A Study of Plant-Filled Polymer Composites Based on Highly Plasticized Polyvinyl Chloride.

To enhance the ecological properties of polyvinyl chloride (PVC) products, the fabrication of PVC-based composites using biofillers with acceptable performance characteristics could be considered. In this work, plant-filled PVC-based composite materials were fabricated and their optical, structural, thermal, and mechanical properties, depending on the nature of the filler, were studied. Spruce flour, birch flour, and rice husk were used as fillers. Optical measurements showed the selected technological parameters, allowing films with a uniform distribution of dispersed plant filler in the polymer matrix to be obtained. Using the plant fillers in PVC films leads to a reduction in strength characteristics; for instance, the tensile strength changed from 18.0 MPa (for pure PVC film) to ~7 MPa (for composites with 20 wt.% of fillers), and to ~5-6.2 MPa (for composites with 40 wt.% of fillers). Thermal investigations showed that the samples with plant fillers could be used at low temperatures without changing their operating characteristics. Thus, plant-filled PVC-based composite materials have a wide operating temperature range, from-65 °C to 150 °C. TGA analysis has demonstrated that the rice husk affected the thermal stability of the composites by increasing their thermal decomposition resistance. The ability to absorb water was observed during the investigation of water absorption of the samples. And the highest degree of water absorption (up to 160 mg/g) was detected for the sample with 40 wt.% of rice husk. In general, plant-filled polymer composites based on PVC can be used on an equal basis with unfilled PVC plastic compounds for some applications such as in construction (for example, for design tasks).

Exploring Embodied Intelligence in Soft Robotics: A Review.

Soft robotics is closely related to embodied intelligence in the joint exploration of the means to achieve more natural and effective robotic behaviors via physical forms and intelligent interactions. Embodied intelligence emphasizes that intelligence is affected by the synergy of the brain, body, and environment, focusing on the interaction between agents and the environment. Under this framework, the design and control strategies of soft robotics depend on their physical forms and material properties, as well as algorithms and data processing, which enable them to interact with the environment in a natural and adaptable manner. At present, embodied intelligence has comprehensively integrated related research results on the evolution, learning, perception, decision making in the field of intelligent algorithms, as well as on the behaviors and controls in the field of robotics. From this perspective, the relevant branches of the embodied intelligence in the context of soft robotics were studied, covering the computation of embodied morphology; the evolution of embodied AI; and the perception, control, and decision making of soft robotics. Moreover, on this basis, important research progress was summarized, and related scientific problems were discussed. This study can provide a reference for the research of embodied intelligence in the context of soft robotics.

A review of constraints and adjustable parameters in microgrids for cost and carbon dioxide emission reduction.

In a world grappling with escalating energy demand and pressing environmental concerns, microgrids have risen as a promising solution to bolster energy efficiency, alleviate costs, and mitigate carbon emissions. This article delves into the dynamic realm of microgrids, emphasizing their indispensable role in addressing today's energy needs while navigating the hazards of pollution. Microgrid operations are intricately shaped by a web of constraints, categorized into two essential domains: those inherent to the microgrid itself and those dictated by the external environment. These constraints, stemming from component limitations, environmental factors, and grid connections, exert substantial influence over the microgrid's operational capabilities. Of particular significance is the three-tiered control framework, encompassing primary, secondary, and energy management controls. This framework guarantees the microgrid's optimal function, regulating power quality, frequency, and voltage within predefined parameters. Central to these operations is the energy management control, the third tier, which warrants in-depth exploration. This facet unveils the art of fine-tuning parameters within the microgrid's components, seamlessly connecting them with their surroundings to streamline energy flow and safeguard uninterrupted operation. In essence, this article scrutinizes the intricate interplay between microgrid constraints and energy management parameters, illuminating how the nuanced adjustment of these parameters is instrumental in achieving the dual objectives of cost reduction and Carbon Dioxide emission minimization, thereby shaping a more sustainable and eco-conscious energy landscape. This study investigates microgrid dynamics, focusing on the nuanced interplay between constraints and energy management for cost reduction and Carbon Dioxide minimization. We employ a three-tiered control framework-primary, secondary, and energy management controls-to regulate microgrid function, exploring fine-tuned parameter adjustments for optimal performance.

Delay estimation for cortical-muscular interaction with wavelet coherence time lag.

BACKGROUND: Cortico-muscular coherence (CMC) between the cerebral cortex and muscle activity is an effective tool for studying neural communication in the motor control system. To accurately evaluate the coherence between electroencephalogram (EEG) and electromyogram (EMG) signals, it is necessary to accurately calculate the time delay between physiological signals to ensure signal synchronization. NEW METHOD: We proposed a new delay estimation method, named wavelet coherence time lag (WCTL) and the significant increase areas (SIA) index as a measure of the specific region enhancement effect of the magnitude squared coherence (MSC) image. RESULTS: The grip strength level had a small effect on the information transmission time from the cortex to the muscles, while the transmission time from the cortex to different muscle channels was different for the same task. A positive correlation was found between the grip strength level and the SIA index on the ß band of C3-B and the α and ß bands of C3-FDS. COMPARISON WITH EXISTING METHOD: The WCTL method was found to accurately calculate the delay time even when the number of repeated segments was low in a simple motor control model, and the results were more accurate than the rate of voxels change (RVC) and CMC with time lag (CMCTL) methods. CONCLUSIONS: The WCTL is an effective method for detecting the transmission time of information between the cortex and muscles, laying the foundation for future rehabilitation treatment for stroke patients.

Application of artificial intelligence technologies in metallographic analysis for quality assessment in the shipbuilding industry.

The necessity to improve the metallographic analysis systems to automate diagnostics of the condition of the metals for all their characteristics has been substantiated. The metallographic analysis algorithm based on the use of neural networks for recognizing metal microstructures and a case-based reasoning approach for determining the metal grade is proposed. The structure of a multilayer neural network to determine the metals quantitative parameters has been developed. The recognizing results by neural networks for determining the metal quantitative parameters are shown. The high accuracy of determining the metals quantitative parameters by neural networks is presented. The specialized metallographic software to automate the recognition of metal microstructures and to determine the metal grade has been developed. Comparative results of carrying out metallographic studies with the developed neural network software to determine the metals quantitative parameters are shown.

Bipedal Walking of Underwater Soft Robot Based on Data-Driven Model Inspired by Octopus.

The soft organisms in nature have always been a source of inspiration for the design of soft arms and this paper draws inspiration from the octopus's tentacle, aiming at a soft robot for moving flexibly in three-dimensional space. In the paper, combined with the characteristics of an octopus's tentacle, a cable-driven soft arm is designed and fabricated, which can motion flexibly in three-dimensional space. Based on the TensorFlow framework, a data-driven model is established, and the data-driven model is trained using deep reinforcement learning strategy to realize posture control of a single soft arm. Finally, two trained soft arms are assembled into an octopus-inspired biped walking robot, which can go forward and turn around. Experimental analysis shows that the robot can achieve an average speed of 7.78 cm/s, and the maximum instantaneous speed can reach 12.8 cm/s.

A novel underwater bipedal walking soft robot bio-inspired by the coconut octopus.

In order to increase the compatibility between underwater robots and the underwater environment and inspired by the coconut octopus's underwater bipedal walking, a method was proposed for bipedal walking for an underwater soft robot based on a spring-loaded inverted pendulum (SLIP) model. Using the characteristics of octopus tentacles rolling on the ground, a wrist arm was designed using the cable-driven method, and an underwater SLIP bipedal walking model was established, which makes an underwater soft robot more suitable for moving on uneven ground. An underwater bipedal walking soft robot based on coconut octopus was then designed, and a machine vision algorithm was used to extract the motion information for analysis. Experimental analysis shows that the underwater bipedal walking robot can achieve an average speed of 6.48 cm s-1, and the maximum instantaneous speed can reach 8.14 cm s-1.

Application of Information Technologies and Programming Methods of Embedded Systems for Complex Intellectual Analysis.

An information model is outlined, which represents an intelligent system of metallographic analysis in the form of a set of subsystems, the interaction of which ensures the performance of metallographic analysis functions. The structure of the information storage subsystem for metallographic analysis is presented. The deployment model of an intelligent metallographic analysis system is proposed and described. The paper outlines the approach to the presentation of an expert subsystem for metallographic quality control of metals based on a neural network. The process of finding a close precedent in metallographic analysis with reference to a multilayer neural network is described. An intelligent metallographic analysis system is described, which based on proposed information model. A specialized software of an intelligent metallographic analysis system is presented. The functioning results of the developed system for processing images of steel microstructures to determine the steel quantitative parameters is presented.