Nanocomposite electret with surface potential self-recovery from water dipping for environmentally stable energy harvesting

Due to their high charge densities, electret materials have gained extensive attention in recent years for their abilities to harvest mechanical energy. However, the environmental stability of electret materials is still a major concern for real applications. Here, we report a thin-film nanocomposite electret material (NCEM) that exhibits immediate and effective self-recovery of the surface potential after water dipping. The NCEM is composed of a polytetrafluoroethylene (PTFE) film, a nanocomposite film with PTFE nanoparticles as the nanofiller and polydimethylsiloxane (PDMS) as the matrix. The surface potential of the NCEM resulting from corona charging could be stably maintained with very little decay of 2% after 25 days. More importantly, the surface potential exhibited quick self-recovery to 75% and 90% of its initial value after 10 min and 60 min, respectively, when the NCEM was removed from water. A 70% self-recovery was still observed even when the NCEM was dipped in water for 200 cycles. When used in electret nanogenerators (ENGs), the electric output recovered to 90% even when the ENG experienced water dipping. Therefore, this work presents a key step towards developing high-performance and environmentally stable energy harvesting nanogenerators that can survive harsh conditions for real applications

A Game Theory-Based Obstacle Avoidance Routing Protocol for Wireless Sensor Networks

The obstacle avoidance problem in geographic forwarding is an important issue for location-based routing in wireless sensor networks. The presence of an obstacle leads to several geographic routing problems such as excessive energy consumption and data congestion. Obstacles are hard to avoid in realistic environments. To bypass obstacles, most routing protocols tend to forward packets along the obstacle boundaries. This leads to a situation where the nodes at the boundaries exhaust their energy rapidly and the obstacle area is diffused. In this paper, we introduce a novel routing algorithm to solve the obstacle problem in wireless sensor networks based on a game-theory model. Our algorithm forms a concave region that cannot forward packets to achieve the aim of improving the transmission success rate and decreasing packet transmission delays. We consider the residual energy, out-degree and forwarding angle to determine the forwarding probability and payoff function of forwarding candidates. This achieves the aim of load balance and reduces network energy consumption. Simulation results show that based on the average delivery delay, energy consumption and packet delivery ratio performances our protocol is superior to other traditional schemes

GPT4Video: A Unified Multimodal Large Language Model for lnstruction-Followed Understanding and Safety-Aware Generation

While the recent advances in Multimodal Large Language Models (MLLMs) constitute a significant leap forward in the field, these models are predominantly confined to the realm of input-side multimodal comprehension, lacking the capacity for multimodal content generation. To fill this gap, we present GPT4Video, a unified multi-model framework that empowers Large Language Models (LLMs) with the capability of both video understanding and generation. Specifically, we develop an instruction-following-based approach integrated with the stable diffusion generative model, which has demonstrated to effectively and securely handle video generation scenarios. GPT4Video offers the following benefits: 1) It exhibits impressive capabilities in both video understanding and generation scenarios. For example, GPT4Video outperforms Valley by 11.8\% on the Video Question Answering task, and surpasses NExt-GPT by 2.3\% on the Text to Video generation task. 2) it endows the LLM/MLLM with video generation capabilities without requiring additional training parameters and can flexibly interface with a wide range of models to perform video generation. 3) it maintains a safe and healthy conversation not only in output-side but also the input side in an end-to-end manner. Qualitative and qualitative experiments demonstrate that GPT4Video holds the potential to function as a effective, safe and Humanoid-like video assistant that can handle both video understanding and generation scenarios

Interplay between Lefty and Nodal signaling is essential for the organizer and axial formation in amphioxus embryos

Abstract(#br)The organizer is an essential signaling center required for axial formation during vertebrate embryonic development. In the basal chordate amphioxus, the dorsal blastopore lip of the gastrula has been proposed to be homologous to the vertebrate organizer. Lefty is one of the first genes to be expressed in the organizer. The present results show that Lefty overexpression abolishes the organizer; the embryos were severely ventralized and posteriorized, and failed to develop anterior and dorsal structures. In Lefty knockouts the organizer is enlarged, and anterior and dorsal structures are expanded. Different from Lefty morphants in vertebrates, amphioxus Lefty mutants also exhibited left-right defects. Inhibition of Nodal with SB505124 partially rescued the effects of Lefty loss-of-function on morphology. In addition, while SB505124 treatment blocked Lefty expression in the cleavage stages of amphioxus embryos, activation of Nodal signaling with Activin protein induced ectopic Lefty expression at these stages. These results show that the interplay between Lefty and Nodal signaling plays an essential role in the specification of the amphioxus organizer and axes

A Network Coding Based Routing Protocol for Underwater Sensor Networks

Due to the particularities of the underwater environment, some negative factors will seriously interfere with data transmission rates, reliability of data communication, communication range, and network throughput and energy consumption of underwater sensor networks (UWSNs). Thus, full consideration of node energy savings, while maintaining a quick, correct and effective data transmission, extending the network life cycle are essential when routing protocols for underwater sensor networks are studied. In this paper, we have proposed a novel routing algorithm for UWSNs. To increase energy consumption efficiency and extend network lifetime, we propose a time-slot based routing algorithm (TSR).We designed a probability balanced mechanism and applied it to TSR. The theory of network coding is introduced to TSBR to meet the requirement of further reducing node energy consumption and extending network lifetime. Hence, time-slot based balanced network coding (TSBNC) comes into being. We evaluated the proposed time-slot based balancing routing algorithm and compared it with other classical underwater routing protocols. The simulation results show that the proposed protocol can reduce the probability of node conflicts, shorten the process of routing construction, balance energy consumption of each node and effectively prolong the network lifetime

Confined FeNi alloy nanoparticles in carbon nanotubes for photothermal oxidative dehydrogenation of ethane by carbon dioxide

Oxidative dehydrogenation of ethane with CO2 (ODEC) is an attractive reaction for reduction of carbon footprints and ethene production. In this work, we present photothermal catalysis on confined bimetal catalysts for ODEC. Carbon nanotubes confined non-noble bimetal alloy (i.e., CoNi@CNTs and FeNi@CNTs) catalysts were prepared and FeNi@CNTs showed effective performance in photothermal catalytic ODEC to ethene. Experiments and simulations reveal that UV and visible lights (420 – 490 nm) are responsible for ODEC and non-oxidative dehydrogenation of ethane, respectively, to ethene. Additionally, ODEC to ethene is preferred to C-C cracking to methane on FeNi@CNTs in light ( \u3e 490 nm)-induced thermocatalysis. The photothermal effect turns more significant when introduced into thermocatalytic ODEC (500 °C), with ethene generation at one order of magnitude. This work advances new mechanism of photo-mediated catalysis and sheds light on utilization of full-spectrum solar energy and non-noble metallic catalysts for ethene production and CO2 recycling at moderate conditions

NTU4DRadLM: 4D Radar-centric Multi-Modal Dataset for Localization and Mapping

Simultaneous Localization and Mapping (SLAM) is moving towards a robust perception age. However, LiDAR- and visual- SLAM may easily fail in adverse conditions (rain, snow, smoke and fog, etc.). In comparison, SLAM based on 4D Radar, thermal camera and IMU can work robustly. But only a few literature can be found. A major reason is the lack of related datasets, which seriously hinders the research. Even though some datasets are proposed based on 4D radar in past four years, they are mainly designed for object detection, rather than SLAM. Furthermore, they normally do not include thermal camera. Therefore, in this paper, NTU4DRadLM is presented to meet this requirement. The main characteristics are: 1) It is the only dataset that simultaneously includes all 6 sensors: 4D radar, thermal camera, IMU, 3D LiDAR, visual camera and RTK GPS. 2) Specifically designed for SLAM tasks, which provides fine-tuned ground truth odometry and intentionally formulated loop closures. 3) Considered both low-speed robot platform and fast-speed unmanned vehicle platform. 4) Covered structured, unstructured and semi-structured environments. 5) Considered both middle- and large- scale outdoor environments, i.e., the 6 trajectories range from 246m to 6.95km. 6) Comprehensively evaluated three types of SLAM algorithms. Totally, the dataset is around 17.6km, 85mins, 50GB and it will be accessible from this link: https://github.com/junzhang2016/NTU4DRadLMComment: 2023 IEEE International Intelligent Transportation Systems Conference (ITSC 2023