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Motion planning is a fundamental problem in robotics that involves determining feasible or optimal paths within finite time. While complete motion planning algorithms are guaranteed to converge to a path solution in finite time, they are proven to be computationally inefficient, making them unsuitable for most practical problems. Resolution-complete algorithms, on the other hand, ensure completeness only if the resolution parameter is sufficiently fine, but they suffer severely from the curse of dimensionality. In contrast, sampling-based algorithms, such as Rapidly Exploring Random Trees (RRT) and its variants, have gained the increasing attention of researchers due to their computational efficiency and effectiveness, particularly in high-dimensional problems. This review paper introduces RRT-based algorithms and provides an overview of their key methodological aspects.

Gastroscopic biopsy provides the only effective method for gastric cancer diagnosis, but the gold standard histopathology is time-consuming and incompatible with gastroscopy. Conventional stimulated Raman scattering (SRS) microscopy has shown promise in label-free diagnosis on human tissues, yet it requires the tuning of picosecond lasers to achieve chemical specificity at the cost of time and complexity. Here, we demonstrate that single-shot femtosecond SRS (femto-SRS) reaches the maximum speed and sensitivity with preserved chemical resolution by integrating with U-Net. Fresh gastroscopic biopsy is imaged in <60 s, revealing essential histoarchitectural hallmarks perfectly agreed with standard histopathology. Moreover, a diagnostic neural network (CNN) is constructed based on images from 279 patients that predicts gastric cancer with accuracy >96%. We further demonstrate semantic segmentation of intratumor heterogeneity and evaluation of resection margins of endoscopic submucosal dissection (ESD) tissues to simulate rapid and automated intraoperative diagnosis. Our method holds potential for synchronizing gastroscopy and histopathological diagnosis. Diagnosis of gastric cancer currently requires gastroscopic biopsy, which requires time and expertize to perform. Here, the authors demonstrate a femto-SRS imaging method which showed high accuracy in diagnosing gastric cancer without the need for pathologistbased diagnosis.

Pyroptosis, systemic inflammation, and mitochondrial apoptosis are the three primary contributors to sepsis's multiple organ failure, the ultimate cause of high clinical mortality. Currently, the drugs under development only target a single pathogenesis, which is obviously insufficient. In this study, an acid‐responsive hollow mesoporous polydopamine (HMPDA) nanocarrier that is highly capable of carrying both the hydrophilic drug NAD+ and the hydrophobic drug BAPTA‐AM, with its outer layer being sealed by the inflammatory targeting peptide PEG‐LSA, is developed. Once targeted to the region of inflammation, HMPDA begins depolymerization, releasing the drugs NAD+ and BAPTA‐AM. Depletion of polydopamine on excessive reactive oxygen species production, promotion of ATP production and anti‐inflammation by NAD+ replenishment, and chelation of BAPTA (generated by BA‐AM hydrolysis) on overloaded Ca2+ can comprehensively block the three stages of sepsis, i.e., precisely inhibit the activation of pyroptosis pathway (NF‐κB‐NLRP3‐ASC‐Casp‐1), inflammation pathway (IL‐1β, IL‐6, and TNF‐α), and mitochondrial apoptosis pathway (Bcl‐2/Bax‐Cyt‐C‐Casp‐9‐Casp‐3), thereby restoring intracellular homeostasis, saving the cells in a state of “critical survival,” further reducing LPS‐induced systemic inflammation, finally restoring the organ functions. In conclusion, the synthesis of this agent provides a simple and effective synergistic drug delivery nanosystem, which demonstrates significant therapeutic potential in a model of LPS‐induced sepsis. Depletion of excessive reactive oxygen species, replenishment of nicotinamide adenine dinucleotide (NAD+), and chelation of overloaded Ca2+ can comprehensively block the three stages of sepsis, saving the cells in a state of “critical survival”, further restoring the organ functions of sepsis mice.

With the development of social media, the internet, and sensing technologies, multimodal data are becoming increasingly common. Integrating these data into knowledge graphs can help models to better understand and utilize these rich sources of information. The basic idea of the existing methods for entity alignment in knowledge graphs is to extract different data features, such as structure, text, attributes, images, etc., and then fuse these different modal features. The entity similarity in different knowledge graphs is calculated based on the fused features. However, the structures, attribute information, image information, text descriptions, etc., of different knowledge graphs often have significant differences. Directly integrating different modal information can easily introduce noise, thus affecting the effectiveness of the entity alignment. To address the above issues, this paper proposes a knowledge graph entity alignment method based on multimodal data supervision. First, Transformer is used to obtain encoded representations of knowledge graph entities. Then, a multimodal supervised method is used for learning the entity representations in the knowledge graph so that the vector representations of the entities contain rich multimodal semantic information, thereby enhancing the generalization ability of the learned entity representations. Finally, the information from different modalities is mapped to a shared low-dimensional subspace, making similar entities closer in the subspace, thus optimizing the entity alignment effect. The experiments on the DBP15K dataset compared with methods such as MTransE, JAPE, EVA, DNCN, etc., all achieve optimal results.

To investigate the influence of different grasping postures on the hand’s skin deformation, a handheld 3D EVA SCANNER was used to obtain 3D models of 111 women in five postures, including a straight posture and grasping cylinders with various diameters (4/6/8/10 cm). Skin relaxation strain ratio ( λ p ) and surface area skin relaxation strain ratio ( λ m ) were used as measures of skin deformation between two landmarks and multiple landmarks, respectively. The effects of grasping posture on skin deformation in different directions were analyzed. The results revealed significant variations in skin deformation among different grasping postures, except for the width of middle finger metacarpal and the length of middle finger’s proximal phalanx. The λ p increased with decreasing grasping object diameter, ranging from 5 to 18% on the coronal axis, and from 4 to 20% on the vertical axis. The overall variation of λ m ranged from 5 to 37.5%, following the same trend as λ p except for the surface area of tiger’s mouth, which exhibited a maximum difference of 10.9% with significant differences. These findings have potential applications in improving the design of hand equipment and understanding hand movement characteristics.

The management of local defects in municipal pipelines in water‐rich areas remains a significant challenge, particularly under high‐pressure conditions. This study investigates the performance of quick‐lock steel sleeves as a trenchless repair method through full‐scale experiments on five different pipeline diameters. The experiments focus on the failure modes and critical buckling pressure of the sleeves under external pressure. A novel design model based on structural reliability theory was developed and validated against experimental results. The results show a close match between the calculated and experimental buckling pressures, with a ratio ranging from 0.87 to 1.15. These findings provide valuable insights for the design and application of quick‐lock sleeves in high‐pressure municipal networks. This study contributes to improving the reliability and effectiveness of pipeline repair technologies, offering practical solutions for addressing pipeline leakage and instability in challenging environments. The study reveals high‐pressure buckling in quick‐lock sleeves and proposes a reliable design model. These research findings offer valuable guidance for the structural design and construction of localized repairs in municipal water supply and drainage networks.

The deep understanding about the photocatalytic reaction induced by the surface plasmon resonance (SPR) effect is desirable but remains a considerable challenge due to the ultrafast relaxation of hole-electron exciton from SPR process and a lack of an efficient monitoring system. Here, using the p-aminothiophenol (PATP) oxidation SPR-catalyzed by Ag nanoparticle as a model reaction, a radical-capturer-assisted surface-enhanced Raman spectroscopy (SERS) has been used as an in-situ tracking technique to explore the primary active species determining the reaction path. Hole is revealed to be directly responsible for the oxidation of PATP to p, p′-dimercaptoazobenzene (4, 4′-DMAB) and O 2 functions as an electron capturer to form isolated hole. The oxidation degree of PATP can be further enhanced through a joint utilization of electron capturers of AgNO 3 and atmospheric O 2 , producing p-nitrothiophenol (PNTP) within 10 s due to the improved hole-electron separation efficiency.

As the complexity of the cyber-physical systems (CPSs) increase, system modeling and simulation tend to be performed on different platforms where collaborative modeling activities are performed on distributed clients, while the simulations of systems are carried out in specific simulation environments, such as high-performance computing (HPC). However, there is a great gap between system models usually designed in system modeling language (SysML) and simulation code, and the existing model transformation-based simulation methods and tools mainly focus on either discrete or continuous models, ignoring the fact that the simulation of hybrid models is quite important in designing complex systems. To this end, a model transformation approach is proposed to simulate hybrid SysML models under a discrete event system specification (DEVS) framework. In this approach, to depict hybrid models, simulation-related meta-models with discrete and continuous features are extracted from SysML views without additional extension. Following the meta object facility (MOF), DEVS meta-models are constructed based on the formal definition of DEVS models, including discrete, hybrid and coupled models. Moreover, a series of concrete mapping rules is defined to transform the discrete and continuous behaviors based on the existing state machine mechanism and constraints of SysML, separately. Such an approach may facilitate a SysML system engineer to use a DEVS-based simulator to validate system models without the necessity of understanding DEVS theory. Finally, the effectiveness of the proposed method is verified by a defense system case.

Temperature-control loads, such as residential air conditioners (ACs) and electric water heaters (EWHs), have become important demand response resources in the power system. However, due to the impact of various factors on users’ response behavior, it has been difficult for power grid operators to accurately evaluate the response potential under complex factor relationships to derive optimal incentive strategy. Therefore, it cannot achieve a win-win economic benefit between the grid and users. In this paper, a method combining Analytic Network Process (ANP) and Fuzzy logical inference is proposed to predict the user’s willingness firstly by taking residential AC load as an example. The weight of each factor affecting users’ willingness is analyzed, and main factors are selected as inputs of fuzzy logic inference to derive the willingness of the resident to actively regulate the AC. Then, this method is applied in evaluating the response potential of certain residential area in Beijing according to the survey. By further considering users’ house size and the sacrificed comfort temperature under the incentive strategy, the power potential curve of the AC load under different incentives is obtained by using the first-order equivalent thermal parameter (ETP) model and the regulation willingness. Finally, with the objective of maximizing the consumption of the photovoltaic (PV) power, the optimal operation is achieved through the coordinated regulation of residential ACs and EWHs based on the potential curve, and the corresponding optimal incentive strategy for the flexible temperature-control loads is obtained. Simulation results show that the optimal incentive strategy proposed not only increases the PV consumption ratio to 98.35% with an increase of 24.71%, but also maximizes the economic benefits of both sides of the power grid and users. This method of deriving incentive strategy can be used as a reference for grid companies to formulate the incentive strategy to realize optimal operation, such as the maximum new energy consumption.

The selective ensemble aims to search the optimal subset balanced accuracy and diversity from the original base classifier set to construct an ensemble classifier with strong generalization performance. A selective ensemble classifier named BRFS-APCSC is proposed in this paper, which realizes the generation and selection of a set of accurate and diverse base classifiers respectively. In the first step, a multimodal perturbation method is introduced to train distinct base classifiers. The method perturbs the sample space by Bootstrap and disturbs the feature space under a newly proposed semi-random feature selection, which is a combination of the core attribute theory and the improved maximum relevance minimum redundancy algorithm. Then, to search the optimal classifier subset, affinity propagation clustering is added to cluster base classifiers in the first step, then the base classifiers are regarded as features so that the improved maximum relevance minimum redundancy algorithm is applied to select parts of base classifiers from each cluster for integration. UCI datasets and an actual dataset of semi-decarbonization are employed to verify the performance of BRFS-APCSC. The experimental results demonstrate that BRFS-APCSC has significantly difference with other selective ensemble methods and improve the classification accuracy.