloys, making them suitable for aerospace components that require efficient heat dissipation and high strength. However, additive manufacturing of such dissimilar metals faces challenges, with each laser powder bed fusion (LPBF) and laser directed energy deposition (LDED) process having its limitations. This study employed an LPBF-LDED integrated additive manufacturing (LLIAM) approach to fabricate CuCrZr/GH4169 components. CuCrZr segments were first produced by LPBF, followed by LDED deposition of GH4169 layers using optimized laser parameters. The microstructure, composition, and mechanical properties of the fabricated components were analyzed. Results show a sound metallurgical bond at the CuCrZr/GH4169 interface with minimal porosity and cracks (typical defects at the interface), achieved by exceeding a threshold laser energy density. Elemental interdiffusion forms a 100–200 μm transition zone, with a smooth hardness gradient (97 HV0.2 to 240 HV0.2). Optimized specimens exhibit tensile failure in the CuCrZr region (234 MPa), confirming robust interfacial bonding. These findings demonstrate LLIAM’s feasibility for CuCrZr/GH4169 and underscore the importance of balancing thermal conductivity and mechanical strength in multi-material components. These findings provide guidance for manufacturing aerospace components with both high thermal conductivity and high strength. Full article

Miniaturized spectrometers employing photonic crystal cavity arrays in conjunction with computational reconstruction have gained attention as effective tools for spectral analysis. Nevertheless, achieving an optimal balance among spectral resolution, detection range, and device compactness remains challenging, particularly when complex nonlinear mappings, inter-pattern correlations, and noise interference are involved. In this work, we present ESTspecNet, a deep learning framework that integrates EfficientNet, the Swin Transformer, and spatial-channel attention mechanisms to improve spectral reconstruction accuracy. We reconstructed near-infrared spectra over an 80 nm range using a 144-unit photonic crystal cavity array, and achieved a single-peak resolution of $0.47$ nm and a double-peak resolution of $0.7$ nm. Compared to conventional methods, the proposed model demonstrates superior performance in both wide-range spectral reconstruction and fine-resolution tasks, thus highlighting its ability to effectively capture intricate spectral features and long-range dependencies, thereby advancing the reconstruction capabilities of miniaturized spectrometers. Full article

High-speed rolling bearings exhibit low friction, high mechanical efficiency, low lubrication requirements, and excellent acceleration performance. The replacement of floating ring bearings in turbochargers with rolling bearings is an important tendency for modern turbochargers. However, due to the nonlinearity in rolling bearings, the nonlinear vibration characteristics of the turbocharger rotor system need to be clearly revealed. The turbocharger rotor is modeled by a lumped mass model. The nonlinear rolling bearing model is derived using the Hertz contact theory. The vibration responses of the nonlinear system are obtained by the modified incremental harmonic balance (MIHB) method. The results demonstrate that the MIHB method significantly improves computational efficiency compared to the traditional fourth-order Runge–Kutta method for solving this class of problems while also being capable of obtaining complete solution branches of the system. The stability of the responses is determined by the Floquet theory. Based on the present rotor dynamic model, the conical mode and cylindrical mode are found. Resonance peaks at 4.5 × 10⁴ rpm (conical mode) and 1.1 × 10⁵ rpm (bending mode) are identified as critical vibration thresholds. Moreover, the vibration amplitude results show that the resonance peak of the bending mode is mainly due to the nonlinearity of the rolling bearings, which also causes the amplitude jumping phenomenon. Changing the parameters of the rolling bearing could avoid the resonance peak appearing in the working speed range. The amplitude of the system under different rotating speeds could be suppressed by choosing the appropriate parameters of the rolling bearing. Full article

Sheath blight (ShB), caused by the necrotrophic fungus Rhizoctonia solani, is one of the most serious rice diseases worldwide. In this study, we successfully grafted salicylic acid (SA) onto mesoporous silica nanoparticles through an amide-bond coupling method, forming functionalized MSN-SA nanoparticles. Physicochemical characterization showed that the MSN-SA nanoparticles were spherical, with an average particle size of approximately 30 nm and an SA loading rate of around 7.21%. The assessment of ShB resistance revealed that both SA and MSN-OH treatments were capable of inducing resistance to a certain extent. When SA and MSN-OH were applied in combination, the resistance was further augmented, indicating an additive effect between them. Intriguingly, MSN-SA treatment (50% in Lemont) exhibited a higher and more durable control efficacy compared with SA + MSN-OH treatment (33%). Moreover, field experiments demonstrated that the MSN-SA was safe for rice, and under severe disease conditions, it could recover 16.7% of the yield loss, thus highlighting its substantial application value. Further transcriptome analysis and physicochemical assays suggested that MSN-SA released SA in a slow and continuous manner, thus persistently activating the immune response, and that MSN-SA integrated the effects of SA and MSN-OH, thereby enhancing the ShB resistance. Altogether, our results provide new perspectives and a novel nanomaterial-based immune elicitor for the green control of ShB. Full article

Nanomaterials (NMs) hold significant potential for enhancing agricultural production, extending the shelf life, and maintaining the quality of postharvest vegetables and fruits. In this study, after foliar spraying with 1, 10, and 50 mg of L⁻¹ Fe-P NMs at different stages (seedling, flowering, and fruit stage), the pepper plant growth was significantly improved. In particular, the foliar application of 10 mg of L⁻¹ Fe-P NMs during the flowering stage was found to be an optimal cultivation approach to promote the growth, yield, and freshness of peppers. Compared with the control group, Fe-P NMs increased net photosynthetic rate, plant height, and fruit number by 132.7%, 40.4%, and 265.7%, respectively. The applied Fe-P NMs, at the flowering stage, altered the capsaicin metabolic pathway, upregulating the genes for the synthesis of total phenols, flavonoids, lignans, and capsaicinoids. Consequently, these metabolites, which are beneficial for maintaining the freshness of pepper fruits, were increased. Furthermore, Fe-P NMs at the flowering stage downregulated the abundance of rot-causing microorganisms (Enterobacter and Chryseobacterium) and upregulated beneficial microorganisms (Pseudomonas, Arthrobacter, Sphingobacterium, and Paenibacillus) to change the microbial community structure. This ultimately created a micro-ecological environment conducive to the preservation of pepper fruits. For comparison, during pepper fruit storage, dipping and spraying with Fe-P NM suspensions effectively delayed weight loss and enhanced the growth of beneficial bacteria. Nevertheless, the effect was less pronounced than preharvest foliar application. This study provides insights into the pre- or postharvest application of NMs for improving the preservation performance of pepper fruits. Full article

Network traffic classification is crucial for effective security management. However, the increasing prevalence of encrypted traffic and the confidentiality of protocol details have made this task more challenging. To address this issue, we propose a progressive, adaptive traffic classification method called SwiftSession, designed to achieve real-time and accurate classification. SwiftSession extracts statistical and sequential features from the first K packets of traffic. Statistical features capture overall characteristics, while sequential features reflect communication patterns. An initial classification is conducted based on the first K packets during the classification process. If the prediction meets the predefined probability threshold, processing stops; otherwise, additional packets are received. This progressive approach dynamically adjusts the required packets, enhancing classification efficiency. Experimental results show that traffic can be effectively classified by using only the initial K packets. Moreover, on most datasets, the classification time is reduced by more than 70%. Unlike existing methods, SwiftSession enhances the classification speed while ensuring classification accuracy. Full article

Complex scenarios pose challenges to tasks in computer vision, including image fusion, object detection, and image-to-image translation. On the one hand, complex scenarios involve fluctuating weather or lighting conditions, where even images of the same scenarios appear to be different. On the other hand, the large amount of textural detail in the given images introduces considerable interference that can conceal the useful information contained in them. An effective solution to these problems is to use the complementary details present in multi-modal images, such as visible-light and infrared images. Visible-light images contain rich textural information while infrared images contain information about the temperature. In this study, we propose a multi-modal registered dataset for complex scenarios under various environmental conditions, targeting security surveillance and the monitoring of low-slow-small targets. Our dataset contains 30,819 images, where the targets are labeled as three classes of “person”, “car”, and “drone” using Yolo format bounding boxes. We compared our dataset with those used in the literature for computer vision-related tasks, including image fusion, object detection, and image-to-image translation. The results showed that introducing complementary information through image fusion can compensate for missing details in the original images, and we also revealed the limitations of visual tasks in single-modal images with complex scenarios. Full article

Camera arrays typically use image-stitching algorithms to generate wide field-of-view panoramas, but parallax and color differences caused by varying viewing angles often result in noticeable artifacts in the stitching result. However, existing solutions can only address specific color difference issues and are ineffective for pinhole images with parallax. To overcome these limitations, we propose a parallax-tolerant weakly supervised pixel-wise deep color correction framework for the image stitching of pinhole camera arrays. The total framework consists of two stages. In the first stage, based on the differences between high-dimensional feature vectors extracted by a convolutional module, a parallax-tolerant color correction network with dynamic loss weights is utilized to adaptively compensate for color differences in overlapping regions. In the second stage, we introduce a gradient-based Markov Random Field inference strategy for correction coefficients of non-overlapping regions to harmonize non-overlapping regions with overlapping regions. Additionally, we innovatively propose an evaluation metric called Color Differences Across the Seam to quantitatively measure the naturalness of transitions across the composition seam. Comparative experiments conducted on popular datasets and authentic images demonstrate that our approach outperforms existing solutions in both qualitative and quantitative evaluations, effectively eliminating visible artifacts and producing natural-looking composite images. Full article

In this paper, we present a Low-Rank Gradient Estimator (LoGE) to accelerate the finetune-time computation of transformers, especially large language models (LLMs). Unlike Parameter-Efficient Fine-Tuning (PEFT) methods, which primarily aim to minimize the number of fine-tuning parameters, LoGE also significantly reduces the computational load of activation gradient calculations by decomposing pre-trained weights and utilizing low-rank matrices during the backward pass. Our approach includes an effective solution for identifying sensitive and important latent subspaces in large models before training with downstream datasets. As LoGE does not alter the network structure, it can be conveniently integrated into existing models. We validated LoGE’s efficacy through comprehensive experiments across various models on various tasks. For the widely used LLaMA model equipped with LoRA, LoGE achieves up to a 1.3× speedup while maintaining graceful accuracy. Full article

Antlers are the sole mammalian organs capable of continuous regeneration. This distinctive feature has evolved into various biomedical models. Research on mechanisms of antler growth, development, and ossification provides valuable insights for limb regeneration, cartilage-related diseases, and cancer mechanisms. Here, ribonucleic acid sequencing (RNA-seq) and four-dimensional data-independent acquisition (4D DIA) technologies were employed to examine gene and protein expression differences among four tissue layers of the Chinese milu deer antler: reserve mesenchyme (RM), precartilage (PC), transition zone (TZ), cartilage (CA). Overall, 4611 differentially expressed genes (DEGs) and 2388 differentially expressed proteins (DEPs) were identified in the transcriptome and proteome, respectively. Among the 828 DEGs common to both omics approaches, genes from the collagen, integrin, and solute carrier families, and signaling molecules were emphasized for their roles in the regulation of antler growth, development, and ossification. Bioinformatics analysis revealed that in addition to being regulated by vascular and nerve regeneration pathways, antler growth and development are significantly influenced by numerous cancer-related signaling pathways. This indicates that antler growth mechanisms may be similar to those of cancer cell proliferation and development. This study lays a foundation for future research on the mechanisms underlying the rapid growth and ossification of antlers. Full article