This systematic review provides a comprehensive analysis of magnetically actuated capsule robots, focusing on their design, control mechanisms, and diverse applications. Through a systematic literature search and analysis, this review aims to provide valuable insights for researchers, engineers, and practitioners involved in the development and utilization of capsule robots in various domains. The design principles of magnetically actuated capsule robots are summarized in terms of size, shape, material and motion mechanism, the precise control methods and positioning navigation strategies analyzed, and the application of magnetically actuated capsule robots in gastrointestinal disease examination discussed, targeting drug delivery and minimally invasive surgery. According to the scope and objectives of the systematic review, the technical challenges and potential research directions in this field are summarized. Through continuous research and innovation on issues such as positioning accuracy, control strategies, and material properties, the magnetically actuated capsule robot system will bring major breakthroughs and advancements in the fields of medical treatment and biological research.

Because of high theoretical specific capacity and energy density, lithium-sulfur batteries (LSBs) are regarded as one of the most promising energy storage batteries. However, the low conductivity of the active sulfur and the Li₂S discharge product, the shuttle effect of intermediate products produced by the charging and discharging process, and serious capacity degradation caused by the slow sulfur redox kinetics, limits the practical application of LSB. Herein, three different vanadium sulfide@MXene hetero-structure catalysts were synthesized by one-step hydrothermal method and applied to the cathode host in LSBs. Compared with VS₄@MXene and V₅S₈@MXene, VS₂@MXene has the largest specific surface area and electrochemical active surface area, which provides more active sites in LSBs, thereby improving the electrochemical reaction kinetics. Meanwhile, the experimental and Density Functional Theory(DFT) theoretical calculation results show that the VS₂@MXene has the strongest polysulfide adsorption ability and electronic conductivity, which effectively alleviates the shuttle effect of polysulfides and improves the utilization of sulfur. LSBs with S/VS₂@MXene as the cathode achieve an initial discharge specific capacity of 815.4 mAh·g^-1 and still maintain a reversible specific capacity of 645.4 mAh·g^-1 after 400 cycles at 1 C. This research provides some insights for the selection of vanadium-based sulfide as the catalytic materials and hosts in lithium-sulfur batteries.

Tin selenide (SnSe₂) is a very promising negative electrode material for sodium ion batteries, which has advantages such as high theoretical specific capacity, wide range of raw materials, and low cost. However, it is also limited by the structural damage caused by volume expansion during charging and discharging processes. A research is conducted on the successful synthesis of carbon coated tin selenide (SnSe₂@C) through high-temperature annealing. Carbon coating improves the conductivity and stability of the material as sodium-ion battery anode, making SnSe₂@C has high specific capacity (549.0 mAh·g^-1 at 1.0 A·g^-1) and excellent rate performance (427.7 mAh·g^-1 at 5.0 A·g^-1).

The Ag@DMSNs composite was prepared by synthesizing the dendritic mesoporous silica nanoparticles (DMSNs) and subsequently loading the nanostructured Ag in the pore channels of the DMSNs via chemical reduction. Thus-obtained Ag@DMSNs feature an intensive and board absorption for the solar irradiation due to the plasmonic coupling of Ag nanostructures, which are anchored in the pore channels of DMSNs and not prone to aggregation. More importantly, the thermal effect of plasmonic relaxation can efficiently convert solar energy into heat. For example, Ag@DMSNs can increase its surface temperature from 26 ℃ to 70 ℃ within 5 minutes under one sun (1 kW·m^-2, 420~2500 nm) . When Ag@DMSNs are loaded on the porous polyurethane foam material, the water evaporation rate reaches 1.10 kg·m^-2·h^-1 under one sun, and they also exhibit excellent stability in simulated seawater. In addition, the thermal electrons produced during the relaxation of the Ag nanoparticle plasmon in the Ag@DMSNs complex can effectively remove contaminants from water, such as the degradation of methylene blue. These results show that it is an effective way to realize solar-powered clean water production by rational construction of the plasmonic coupling model and utilizing the thermal and hot-electron effect of plasmonic relaxation process, opening new avenues to tackling the deteriorating problem of fresh water scarcity.

A low-layer SnO₂ photonic crystal thin film with an inverse opal structure was constructed in this study. Based on the unique ordered porous structure and slow photon effect of inverse opal photonic crystal materials, it effectively enhances light absorption and plays a significant role in fields such as solar cells and photocatalysis. In this study, three sizes of polystyrene (Polystyrene, PS) microspheres were prepared using a soap-free emulsion polymerization method, with controlled amounts of monomers and initiators. A small amount of sodium dodecyl sulfate (Sodium Dodecyl Sulfate, SDS) was added dropwise to the dispersion solution during vertical deposition self-assembly to prepare low-layer PS opal templates. Finally, a low-layer SnO₂ inverse opal photonic crystal film was obtained using a sacrificial template method. Compared with planar structures, this film exhibits enhanced light absorption and diffuse reflectance while maintaining a higher specific surface area within the visible wavelength range. The inverse opal photonic crystal film provides a new strategy for designing electron transport layers in perovskite solar cells.

Surface-enhanced Raman Scattering (SERS) is a rapid and highly sensitive molecular detection technology. Having a highly sensitive and uniform Raman signal is a necessary factor in spectroscopy. Raman substrate structures often face complex processes and high costs. To achieve high-performance SERS, Au-Ag composite nanostructures were prepared by multilayer Au-Ag alternate deposition, annealing and dealloying technology in this research, the method can be used for large-area preparation and facile preparation process. By annealing at a suitable temperature, a large number of nanopores are distributed on the surface of Au-Ag composite nanostructures, which can be firmly distributed on the surface to provide hot spots. The Finite-difference Time-domain (FDTD) method is used to simulate the electric field distribution, and the results show that the Au-Ag composite nanostructure surface can induce great local field enhancement. The experiment results exhibit excellent uniformity and high sensitivity of the SERS detection. The enhancement factor of the Rhodamine 6G (R6G) molecules detected by SERS substrate reaches 2.4×10⁵, and the Relative Standard Deviation (RSD) is as low as 6.9%. The minimum detection concentration of R6G molecules by the Au-Ag composite nanostructures can reach 10^-11 mol/L. The proposed Au-Ag composite nanostructures and the fabrication process have great potential in preparation of high sensitivity and excellent uniformity SERS substrate.

A series of polyurethane prepolymers were designed and synthesized as isocyanate-terminated component for the final product: two-component solvent-free polyurethane adhesives. Structural characterization using infrared and ¹H NMR spectroscopies confirmed the structure of the designed prepolymers. The effect of the isocyanate mass fraction (ω_NCO) of the prepolymer, molecular weight of polyols and ratio R (molar ratio of –OH to –NCO in adhesives) on viscosity and cooking resistance of the polyurethane adhesives were investigated by a variable-control method. The results show that in the case of ω_NCO=17%, the prepolymer has a low viscosity, and the prepared adhesive has a higher peel force after boiling treatment. For a constant value of ω_NCO, as the molecular weight of the polyol decreases, the viscosity of the prepolymer increases, so does the peel force of the polyurethane adhesive before and after boiling. With polyols molecular weight of 1 609 g/mol, ω_NCO of 17% and R of 2.5, the peel force of retort cast polypropylene (RCPP) layer before and after boiling is as high as 6.30 N and 4.37 N, respectively. A series of polyurethane adhesives were prepared by mixing this prepolymer with different B components, meeting the requirements in standard GB/T41168-2021 for cooking resistance requirements of aluminum plastic composite film.

With the development of deep learning methods, the accuracy and speed of face recognition based on visible light in ideal environments have reached an excellent level. However, in complex environments such as low light, the lack of a light source keeps visible images from reflecting face details, resulting in reduced or even invalid face recognition. Aiming at the problems in this issue, a face recognition method in complex environments based on infrared-visible light fusion is proposed. Firstly, an infrared and visible fusion recognition network combining CNN and Transformer is introduced, specifically designed for low illumination environments. This network combines CNN and visual Transformer in parallel to form a single-mode feature fusion module, which is utilized to effectively utilize local details and global context information from the source image. Additionally, a multimodal feature fusion strategy based on the average difference of modes is proposed to enhance the distinctive expression of different regional features in the source image. Secondly, a lightweight face recognition network MobileFaceNet-Coo and an adaptive recognition strategy based on edge-cloud collaboration are proposed in order to solve the problem of large and slow fusion recognition network models in practical applications. This strategy selects the recognition model through image quality and effectively utilizes hardware resources. Experimental results demonstrate that the recognition rate of fused infrared light is 13.96 percentage point higher than that of visible light alone. Real-world project result shows that this method significantly improves real-time and accuracy of face recognition in complex environments.

A lightweight unmanned aerial vehicle (UAV) aerial photography small target detection algorithm GA-YOLO based on YOLOv5 is proposed to address the problem of small target feature size, complex background, and dense distribution in images from the perspective of UAV aerial photography. This algorithm improves the Mosaic data augmentation method and overall network structure, and adds a small object detection head. At the same time, a lightweight global attention module and a parallel spatial channel attention mechanism module are designed to enhance the network's global feature extraction ability and the competition and cooperation between convolutional channels during the training process. Based on the 4.0 version of YOLOv5s, experiments were conducted on the publicly available drone aerial photography dataset VisDrone2019-DET. The results showed that the improved model reduced the number of parameters by 48% and the computational complexity by 26% compared to the original model, and mAP@0.5 improved by 4.9 percentage points, mAP@0.5 0.95 increased by 3.3 percentage points, effectively enhancing the detection capability of unmanned aerial vehicles for dense small targets from an aerial perspective.

Text-based CAPTCHAs are friendly and easy to understand, which have been widely used in the security defense mechanism of many Internet applications. Traditional text-based CAPTCHAs improve security by distorting characters or adding background noise. With the development of deep learning, its security is threatened and over-deformed characters will bring new problems to human. To address this, this paper designs a perturbation optimization framework with randomization strategy for text-based CAPTCHAs generation (denoted as PORG), which is friendly for human but difficult for machines. Specifically, the proposed PORG devises a perturbation generation network (PGN) based on current advanced and stable perturbation methods to construct multiple perturbation factors and applies a randomization strategy to generate diverse perturbed images. In particular, the perturbation factors generated by existing methods destroy the visual information conveyed by the CAPTCHA images. To this end, a perturbation optimization network (PON) is designed to control the introduced perturbation factors by extending the distance at feature-level and narrowing the gap at global-level, which makes the generated CAPTCHAs remain human-friendly while effectively treating the attacker model. Extensive experiments conducted on eight real-world datasets show the outperformance of the proposed PORG (e.g., attack accuracy is dropped from 90.03% to 0.12% on the CNKI dataset).

Target speaker extraction aims to extract the speech of the specific speaker from mixed audio, which usually treats the enrolled audio of the target speaker as auxiliary information. Existing approaches mainly have the following limitations: the auxiliary network for speaker recognition cannot capture the critical information from enrolled audio, and the second one is the lack of an interactive learning mechanism between mixed and enrolled audio embedding. These limitations lead to speaker confusion when the difference between the enrolled and target audio is significant. To address this, a speaker-aware cross-attention speaker extraction network (SACAN) is proposed. First, SACAN introduces an attention-based speaker aggregation module in the speaker recognition auxiliary network, which effectively aggregates critical information about target speaker characteristics. Then, it uses mixed audio to enhance target speaker embedding. After that, to promote the integration of speaker embedding and mixed audio embedding, SACAN builds an interactive learning mechanism through cross-attention and enhances the speaker perception ability of the model. The experimental results show that SACAN improves by 0.0133 and 1.0695 in terms of STOI and SI-SDRi when compared with the benchmark model, validating the effectiveness of the proposed module in speaker confusion assessment and ablation experiments.

In traditional anchor box-based text detection methods for natural scenes, anchor boxes are prone to interference from other text instances, resulting in erroneous judgments or affecting accuracy. Moreover, text instances contain strong topological features, which are usually be ignored, resulting in poor performance in curved circular text detection tasks. To solve this problem, a novel neural network structure is proposed, which introduces the concept of graph convolutional networks by fully considering the relationship between adjacent anchor frames, and incorporating the topological characteristics of anchor frames to assist the learning of graph neural networks, improving the effectiveness of the overall network. The ablation experiments were conducted on two publicly available natural scene text detection datasets. In the CTW1500 dataset, the proposed method improved the model by approximately 3.0%, 1.9%, and 2.5% in terms of recall, accuracy, and F-score, respectively, and in the Totel-Text dataset , the three values were improved by approximately 2.2%, 1.8%, and 2.0%, respectively. In addition, the proposed method has also been compared with other text detection algorithms proposed in recent years. Experimental results show that the proposed method performs well for text detection in complex natural scenes, demonstrating the promising effectiveness of the proposed module for improving the performance of text detection.

In order to solve the micro battery energy problem of terminal Internet of Things user devices in computation-intensive applications, green renewable energy harvesting technology and double reconfigurable intelligent surfaces(RIS) technology enabling edge computing were studied and a green Internet of Things edge computing system assisted by double-RIS was constructed, effectively extending the computing life of terminal Internet of Things user devices and improving system computation capacity. Firstly, a multi-user cascade fading channel model assisted by double-RIS was established, and a multi-slot random arrival model of green renewable energy harvesting was established to model the causal constraints of energy supply and demand of Internet of Things terminal devices. Secondly, based on the maximization of system computation capacity, the joint optimization design problem of terminal local computing rate, edge computing offloading power and phase shift of RISs was modeled. This design problem belongs to a class of complex non-convex optimization problem. To this end, lightweight multi-stage optimization technology was adopted to rapidly and iteratively design variables such as local computation, computation offloading and phase shift of RISs, etc, to complete the design of green Internet of Things edge computing system. The experimental results show that the performance gains of the proposed scheme are better than the existing benchmark schemes, and the proposed scheme is equivalent to the scheme based on semidefinite relaxation algorithm under less system computing time.

In order to improve the spectrum utilization efficiency and the energy limitation of cognitive simultaneous wireless information and power transfer (SWIPT) network, a study is conducted on an intelligent reflecting surface (IRS) -assisted cognitive SWIPT network, where the primary user network shares its spectrum with the secondary user network in overlay mode, the secondary transmitter simultaneously transmits energy to the primary transmitter and information to the secondary receiver. An optimization algorithm for the throughput of the secondary user network is proposed, under the constraints of the maximum transmit power of the secondary user transmitter, the minimum throughput requirement of the primary user network, the available time slots, and the phase shifts of the IRS, and the beamforming vector of the secondary transmitter, the time slot allocation, and the phase shifts of the IRS are jointly optimized to maximize the throughput of the secondary user network. The optimization variables of the proposed problem are coupled with each other and the structure is highly non-convex, making it is difficult to solve directly. The proposed algorithm applies alternating optimization, semi-positive relaxation, and successive convex approximation techniques to transform the original problem into three subproblems for alternative solution. Simulation results show that the proposed algorithm can significantly improve the throughput of the secondary user network compared with the existing benchmark schemes.

To improve the real-time performance of High Efficiency Video Coding (HEVC) intra-frame encoding, a method, which utilizes a lightweight convolutional network with even-length and step-size convolutional kernels and a self-attention mechanism, is proposed to predict the intra-frame partitioning structure of Coding Tree Units(CTU) , thereby reducing the encoding time required for the encoder to perform quadtree recursive traversal partitioning on CTUs. In the original encoding strategy, Rough Mode Decision accelerates the process by estimating the rate-distortion loss value in Rate Distortion Optimization based on the Sum of Absolute Transformed Difference (SATD) -based loss value, but it still consumes a certain amount of encoding time. A proposed method reduces the number of patterns calculated in the Rough Mode Decision process through a sampling search approach, reducing the number of patterns from 35 to 18, and decreasing the time required to estimate the loss value during the Rough Mode Decision process. The more favorable multiple candidate intra-frame modes obtained from the Rough Mode Decision process are used for Rate Distortion Optimization. In order to reduce the number of candidate modes that need to be calculated in Rate Distortion Optimization, an early stopping decision is implemented by filtering out some less likely candidate modes based on the differences in the estimated loss values of the intra-frame prediction angle modes in the candidate mode list, thus reducing the number of candidate modes that need to be evaluated in Rate Distortion Optimization and consequently decreasing the computation time of the Rate Distortion Optimization process. The proposed algorithm achieves an average encoding time reduction of 78.15% on the test sequences, with a BD-PSNR of -0.168dB and a BD-RATE of 3.49%.