Search Results (7)

In recent years, Mongolian-Chinese neural machine translation (MCNMT) technology has made substantial progress. However, the establishment of the Mongolian dataset requires a significant amount of financial and material investment, which has become a major obstacle to the performance of MCNMT. Pre-training and fine-tuning technology have also achieved great success in the field of natural language processing, but how to fully exploit the potential of pre-training language models (PLMs) in MCNMT has become an urgent problem to be solved. Therefore, this paper proposes a novel MCNMT model based on the soft target template and contextual knowledge. Firstly, to learn the grammatical structure of target sentences, a selection-based parsing tree is adopted to generate candidate templates that are used as soft target templates. The template information is merged with the encoder-decoder framework, fully utilizing the templates and source text information to guide the translation process. Secondly, the translation model learns the contextual knowledge of sentences from the BERT pre-training model through the dynamic fusion mechanism and knowledge extraction paradigm, so as to improve the model’s utilization rate of language knowledge. Finally, the translation performance of the proposed model is further improved by integrating contextual knowledge and soft target templates by using a scaling factor. The effectiveness of the modified model is verified by a large number of data experiments, and the calculated BLEU (BiLingual Evaluation Understudy) value is increased by 4.032 points compared with the baseline MCNMT model of Transformers. Full article

To enhance the anti-submarine and search capabilities of multiple Unmanned Aerial Vehicle (UAV) groups in complex marine environments, this paper proposes a flexible action-evaluation algorithm known as Knowledge-Driven Soft Actor-Critic (KD-SAC), which can effectively interact with real-time environmental information. KD-SAC is a reinforcement learning algorithm that consists of two main components: UAV Group Search Knowledge Base (UGSKB) and path planning strategy. Firstly, based on the UGSKB, we establish a cooperation search framework that comprises three layers of information models: the data layer provides prior information and fundamental search rules to the system, the knowledge layer enriches search rules and database in continuous searching processes, and the decision layer utilizes above two layers of information models to enable autonomous decision-making by UAVs. Secondly, we propose a rule-based deductive inference return visit (RDIRV) strategy to enhance the knowledge base of search. The core concept of this strategy is to enable UAVs to learn from both successful and unsuccessful experiences, thereby enriching the search rules based on optimal decisions as exemplary cases. This approach can significantly enhance the learning performance of KD-SAC. The subsequent step involves designing an event-based UGSKB calling mechanism at the decision-making level, which calls a template based on the target and current motion. Finally, it uses a punishment function, and is then employed to achieve optimal decision-making for UAV actions and states. The feasibility and superiority of our proposed algorithm are demonstrated through experimental comparisons with alternative methods. The final results demonstrate that the proposed method achieves a success rate of 73.63% in multi-UAV flight path planning within complex environments, surpassing the other three algorithms by 17.27%, 29.88%, and 33.51%, respectively. In addition, the KD-SAC algorithm outperforms the other three algorithms in terms of synergy and average search reward. Full article

Carbon nanomaterials derived from agricultural waste streams present an exciting material platform that hits multiple sustainability targets by reducing waste entering landfill, and enabling clean energy and environmental remediation technologies. In this work, the energy and photocatalytic properties of reduced graphene oxide fabricated from coconut coir using a simple reduction method using ferrocene are substantially improved by introducing metallic oxides flakes. A series of cobalt ferrite rGO/CoFe₂O₄ nanocomposites were assembled using a simple soft bubble self-templating assembly, and their potential for clean energy applications confirmed. The transmission electron microscopy images revealed the uniform dispersion of the metal oxide on the rGO sheets. The functional group of the as synthesized metal oxide and the rGO nanocomposites, and its individual constituents, were identified through the FTIR and XPS studies, respectively. The composite materials showed higher specific capacitance then the pure materials, with rGO spinal metal oxide nanocomposites showing maximum specific capacitance of 396 F/g at 1 A/g. Furthermore, the hybrid super capacitor exhibits the excellent cyclic stability 2000 cycles with 95.6% retention. The photocatalytic properties of the synthesized rGO nanocomposites were analyzed with the help of malachite green dye. For pure metal oxide, the degradation rate was only around 65% within 120 min, while for rGO metal oxide nanocomposites, more than 80% of MG were degraded. Full article

Plasmonic bio/chemical sensing based on optical fibers combined with molecularly imprinted nanoparticles (nanoMIPs), which are polymeric receptors prepared by a template-assisted synthesis, has been demonstrated as a powerful method to attain ultra-low detection limits, particularly when exploiting soft nanoMIPs, which are known to deform upon analyte binding. This work presents the development of a surface plasmon resonance (SPR) sensor in silica light-diffusing fibers (LDFs) functionalized with a specific nanoMIP receptor, entailed for the recognition of the protein human serum transferrin (HTR). Despite their great versatility, to date only SPR-LFDs functionalized with antibodies have been reported. Here, the innovative combination of an SPR-LFD platform and nanoMIPs led to the development of a sensor with an ultra-low limit of detection (LOD), equal to about 4 fM, and selective for its target analyte HTR. It is worth noting that the SPR-LDF-nanoMIP sensor was mounted within a specially designed 3D-printed holder yielding a measurement cell suitable for a rapid and reliable setup, and easy for the scaling up of the measurements. Moreover, the fabrication process to realize the SPR platform is minimal, requiring only a metal deposition step. Full article

A self-assembled MnO₂/Mxene composite film was compounded with MXene nanosheets and layered crystalized MnO₂ nanosheets using surfactant sodium dodecyl sulfate (SDS) as a soft template. The obtained material was characterized by XRD, SEM, XPS, and FT-IR, which showed that the films have large surface-active functional groups and metal ion flow channels, indicating that the MnO₂/Mxene composite films were capable of both the chemical and physical adsorption of the target heavy metal ions. The analysis of adsorption performance showed that the Pb²⁺ removal rate reached 98.3% at pH 6 and an initial Pb²⁺ concentration of 30 mg/L, while the maximum adsorption capacity could reach 1235 µmol/g. In addition, the MnO₂/Mxene composite film had specific selectivity and recyclability. The reuse study verified that the Pb²⁺ removal rate reached 96.4% after five cycles, confirming that the MnO₂/Mxene composite films had practical application prospects. Full article

Rare-earth nanomaterials are being widely applied in medicine as cytotoxicity agents, in radiation and photodynamic therapy, as drug carriers, and in biosensing and bioimaging technology. Terbium (Tb), a rare-earth element belonging to the lanthanides, has a long luminescent lifetime, large stock displacement, narrow spectral width, and biofriendly probes. In cancer therapy, cancer stem cell (CSC)-targeted treatment is receiving considerable attention due to these cells’ harmful characteristics. However, CSCs remain barely understood. Therefore, to effectively label and inhibit the growth of CSCs, we produced a nanocomplex in which TbPO₄·H₂O nanorods were double conjugated with CD133 and PD-L1 monoclonal antibodies. The Tb³⁺ nanomaterials were created in the presence of a soft template (polyethylene glycol 2000). The obtained nanomaterial TbPO₄·H₂O was hexagonal crystal and nanorod in shape, 40–80 nm in diameter, and 300–800 nm in length. The nanorods were further surfaced through tetraethyl orthosilicate hydrolysis and functionalized with amino silane. Finally, the glutaraldehyde-activated Tb³⁺ nanorods were conjugated with CD133 monoclonal antibody and PD-L1 monoclonal antibody on the surface to obtain the nanocomplex TbPO₄·H₂O@silica-NH₂+mAb^CD133+mAb^PD-L1 (TMC). The formed nanocomplex was able to efficiently and specifically label NTERA-2 cells, a highly expressed CD133 and PD-L1 CSC cell line. The conjugate also demonstrated promising anti-CSC activity by significant inhibition (58.50%) of the growth of 3D tumor spheres of NTERA-2 cells (p < 0.05). Full article

Vitamin K was originally discovered as a cofactor required to activate clotting factors and has recently been shown to play a key role in the regulation of soft tissue calcification. This property of vitamin K has led to an increased interest in novel methods for accurate vitamin K detection. Molecularly Imprinted Polymers (MIPs) could offer a solution, as they have been used as synthetic receptors in a large variety of biomimetic sensors for the detection of similar molecules over the past few decades, because of their robust nature and remarkable selectivity. In this article, the authors introduce a novel imprinting approach to create a MIP that is able to selectively rebind vitamin K₁. As the native structure of the vitamin does not allow for imprinting, an alternative imprinting strategy was developed, using the synthetic compound menadione (vitamin K₃) as a template. Target rebinding was analyzed by means of UV-visible (UV-VIS) spectroscopy and two custom-made thermal readout techniques. This analysis reveals that the MIP-based sensor reacts to an increasing concentration of both menadione and vitamin K₁. The Limit of Detection (LoD) for both compounds was established at 700 nM for the Heat Transfer Method (HTM), while the optimized readout approach, Thermal Wave Transport Analysis (TWTA), displayed an increased sensitivity with a LoD of 200 nM. The sensor seems to react to a lesser extent to Vitamin E, the analogue under study. To further demonstrate its potential application in biochemical research, the sensor was used to measure the absorption of vitamin K in blood serum after taking vitamin K supplements. By employing a gradual enrichment strategy, the sensor was able to detect the difference between baseline and peak absorption samples and was able to quantify the vitamin K concentration in good agreement with a validation experiment using High-Performance Liquid Chromatography (HPLC). In this way, the authors provide a first proof of principle for a low-cost, straightforward, and label-free vitamin K sensor. Full article