Strategy to combine two functional components: Efficient nano material development for iodine immobilization

Real-time precision detection algorithm for jellyfish stings in neural computing, featuring adaptive deep learning enhanced by an advanced YOLOv4 framework

Introduction

Sea jellyfish stings pose a threat to human health, and traditional detection methods face challenges in terms of accuracy and real-time capabilities.

Methods

To address this, we propose a novel algorithm that integrates YOLOv4 object detection, an attention mechanism, and PID control. We enhance YOLOv4 to improve the accuracy and real-time performance of detection. Additionally, we introduce an attention mechanism to automatically focus on critical areas of sea jellyfish stings, enhancing detection precision. Ultimately, utilizing the PID control algorithm, we achieve adaptive adjustments in the robot's movements and posture based on the detection results. Extensive experimental evaluations using a real sea jellyfish sting image dataset demonstrate significant improvements in accuracy and real-time performance using our proposed algorithm. Compared to traditional methods, our algorithm more accurately detects sea jellyfish stings and dynamically adjusts the robot's actions in real-time, maximizing protection for human health.

Results and discussion

The significance of this research lies in providing an efficient and accurate sea jellyfish sting detection algorithm for intelligent robot systems. The algorithm exhibits notable improvements in real-time capabilities and precision, aiding robot systems in better identifying and addressing sea jellyfish stings, thereby safeguarding human health. Moreover, the algorithm possesses a certain level of generality and can be applied to other applications in target detection and adaptive control, offering broad prospects for diverse applications.

Motion sensitive network for action recognition in control and decision-making of autonomous systems

Spatial-temporal modeling is crucial for action recognition in videos within the field of artificial intelligence. However, robustly extracting motion information remains a primary challenge due to temporal deformations of appearances and variations in motion frequencies between different actions. In order to address these issues, we propose an innovative and effective method called the Motion Sensitive Network (MSN), incorporating the theories of artificial neural networks and key concepts of autonomous system control and decision-making. Specifically, we employ an approach known as Spatial-Temporal Pyramid Motion Extraction (STP-ME) module, adjusting convolution kernel sizes and time intervals synchronously to gather motion information at different temporal scales, aligning with the learning and prediction characteristics of artificial neural networks. Additionally, we introduce a new module called Variable Scale Motion Excitation (DS-ME), utilizing a differential model to capture motion information in resonance with the flexibility of autonomous system control. Particularly, we employ a multi-scale deformable convolutional network to alter the motion scale of the target object before computing temporal differences across consecutive frames, providing theoretical support for the flexibility of autonomous systems. Temporal modeling is a crucial step in understanding environmental changes and actions within autonomous systems, and MSN, by integrating the advantages of Artificial Neural Networks (ANN) in this task, provides an effective framework for the future utilization of artificial neural networks in autonomous systems. We evaluate our proposed method on three challenging action recognition datasets (Kinetics-400, Something-Something V1, and Something-Something V2). The results indicate an improvement in accuracy ranging from 1.1% to 2.2% on the test set. When compared with state-of-the-art (SOTA) methods, the proposed approach achieves a maximum performance of 89.90%. In ablation experiments, the performance gain of this module also shows an increase ranging from 2% to 5.3%. The introduced Motion Sensitive Network (MSN) demonstrates significant potential in various challenging scenarios, providing an initial exploration into integrating artificial neural networks into the domain of autonomous systems.

Three-Dimensional-Network-Structured Bismuth-Based Silica Aerogel Fiber Felt for Highly Efficient Immobilization of Iodine

The effective capture and deposition of radioactive iodine in the spent fuel reprocessing process is of great importance for nuclear safety and environmental protection. Three-dimensional (3D) fiber felt with structural diversity and tunability is applied as an efficient adsorbent with easy separation for iodine capture. Here, a bismuth-based silica aerogel fiber felt (Bi@SNF) was synthesized using a facile hydrothermal method. Abundant and homogeneous Bi nanoparticles greatly enhanced the adsorption and immobilization of iodine. Notably, Bi@SNF demonstrated a high capture capacity of 982.9 mg/g by forming stable BiI3 and Bi5O7I phases, which was about 14 times higher than that of the unloaded material. Fast uptake kinetics and excellent resistance to nitric acid and radiation were exhibited as a result of the 3D porous interconnected network and silica aerogel fiber substrate. Adjustable size and easy separation and recovery give the material potential as a radioactive iodine gas capture material.

Rapid, Highly-Efficient and Selective Removal of Anionic and Cationic Dyes from Wastewater Using Hollow Polyelectrolyte Microcapsules

Herein, poly (allylamine hydrochloride) (PAH)/ poly (styrene sulfonic acid) sodium salt (PSS) microcapsules of (PAH/PSS)2PAH (P2P MCs) and (PAH/PSS)2 (P2 MCs) were obtained by a layer-by-layer method. The P2 MCs show high adsorption capacity for Rhodamine B (642.26 mg/g) and methylene blue (909.25 mg/g), with an extremely low equilibrium adsorption time (~20 min). The P2P MCs exhibited high adsorption capacities of reactive orange K-G (ROKG) and direct yellow 5G (DY5G) which were 404.79 and 451.56 mg/g. Adsorption processes of all dyes onto microcapsules were best described by the Langmuir isotherm model and a pseudo-second-order kinetic model. In addition, the P2P MCs loaded with reactive dyes (P2P–ROKG), could further adsorb rhodamine B (RhB) dye, and P2 MCs that had adsorbed cationic MB dyes could also be used for secondary adsorption treatment of direct dye waste-water, respectively. The present work confirmed that P2P and P2 MCs were expected to become an excellent adsorbent in the water treatment industry.