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The remediation of soil contaminated by heavy metals has long been a concern of academics. This is due to the fact that heavy metals discharged into the environment as a result of natural and anthropogenic activities may have detrimental consequences for human health, the ecological environment, the economy, and society. Metal stabilization has received considerable attention and has shown to be a promising soil remediation option among the several techniques for the remediation of heavy metal-contaminated soils. This review discusses various stabilizing materials, including inorganic materials like clay minerals, phosphorus-containing materials, calcium silicon materials, metals, and metal oxides, as well as organic materials like manure, municipal solid waste, and biochar, for the remediation of heavy metal-contaminated soils. Through diverse remediation processes such as adsorption, complexation, precipitation, and redox reactions, these additives efficiently limit the biological effectiveness of heavy metals in soils. It should also be emphasized that the effectiveness of metal stabilization is influenced by soil pH, organic matter content, amendment type and dosage, heavy metal species and contamination level, and plant variety. Furthermore, a comprehensive overview of the methods for evaluating the effectiveness of heavy metal stabilization based on soil physicochemical properties, heavy metal morphology, and bioactivity has also been provided. At the same time, it is critical to assess the stability and timeliness of the heavy metals' long-term remedial effect. Finally, the priority should be on developing novel, efficient, environmentally friendly, and economically feasible stabilizing agents, as well as establishing a systematic assessment method and criteria for analyzing their long-term effects.

Soil carbon and nitrogen are essential factors for agricultural production and climate changes. A total of 106 soil samples from three agricultural lands (including two rice fields and one sugarcane field) and four non-agricultural lands (including two forest lands, one wasteland and one built-up land) in the Mun River Basin were collected to determine soil carbon, nitrogen, soil pH, soil particle sizes and explore the influence of pH and soil texture on soil C and N. The results show that total organic carbon (TOC) and nitrogen (TON) contents in topsoil (TOC: 2.78 ~ 18.83 g kg −1 ; TON: 0.48 ~ 2.05 g kg −1 ) are much higher than those in deep soil (TOC: 0.35 ~ 6.08 g kg −1 ; TON: <0.99 g kg −1 ). In topsoil, their contents of forest lands and croplands (TOC: average 15.37 g kg −1 ; TON: average 1.29 g kg −1 ) are higher than those of other land uses (TOC: average 5.28 g kg −1 ; TON: average 0.38 g kg −1 ). The pH values range from 4.2 to 6.1 in topsoil, and with increase in soil depth, they tend to increase and then decrease. Soil carbon, nitrogen and the C/N (TC/TN ratio) are negatively correlated with soil pH, demonstrating that relatively low pH benefits the accumulation of organic matter. Most soil samples are considered as sandy loam and silt loam from the percentages of clay, silt and sand. For soil profiles below 50 cm, the TOC and TON average contents of soil samples which contain more clay and silt are higher than those of other soil samples.

Heterogeneous interface design to boost interfacial polarization has become a feasible way to realize high electromagnetic wave absorbing (EMA) performance of dielectric materials. However, interfacial polarization in simple structures such as particles, rods, and flakes is weak and usually plays a secondary role. In order to enhance the interfacial polarization and simultaneously reduce the electronic conductivity to avoid reflection of electromagnetic wave, a more rational geometric structure for dielectric materials is desired. Herein, a Ti3C2Tx/MoS2 self‐rolling rod‐based foam is proposed to realize excellent interfacial polarization and achieve high EMA performance at ultralow density. Different surface tensions of Ti3C2Tx and ammonium tetrathiomolybdate are utilized to induce the self‐rolling of Ti3C2Tx sheets. The rods with a high aspect ratio not only remarkably improve the polarization loss but also are beneficial to the construction of Ti3C2Tx/MoS2 foam, leading to enhanced EMA capability. As a result, the effective absorption bandwidth of Ti3C2Tx/MoS2 foam covers the whole X band (8.2–12.4 GHz) with a density of only 0.009 g cm−3, at a thickness of 3.3 mm. The advantages of rod structures are verified through simulations in the CST microwave studio. This work inspires the rational geometric design of micro/nanostructures for new‐generation EMA materials. A more rational geometric structure for dielectric materials, i.e., a self‐rolling rod‐based foam structure is proposed, which can not only remarkably enhance the interfacial polarization loss but also simultaneously reduce the electronic conductivity to avoid reflection of electromagnetic wave. This work will inspire the rational geometric design of micro/nanostructures for new‐generation electromagnetic wave absorbing materials.

Blazed gratings can reflect an oblique incident wave back in the path of incidence, unlike mirrors and metal plates that only reflect specular waves. Perfect blazing (and zero specular scattering) is a type of Wood’s anomaly that has been observed when a resonance condition occurs in the unit-cell of the blazed grating. Such elusive anomalies have been studied thus far as individual perfect blazing points. In this work, we present reflective blazed surfaces that, by design, have multiple coupled blazing resonances per cell. This enables an unprecedented way of tailoring the blazing operation, for widening and/or controlling of blazing bandwidth and incident angle range of operation. The surface can thus achieve blazing at multiple wavelengths, each corresponding to different incident wavenumbers. The multiple blazing resonances are combined similar to the case of coupled resonator filters, forming a blazing passband between the incident wave and the first grating order. Blazed gratings with single and multi-pole blazing passbands are fabricated and measured showing increase in the bandwidth of blazing/specular-reflection-rejection, demonstrated here at X-band for convenience. If translated to appropriate frequencies, such technique can impact various applications such as Littrow cavities and lasers, spectroscopy, radar, and frequency scanned antenna reflectors.

Traditional image steganography often leans interests towards safely embedding hidden information into cover images with payload capacity almost neglected. This paper combines recent deep convolutional neural network methods with image-into-image steganography. It successfully hides the same size images with a decoding rate of 98.2% or bpp (bits per pixel) of 23.57 by changing only 0.76% of the cover image on average. Our method directly learns end-to-end mappings between the cover image and the embedded image and between the hidden image and the decoded image. We further show that our embedded image, while with mega payload capacity, is still robust to statistical analysis.

Flutter is an extremely significant academic topic in both aerodynamics and aircraft design. Since flutter can cause multiple types of phenomena including bifurcation, period doubling, and chaos, it becomes one of the most unpredictable instability phenomena. The complexity of modeling aeroelasticity of high flexibility wings will be substantially simplified by investigating the prospect of system identification techniques to forecast flutter velocity. Therefore, a novel neural network (NN)-based method for aeroelastic system identification is proposed. The proposed NN-based approach constructs an NN framework of high flexibility wings flutter models with different materials and sizes, which can effectively predict the flutter velocity of flexible wings. The accuracy of the method is demonstrated by comparing with the simulation results.

In recent years, lipid nanoparticles (LNPs) have attracted extensive attention in tumor immunotherapy. Targeting immune cells in cancer therapy has become a strategy of great research interest. mRNA vaccines are a potential choice for tumor immunotherapy, due to their ability to directly encode antigen proteins and stimulate a strong immune response. However, the mode of delivery and lack of stability of mRNA are key issues limiting its application. LNPs are an excellent mRNA delivery carrier, and their structural stability and biocompatibility make them an effective means for delivering mRNA to specific targets. This study summarizes the research progress in LNP delivery carrier-assisted targeted controlled release mRNA vaccines in tumor immunity. The role of LNPs in improving mRNA stability, immunogenicity, and targeting is discussed. This review aims to systematically summarize the latest research progress in LNP delivery carrier-assisted targeted controlled release mRNA vaccines in tumor immunity to provide new ideas and strategies for tumor immunotherapy, as well as to provide more effective treatment plans for patients.

Large Language Models (LLMs), recognized for their advanced capabilities in natural language processing, have been successfully employed across various domains. However, their effectiveness in addressing challenges related to drug discovery has yet to be fully elucidated. In this paper, we propose a novel LLM based method for drug-drug interaction (DDI) prediction, named DDI-JUDGE, achieved through the integration of judging and ICL prompts. The proposed method outperforms existing LLM approaches, demonstrating the potential of LLMs for predicting DDIs. We introduce a novel in-context learning (ICL) prompt paradigm that selects high-similarity samples as positive and negative prompts, enabling the model to effectively learn and generalize knowledge. Additionally, we present an ICL-based prompt template that structures inputs, prediction tasks, relevant factors, and examples, leveraging the pre-trained knowledge and contextual understanding of LLMs to enhance DDI prediction capabilities. To further refine predictions, we employ GPT-4 as a discriminator to assess the relevance of predictions generated by multiple LLMs. DDI-JUDGE achieves the best performance among all models in both zero-shot and few-shot settings, with an AUC of 0.642/0.788 and AUPR of 0.629/0.801, respectively. These results demonstrate its superior predictive capability and robustness across different learning scenarios. These findings highlight the potential of LLMs in advancing drug discovery through more effective DDI prediction. The modular prompt structure, combined with ensemble reasoning, offers a scalable framework for knowledge-intensive biomedical applications. The code for DDI-JUDGE is available at https://github.com/zcc1203/ddi-judge.

Due to the high proportion of aircraft faults caused by cracks in aircraft structures, crack inspection in aircraft structures has long played an important role in the aviation industry. The existing approaches, however, are time-consuming or have poor accuracy, given the complex background of aircraft structure images. In order to solve these problems, we propose the YOLOv3-Lite method, which combines depthwise separable convolution, feature pyramids, and YOLOv3. Depthwise separable convolution is employed to design the backbone network for reducing parameters and for extracting crack features effectively. Then, the feature pyramid joins together low-resolution, semantically strong features at a high-resolution for obtaining rich semantics. Finally, YOLOv3 is used for the bounding box regression. YOLOv3-Lite is a fast and accurate crack detection method, which can be used on aircraft structure such as fuselage or engine blades. The result shows that, with almost no loss of detection accuracy, the speed of YOLOv3-Lite is 50% more than that of YOLOv3. It can be concluded that YOLOv3-Lite can reach state-of-the-art performance.

Aeroponics is a relatively new soilless culture technology which may produce food in space-limited cities or on non-arable land with high water-use efficiency. The shoot and root growth, root characteristics, and mineral content of two lettuce cultivars were measured in aeroponics, and compared with hydroponics and substrate culture. The results showed that aeroponics remarkably improved root growth with a significantly greater root biomass, root/shoot ratio, and greater total root length, root area, and root volume. However, the greater root growth did not lead to greater shoot growth compared with hydroponics, due to the limited availability of nutrients and water. It was concluded that aeroponics systems may be better for high value true root crop production. Further research is necessary to determine the suitable pressure, droplet size, and misting interval in order to improve the continuous availability of nutrients and water in aeroponics, if it is to be used to grow crops such as lettuce for harvesting above-ground parts.