In the field of Web knowledge base mining and Web services, the recognition service for named entity faces many challenges such as context complexity, semantic subtlety, and fuzzy entity boundaries, all of which require highly accurate and robust recognition service. The current services usually fail to reach those conditions. To address this issue, this paper proposes a recognition service for named entities for large Web knowledge bases, and the core contrition is the developed dual multilayer feature learning (D-MLFL) service, which combines projected gradient descent (PGD), adversarial learning, and a fused attention mechanism. Our service successfully addresses the challenges of complex context and subtle semantics faced by named entity recognition tasks. Our service integrates deep language models, recurrent neural networks, and conditional random fields, and clearly outperforms existing approaches in many sub-tasks, including in feature extraction, sequence modeling, and label decoding, especially in dealing with complex and diverse entity types and contextual relationships. We performed sufficient experiments, and the results show that our service significantly enhances the robustness against noise and abnormal Web data. The ability to extract entity features is improved, resulting in higher accuracy in identifying entities with fuzzy boundaries and complex semantics.

In representation learning for Web temporal knowledge bases, each node in Web knowledge bases carries a specific contextual meaning. Existing services often neglect the implicit semantic Web knowledge behind entities and relations, thus failing to effectively capture the knowledge representation of temporal Web knowledge bases. To address this issue, this paper develops an autocompletion service for temporal Web knowledge bases, which is based on multisource semantic feature learning and feature fusion. We construct a semantic model oriented toward external semantic Web repositories to supplement entity-relation descriptions, and our service leverages the pretrained language model BERT, effectively learning semantic knowledge features. Additionally, our service captures the textual features of quadruples using a recurrent neural network, constructs a historical sparse timestamp matrix, and generates a mask tensor, successfully obtaining the weights of potentially correct entities, and thereby capturing the historical features of quadruples. Furthermore, our service integrates complementary features from different modules through an attention mechanism. Experimental validation shows that our service outperforms existing approaches in terms of four evaluation metrics: mean reciprocal rank (MRR), Hits@1, Hits@3, and Hits@10. The results also exhibit that it improves the accuracy and performance for autocompletion service for temporal Web knowledge bases.

This paper presents a multi-particle parallel manipulation optoelectronic tweezers system integrated with computer vision technology, enabling the parallel and precise manipulation of dozens of particles. This system significantly enhances manipulation efficiency while maintaining high precision. By real-time monitoring of particle motion and light patterns, the system can rapidly adjust and optimize its manipulation strategy, thereby improving the stability and reliability of multi-particle synchronization in complex environments. Extensive experimental results demonstrate the system’s outstanding performance. For instance, it can quickly arrange complex patterns and letter sequences, facilitate the coordinated assembly of organoids from particle groups, and efficiently perform the precise separation and arrangement of mixed particles. The core advantage of this system lies in its high parallelism and flexibility, enabling it to handle large-scale synchronous manipulation tasks with exceptional operating accuracy. With continuous technological advancements and the broadening of application scenarios, this system is expected to have a profound impact in fields such as cell sorting, micro-device assembly, and organoid construction, providing robust support for research and technological development in these areas.

The efficacy of targeted cancer drug therapy is significantly compromised by imprecise drug delivery mechanisms. Micro/nano robots (MNRs), characterized by their controllable motion, present a promising solution to this challenge. However, the non-Newtonian nature of blood, with its high viscosity and blood cells’ interference, poses substantial limitations on the upstream efficiency of MNRs. This paper innovatively discusses for the first time the effects of blood viscosity and blood cell interference on the motion of MNRs, investigating their upstream motion capabilities in blood through comprehensive theoretical modeling, simulation, and experimental validation. A dynamic model of MNR motion was developed, and the velocity formula for MNRs in non-Newtonian fluid was derived. Experiments were conducted using different magnetic fields in pure water, high-viscosity simulated blood, and diluted blood. Results indicated that under a gradient magnetic field, the upstream velocities of MNRs in pure water, simulated blood, and diluted blood were 45.0, 14.4, and 11.1 mm/s, respectively. Under a rotating magnetic field, the velocities of vortex swarms were 825, 240, and 145 µm/s, respectively. Increased fluid viscosity reduced MNR velocity by 70%, while blood cells caused an additional 10% reduction. This research establishes a theoretical and experimental framework for the upstream motion of MNRs against blood flow, enhancing their potential in targeted drug delivery and broader biomedical applications.

Magnetically controlled micro-nano robots hold revolutionary significance in the clinical targeted treatment of brain tumors. Imaging and tracking miniature robots can provide feedback for precise magnetic field control. The cooperation among micro-nano robots, magnetic field control system, and imaging system is a significant challenge for transitioning micro-nano robots from laboratory research to clinical applications. This study explores the control and spatial localization of magnetic nanorobot swarms in a highly realistic, human-sized vascular phantom which is manufactured using the raw CT scan images. The cerebral arterial vessels are the key focus area with four main inlets and twenty-six branch outlets. The simulation results show that, under the influence of a magnetic field, the nanorobots can accumulate at the target tumor site. The Kernelized Correlation Filter (KCF) algorithm was employed to achieve single-plane tracking of nanorobots. Furthermore, based on a biplanar imaging system, three-dimensional spatial trajectory tracking of nanorobots was realized. This study provides a reference for in vivo spatial localization and imaging of magnetic nanorobot swarms (MNRS) transported through vascular system.

Smart contracts, due to their immutability and transparency upon deployment, entail significant economic and systemic risks from any vulnerabilities present. Traditional vulnerability detection methods suffer from low automation and high false positive rates, while existing deep learning-based approaches inadequately extract contract features, thereby limiting detection accuracy. To address these issues, this paper proposes FEMD: a feature-enhanced aided multimodal feature fusion method for smart contract vulnerability detection. Building on the foundation of addressing the low automation of traditional detection tools, our method improves the model’s feature extraction performance and detection capabilities. Specifically, we construct a contract graph through Comprehensive Expert-Graph Fusion, combining multi-modal feature fusion using a multi-head attention mechanism with expert patterns to ensure the capture and effective preservation of all critical information during the fusion process. To delve deeper into potential information within smart contract graphs, we employ a Feature Enhancer that leverages transpose operations on feature matrices to extract complex interaction patterns across different dimensions. Our approach is validated through batches of experiments on the Ethereum open dataset focusing on reentrancy and timestamp dependency vulnerabilities, demonstrating significant improvements in detection accuracy and robustness.

As a medical microrobot, magnetic-controlled capsule robots (MCRs) are pivotal in internal diagnostics and therapeutic interventions. Achieving high-precision localization of MCRs is essential for the successful execution of medical procedures. This paper introduces a novel Dung Beetle Optimizer (DBO)-based localization method for MCR, demonstrating high localization accuracy and flexibility in static magnetic field environments and under the control of existing magnetic control systems. With the aid of an FPGA-based parallel measurement system, it can effectively eliminate measurement distortion. The average position and orientation errors could achieve 0.53 mm and 0.60° when performing 600 iterations per computation, and further increasing the number of iterations reduces the errors, which is superior to existing methods. Experimental validations underscore the method’s robust performance and compatibility with existing magnetic control systems.

Magnetic microrobots exhibit enormous potential in targeted drug delivery owing to the remote wireless manipulation and minimum invasion for medical treatment. High degree of freedom offers the magnetic propelled robots extraordinary application prospect since they can be controlled precisely when different magnetic fields sources working cooperatively. However, the biocompatibility of microrobots have attracted sustained and general concern. Therefore, it is highly necessary to develop a promising carrier with high biocompatibility and investigate the mechanism of drug loading-release triggered by special microenvironment in the targeted region. In this paper, we proposed a magnetically controlled cell robots (MCRs) based on macrophages propelled by a rotating magnetic field. The innovative MCRs exhibit good biocompatibility and low toxicity by optimizing the concentration of polylysine-coated Fe nanoparticles (PLL@FeNPs) to 40 µg/mL. These MCRs loaded with murine interleukin-12 (IL-12), murine chemokine (C-C motif) ligand 5 (CCL-5), and murine C-X-C motif chemokine ligand 10 (CXCL-10) which can stimulate T cell differentiation and recruitment of monocytes, respectively. The macrophages showed an obvious M1-polarization tendency of macrophages to phagocytose intracellular pathogens and resist the growth of tumor cells. Under the control of a magnetic propelling system composed of 3 pairs of Helmholtz coil, the cell robot can be propelled wirelessly and moved along a predefined path with high accuracy. Moreover, the MCRs could approach to cancer cells and stop at places of interest in vitro. In conclusion, we have accomplished the preliminary construction of a targeted drug delivery system which displays great immune-enhancing potential for targeted drug delivery.

All-weather Land Surface Temperature (LST) derived from passive microwave (PMW) sensors has significant implications for characterization on the physical processes of surface energy and water balance at local through global scales. However, the PMW sensors (e.g., the AMSR-E) suffer from swath gaps, cannot provide completely spatial-gapless observations. The existing Multi-temporal Feature Connection-CNN (MTFC-CNN) method caused obvious traces of ‘gaps’ when the sample number is small or features are not rich. This paper proposes a Sample Optimized-MTFC (SO-MTFC) seamless reconstruction method based on analyzing the periodicity and complementarity of AMSR-E swath gaps. Sample optimization includes two aspects: sample enhancement and single-cycle mask strategy. Taking China’s landmass as the study area, experimental results show that the original AMSR-E LSTs and the reconstructed AMSR-E LSTs are basically connected seamlessly. Validation against with the MODIS LSTs show that the daytime (nighttime) RMSEs of the original and the reconstructed AMSR-E LSTs are 3.87 K (2.57 K) and 4.76 K (2.96 K), respectively; while the corresponding daytime (nighttime) R2are 0.88 (0.94) and 0.73 (0.90), respectively. Validation against with the six in-situ LSTs show that the RMSEs and R2of reconstructed AMSR-E LSTs against in-situ LSTs are almost consistent with those of the original AMSR-E LST. The ablation study proves the effectiveness of the sample optimization. These findings indicated the SO-MTFC achieved a good reconstruction effect. Compared with the MTFC-CNN, the SO-MTFC got higher scores in visually and quantitatively. The SO-MTFC can potentially be implemented with other satellite PMW sensors to produce completely spatial-seamless PMW LST records on a global scale.

The unique chemical reactivity of cysteine residues results in various posttranslational modifications (PTMs), which are implicated in regulating a range of fundamental biological processes. With the advent of chemical proteomics technology, thousands of cysteine PTM (CysPTM) sites have been identified from multiple species. A few CysPTM-based databases have been developed, but they mainly focus on data collection rather than various annotations and analytical integration. Here, we present a platform-dubbed CysModDB, integrated with the comprehensive CysPTM resources and analysis tools. CysModDB contains five parts: (1) 70 536 experimentally verified CysPTM sites with annotations of sample origin and enrichment techniques, (2) 21 654 modified proteins annotated with functional regions and structure information, (3) cross-references to external databases such as the protein-protein interactions database, (4) online computational tools for predicting CysPTM sites and (5) integrated analysis tools such as gene enrichment and investigation of sequence features. These parts are integrated using a customized graphic browser and a Basket. The browser uses graphs to represent the distribution of modified sites with different CysPTM types on protein sequences and mapping these sites to the protein structures and functional regions, which assists in exploring cross-talks between the modified sites and their potential effect on protein functions. The Basket connects proteins and CysPTM sites to the analysis tools. In summary, CysModDB is an integrated platform to facilitate the CysPTM research, freely accessible via https://cysmoddb.bioinfogo.org/.