This paper explores the movement mechanisms of microswimmers in low Reynolds number environments by analyzing key physical concepts, microbial swimming mechanisms, and methods of calculating swimming speed. The study is based on three significant documents: “Life at Low Reynolds Number,” “The Hydrodynamics of Swimming Microorganisms,” and “A Mathematical Explanation of an Increase in Bacterial Swimming Speed with Viscosity in Linear-Polymer Solutions.” The main focus is on understanding how microorganisms adapt to their environment. Methods for calculating swimming speed were translated into MATLAB code to simulate the effect of angle on swimming speed, and a model confirming microbial movement was designed through SOLIDWORKS and 3D printing. Two experiments using the model were conducted in two different fluid environments. The result demonstrated the influence of different fluid viscosity on swimming velocity and stability. Computational simulations and experiments offered valuable insights into the swimming mechanisms of microorganisms in surroundings with low Reynolds numbers.

Against the background of the global emphasis on carbon emission reduction, the reform of the European Union's carbon emission trading system (EU ETS) will be incorporated into the shipping industry from 2024, which will have a significant impact on the operation of shipping enterprises. In this context, China's shipping enterprises urgently need to choose an appropriate carbon emissions trading model to effectively respond to the impact of the EU ETS, so as to accelerate the low-carbon transformation. Based on this, this paper constructs the Gounod competitive game model of two shipping companies under the baseline scenario and the cooperative strategy scenario, takes into full consideration the key factors of the shipping companies' market potential, cost, carbon emission quota and price in the model, and researches on the choice of carbon quota trading modes of the shipping companies under the EU ETS, such as purchasing emission allowances or cooperating with other companies to obtain allowances, etc. The equilibrium transportation of the shipping companies under different scenarios is also studied, and the impacts of different scenarios are also examined. The equilibrium volume, price and profit of shipping companies under different scenarios are solved, and MATLAB is applied to numerically analyze the decision-making variables of the model to further analyze the potential impact of EU ETS on the shipping industry. The results show that EU ETS policies always have a negative impact on traditional shipping companies, while sustainable shipping companies are affected.

With the rapid development of power electronics technology, DC-DC converters are increasingly applied in areas such as electric vehicles and renewable energy systems. This paper focuses on two common DC-DC converters—Buck (Step-Down Converter) and Boost (Step-Up Converter)—and elaborates on their operating principles. Through literature analysis and comparative studies, this paper explores the application of wide-bandgap semiconductor materials, strategies for suppressing electromagnetic interference (EMI), and optimization methods such as synchronous rectification and multiphase interleaving topologies. Additionally, the importance of these two converters is analyzed in practical applications like electric vehicle charging systems and solar power systems. The results show that adopting new materials such as gallium nitride (GaN) and silicon carbide (SiC) and intelligent control strategies can effectively improve converter efficiency, reduce losses, and address technical challenges in high-frequency operation. This research provides theoretical and practical references for the design and application of high-performance DC-DC converters, contributing significantly to technological advancements in the renewable energy field.

This paper investigates the critical impact of industrial robotic arms on the shift towards intelligent manufacturing, a transition defined by Industry 4.0. We explore the ways in which these arms, characterized by their growing flexibility and accuracy, are transforming smart manufacturing by boosting productivity, safety, and comfort in the workplace. Our research covers the historical development of robotic arm technology, its significant improvements, and its use in diverse fields such as factory automation, space missions, medical operations, and agriculture. We evaluate how robotic arms influence the intelligent transformation of businesses, emphasizing their role in economic growth, eco-friendly practices, and the move to digital operations. The paper points out areas where further research is needed, especially regarding precision agriculture and cross-disciplinary studies, and proposes solutions to bridge these gaps. We also discuss a successful example of heavy-duty robots in car manufacturing and how government policies can drive intelligent transformation. Our findings highlight the importance of robotic arms in the evolution of smart manufacturing and stress the importance of ongoing innovation and the adoption of these technologies.

Modern robotics technology has become an indispensable part of modern manufacturing, widely applied in industries such as manufacturing and services. This article discusses the current development and research status of robotics across various fields. It concludes that in the industrial sector, welding robots enhance welding precision and quality; in logistics, industrial robots improve transportation efficiency; and in the electrical sector, inspection robots replace manual labour, reducing workload and enhancing safety. Additionally, in substations, inspection robots have replaced manual inspections, achieving cost reduction and efficiency improvement. However, challenges such as cost, precision, and safety still exist in the process of future technological iterations. Finally, this article highlights the potential of integrating artificial intelligence and 5G technology with robotics to drive future advancements, making robots more intelligent and autonomous. This integration aims to provide a comprehensive understanding of the applications of robotics in modern manufacturing and inspection systems.

As a common aquatic product, the freshness of bass directly impacts food safety and consumer experience. Traditional detection methods are limited by strong subjectivity and high destructiveness, making it difficult to meet the rapid and non-destructive testing requirements of modern food industry. Electronic nose technology provides an efficient solution for bass freshness detection by capturing characteristic volatile organic compounds produced during spoilage. This study systematically reviews the core aspects of this technology: in sample preparation, it standardizes the processing of fish meat and odor collection procedures; in system construction, it introduces the specific response mechanisms of gas sensor arrays to spoilage markers; in data analysis, it compares the application differences between traditional pattern recognition methods and deep learning algorithms. The study also identifies current industrial bottlenecks and future development directions. This study provides technical references for freshness detection in the bass and other aquatic industries, while also exploring technological innovations in the field of food freshness detection.

Humanity faces systemic challenges in key areas such as energy security and climate governance, so it’s crucial to develop and use clean energy. And the solid-state lithium battery shows great potential as an energy resource. However, the solid-state lithium battery has some deadly defects, like low efficiency and so on. This article mainly studies the interfacial electronic coupling effect that exists in the two-dimensional MXene heterostructures (mentioned as 2D MXene heterostructures in the following paragraphs) and their coordinative mechanism. So, the thesis aims to find the principle of the electronic coupling effect and the improvement that 2D MXene could make in the properties of solid-state lithium metal batteries. This paper mainly conducts the research by using the document analysis method. The result of the research shows that not only does the electron coupling effect occur at the MXene material and its heterostructures, but this phenomenon also helps to improve the properties of the battery.

With the rapid advancement of Internet of Things (IoT) technologies, intelligent greenhouses, as a critical component of modern agriculture, are progressively supplanting traditional farming practices and emerging as a cornerstone technology for precision agriculture. By integrating sensor technologies, automated control systems, cloud computing, and artificial intelligence algorithms, intelligent greenhouses facilitate precise monitoring and automated management of environmental parameters such as temperature, humidity, light intensity, and soil moisture. This integration not only enhances crop yield and quality but also minimizes resource wastage. Nevertheless, the effective amalgamation of these technologies to achieve intelligent greenhouse environment management remains a primary research focus and challenge. This paper provides a comprehensive review of the current research status of intelligent greenhouses based on IoT control technology, addressing the following aspects: first, the utilization of microcontrollers and sensor technologies for localized monitoring and control of greenhouse environments; second, the implementation of cloud computing to enable remote monitoring and management of greenhouse conditions; third, the optimization of greenhouse environmental control strategies through the integration of artificial intelligence algorithms; and finally, the design of compact intelligent greenhouses suitable for domestic cultivation. This study systematically examines the research progress of intelligent greenhouses leveraging IoT control technologies, offering technical insights and practical applications for researchers in the field. Furthermore, the proposed integration frameworks and future development directions presented herein hold significant implications for advancing the adoption and expansion of smart agriculture, thereby contributing to the realization of intelligent and sustainable agricultural production systems.

When renewable energy are connected to the power grid through power electronic technological (such as inverters), ensuring the stability of the power grid becomes a very valuable challenge. This is because power electronic converters usually encounter some problems when converting electrical energy, such as unstable voltage, the presence of high-frequency or low-frequency harmonics, islanding effect, leakage current, etc. To solve these problems and enhance the stability of power grids when new energy is connected, improvements can be made to the components and topological structure of power electronic modules, or more precise and comprehensive control strategies can be adopted. This paper outlines the topological structures and control strategies of inverters in photovoltaic grid-connected systems, including innovative topological structures, control algorithms, and analysis methods. It summarizes recent research achievements in improving grid stability through inverters, discovering several disadvantages and raising some possible development directions of inverters in the future.

With the increase in the proportion of wind energy in the power system, its intermittentity and volatility pose a challenge to the stable operation of the power system. Accurate wind forecasts are crucial. This paper proposes a wind energy prediction method for backpropagation (BP) neural networks which rely on the particle swarm optimization (PSO) algorithm improved. First, the year-on-year operational data of the wind farm, including multi-dimensional information, such as wind speed and direction, were standardized for pre-processing. The processed data are then inputted to the BP neural network model which is founded on the trained PSO and the initial weight and threshold are optimized by PSO. Case studies show that this method performs best in root square error, average absolute percentage error and accuracy compared to traditional methods such as BP neural network, support vector apparatus, and chronological series analysis, achieving more accurate and stable wind energy prediction.