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Object tracking can improve the performance of mobile robot especially in populated dynamic environments. A novel joint conditional random field Filter (JCRFF) based on conditional random field with hierarchical structure is proposed for multi-object tracking by abstracting the data associations between objects and measurements to be a sequence of labels. Since the conditional random field makes no assumptions about the dependency structure between the observations and it allows non-local dependencies between the state and the observations, the proposed method can not only fuse multiple cues including shape information and motion information to improve the stability of tracking, but also integrate moving object detection and object tracking quite well. At the same time, implementation of multi-object tracking based on JCRFF with measurements from the laser range finder on a mobile robot is studied. Experimental results with the mobile robot developed in our lab show that the proposed method has higher precision and better stability than joint probabilities data association filter (JPDAF).

An adaptive Monte Carlo localization algorithm based on coevolution mechanism of ecological species is proposed. Samples are clustered into species, each of which represents a hypothesis of the robot's pose. Since the coevolution between the species ensures that the multiple distinct hypotheses can be tracked stably, the problem of premature convergence when using MCL in highly symmetric environments can be solved. And the sample size can be adjusted adaptively over time according to the uncertainty of the robot's pose by using the population growth model. In addition, by using the crossover and mutation operators in evolutionary computation, intra-species evolution can drive the samples move towards the regions where the desired posterior density is large. So a small size of samples can represent the desired density well enough to make precise localization. The new algorithm is termed coevolution based adaptive Monte Carlo localization (CEAMCL). Experiments have been carried out to prove the efficiency of the new localization algorithm.

In multi-robot system the ability to exchange information can reduce the uncertainty in the estimated location when robots can see each other. In this paper, a kind of dynamically evolving coordination architecture is proposed for cooperative localization according to the relative positions between robots. And to further improve the efficiency of cooperative localization, a decision theory based mechanism is proposed to make the robots cooperate actively during the localization process. Since stably tracking the multi-hypothesis of the robots' own position and their partners' position is of great importance for making a good decision of where to go in active localization, the co-evolution based adaptive Monte Carlo localization method in which samples are clustered into species to represents a hypothesis of robot's pose in a higher level than a single sample is adopted. Experiments are designed and carried out to prove the efficiency and stability of the proposed method.

This paper presents the novel method of mobile robot simultaneous localization and mapping (SLAM), which is implemented by using the Rao-Blackwellised particle filter (RBPF) for monocular vision-based autonomous robot in unknown indoor environment. The particle filter is combined with unscented Kalman filter (UKF) to extending the path posterior by sampling new poses that integrate the current observation. The landmark position estimation and update is implemented through the unscented transform (UT). Furthermore, the number of resampling steps is determined adaptively, which seriously reduces the particle depletion problem. Monocular CCD camera mounted on the robot tracks the 3D natural point landmarks, which are structured with matching image feature pairs extracted through Scale Invariant Feature Transform (SIFT). The matching for multi-dimension SIFT features which are highly distinctive due to a special descriptor is implemented with a KDTree in the time cost of O(log2N). Experiments on the robot Pioneer3 in our real indoor environment show that our method is of high precision and stability.

In multi-robot system the ability to exchange information can reduce the uncertainty in the estimated location when robots can see each other. In this paper, a kind of dynamically evolving coordination architecture is proposed for cooperative localization according to the relative positions between robots. And to further improve the efficiency of cooperative localization, a decision theory based mechanism is proposed to make the robots cooperate actively during the localization process. Since stably tracking the multi-hypothesis of the robots' own position and their partners' position is of great importance for making a good decision of where to go in active localization, the co-evolution based adaptive Monte Carlo localization method in which samples are clustered into species to represents a hypothesis of robot's pose in a higher level than a single sample is adopted. Experiments are designed and carried out to prove the efficiency and stability of the proposed method.

This paper presents the novel method of mobile robot simultaneous localization and mapping (SLAM), which is implemented by using the Rao-Blackwellised particle filter (RBPF) for monocular vision-based autonomous robot in unknown indoor environment. The particle filter is combined with unscented Kalman filter (UKF) to extending the path posterior by sampling new poses that integrate the current observation. The landmark position estimation and update is implemented through the unscented transform (UT). Furthermore, the number of resampling steps is determined adaptively, which seriously reduces the particle depletion problem. Monocular CCD camera mounted on the robot tracks the 3D natural point landmarks, which are structured with matching image feature pairs extracted through Scale Invariant Feature Transform (SIFT). The matching for multi-dimension SIFT features which are highly distinctive due to a special descriptor is implemented with a KD-Tree in the time cost of O(log2 N ). Experiments on the robot Pioneer3 in our real indoor environment show that our method is of high precision and stability.

An adaptive Monte Carlo localization algorithm based on coevolution mechanism of ecological species is proposed. Samples are clustered into species, each of which represents a hypothesis of the robot's pose. Since the coevolution between the species ensures that the multiple distinct hypotheses can be tracked stably, the problem of premature convergence when using MCL in highly symmetric environments can be solved. And the sample size can be adjusted adaptively over time according to the uncertainty of the robot's pose by using the population growth model. In addition, by using the crossover and mutation operators in evolutionary computation, intra-species evolution can drive the samples move towards the regions where the desired posterior density is large. So a small size of samples can represent the desired density well enough to make precise localization. The new algorithm is termed coevolution based adaptive Monte Carlo localization (CEAMCL). Experiments have been carried out to prove the efficiency of the new localization algorithm.

Due to the interdisciplinary nature of robotics, more and more attention has been paid to its effectiveness in the field of education in recent years. This systematic review evaluated existing studies in improving K-12 students' computational thinking and STEM attitudes. Research articles published between 2010 and 2019 were collated from major databases according to six criteria, and 17 studies were eligible. A meta-analysis was conducted to evaluate the effectiveness of educational robots in terms of standardized mean differences (SMD) or mean differences (MD) of test scores as outcome measures. The overall effect size was medium (SMD = 0.46, 95% CI: 0.23-0.69). Subgroup analysis found that some groups to have better effectiveness. Specifically, the effect of STEM attitudes (SMD = 0.01) was smaller than computational thinking (SMD = 0.48). Educational robots had more significant effect on boys (MD = 0.39) than girls (MD = 0.27). The effect in primary school (SMD = 0.27) was higher than in middle school (SMD = 0.04), and the effect was great on short-term instruction with educational robots (SMD = 0.35). Based on these results, the study makes some recommendations for educators about strengthening the influence of educational robots on STEM attitudes, improving the persistence of their learning effects, and further exploring their application models.

Phorbol esters are recognized for their dual role as anti-HIV-1 agents and as activators of protein kinase C (PKC). The efficacy of phorbol esters in binding with PKC is attributed to the presence of oxygen groups at positions C20, C3/C4, and C9 of phorbol. Concurrently, the lipids located at positions C12/C13 are essential for both the anti-HIV-1 activity and the formation of the PKC-ligand complex. The influence of the cyclopropane ring at positions C13 and C14 in phorbol derivatives on their anti-HIV-1 activity requires further exploration. This research entailed the hydrolysis of phorbol, producing seco-cyclic phorbol derivatives. The anti-HIV-1 efficacy of these derivatives was assessed, and the affinity constant (K d) for PKC-δ protein of selected seco -cyclic phorbol derivatives was determined through isothermal titration calorimetry. The findings suggest that the chemical modification of cyclopropanols could affect both the anti-HIV-1 activity and the PKC binding affinity. Remarkably, compound S11, with an EC50 of 0.27 μmol·L−1 and a CC 50 of 153.92 μmol·L−1, demonstrated a potent inhibitory effect on the intermediate products of HIV-1 reverse transcription (ssDNA and 2LTR), likely acting at the viral entry stage, yet showed no affinity for the PKC-δ protein. These results position compound S11 as a potential candidate for further preclinical investigation and for studies aimed at elucidating the pharmacological mechanism underlying its anti-HIV-1 activity. [ABSTRACT FROM AUTHOR]