3D Path Planning and Obstacle Avoidance Algorithms for Obstacle-Overcoming Robots

This article introduces a multimodal motion planning (MMP) algorithm that combines three-dimensional (3-D) path planning and a DWA obstacle avoidance algorithm. The algorithms aim to plan the path and motion of obstacle-overcoming robots in complex unstructured scenes. A novel A-star algorithm is proposed to combine the characteristics of unstructured scenes and a strategy to switch it into a greedy best-first strategy algorithm. Meanwhile, the algorithm of path planning is integrated with the DWA algorithm so that the robot can perform local dynamic obstacle avoidance during the movement along the global planned path. Furthermore, when the proposed global path planning algorithm combines with the local obstacle avoidance algorithm, the robot can correct the path after obstacle avoidance and obstacle overcoming. The simulation experiments in a factory with several complex environments verified the feasibility and robustness of the algorithms. The algorithms can quickly generate a reasonable 3-D path for obstacle-overcoming robots and perform reliable local obstacle avoidance under the premise of considering the characteristics of the scene and motion obstacles.Comment: 2nd IEEE International Conference on Electronic Communications, Internet of Things and Big Data Conference 2022 (IEEE ICEIB 2022

Evaluating and Enhancing Large Language Models for Conversational Reasoning on Knowledge Graphs

The development of large language models (LLMs) has been catalyzed by advancements in pre-training techniques. These models have demonstrated robust reasoning capabilities through manually designed prompts. In this work, we evaluate the conversational reasoning capabilities of the current state-of-the-art LLM (GPT-4) on knowledge graphs (KGs). However, the performance of LLMs is constrained due to a lack of KG environment awareness and the difficulties in developing effective optimization mechanisms for intermediary reasoning stages. We further introduce LLM-ARK, a LLM grounded KG reasoning agent designed to deliver precise and adaptable predictions on KG paths. LLM-ARK leverages Full Textual Environment (FTE) prompt to assimilate state information within each reasoning step. We reframe the challenge of multi-hop reasoning on the KG as a sequential decision-making task. Utilizing the Proximal Policy Optimization (PPO) online policy gradient reinforcement learning algorithm, our model is optimized to learn from rich reward signals. Additionally, we conduct an evaluation of our model and GPT-4 on the OpenDialKG dataset. The experimental results reveal that LLaMA-2-7B-ARK outperforms the current state-of-the-art model by 5.28 percentage points, with a performance rate of 36.39% on the target@1 evaluation metric. Meanwhile, GPT-4 scored 14.91%, further demonstrating the effectiveness of our method. Our code is available on GitHub (https://github.com/Aipura/LLM-ARK) for further access

Activity modulation and allosteric control of a scaffolded DNAzyme using a dynamic DNA nanostructure.

Recognition of the fundamental importance of allosteric regulation in biology dates back to not long after its discovery in the 1960s. Our ability to rationally engineer this potentially useful property into normally non-allosteric catalysts, however, remains limited. In response we report a DNA nanotechnology-enabled approach for introducing allostery into catalytic nucleic acids. Specifically, we have grafted one or two copies of a peroxidase-like DNAzyme, hemin-bound G-quadruplex (hemin-G), onto a DNA tetrahedral nanostructure in such a manner as to cause them to interact, modulating their catalytic activity. We achieve allosteric regulation of these catalysts by incorporating dynamically responsive oligonucleotides that respond to specific "effector" molecules (complementary oligonucleotides or small molecules), altering the spacing between the catalytic sites and thus regulating their activity. This designable approach thus enables subtle allosteric modulation in DNAzymes that is potentially of use for nanomedicine and nanomachines

International Conference «Tumor Hypoxia and Malignant Progression». October 1–4, 2008, House of Scientists of NASU, Kiev, Ukraine

Tumor hypoxia: pathophysiological and molecular mechanisms; Hypoxia and HIF-1: role in tumor progression; Evaluation of tumor oxygenation; Hypoxia-modulated tumor stroma; Hypoxia and glycolysis in tumor: mechanisms of interrelationship;Hypoxia and tumor-host interface; Hypoxia-induced metastasis; Tumor hypoxia: therapeutic resistance and hypoxia-related therapies; Hypoxia and prognosis of clinical outcom