The vibration is dangerous factor in machinery and construction. As it causes the damages to the machines and even humans. In these cases, people have developed many different kinds of technologies that used to minimize the influence of vibration on the parts of machines and human who operate the machine or construct the devices. In fact, means for vibration isolation contain two main types, the active and the passive methods. Also, there are other solutions, which combining the AI techniques with the passive and active isolation vibration means, not only increasing the effectivity of each method, but also decline the cost of devices. This essay mainly summarizes how passive and active vibration isolation methods take their advantages to create a more stable environment of working and how AI methods just raise the ceiling of the final effect of each method. Moreover, there still some problems exist, so some direction of future research and possible solution also included in this essay.

In recent years, the ongoing advancement of society and technology has placed increasingly higher demands on battery performance. Currently, in commercial lithium-ion batteries (LIBs), graphite serves as the most widely used anode material. However, its specific capacity remains relatively low, making it difficult to satisfy evolving development requirements. However, carbon-based materials have a better theoretical specific capacity. When used as anode materials, they can significantly improve the battery performance, showing great development prospects. This article describes a detailed review of the research progress of carbon-based anode materials of LIBs. Firstly, we introduced the principle of LIBs, the overall classification of carbon-based materials and their characteristics. Furthermore, various modification methods of carbon-based anode materials for LIBs are discussed, including activation, doping, and composite with metal materials. Finally, the future research directions of carbon-based anode materials for LIBs are prospected.

The coordination of wind, PV and PV systems in electricity networks has become one of the most important drivers for the green growth of electricity systems as the energy transformation and carbon neutral initiatives are being taken forward. In this article, we discuss the Intelligent Wind Solar Storage Network with a detailed description of its basic structure, operating principles, and present development situation. Based on the analysis of documents and examples, we have found that Model Predictive Control (MPC) and DRL (DRL) have proved to be economically efficient and flexible in distributing and storing power. Based on the examples from Qinghai, German and Shanghai, we find that the integration of wind and solar energy can increase the efficiency of RES and keep the electricity network stable. The research shows that the integration of artificial intelligence and the new generation of communication technology will become a key direction for the intelligent and coordinated development of wind-solar-storage systems in the future.

This research develops a lightweight and highly reproducible optimization model for residential solar-storage carport system operating in a carbon-pricing environment. By integrating the real-time carbon price into the system's scheduling decisions, a genetic algorithm is used to simultaneously optimize photovoltaic (PV) and battery storage capacity, as well as the day-ahead dispatch strategy. This optimization framework maximizes electricity export revenues while minimizing total operational costs, including purchased electricity costs, carbon emissions and peak-valley gap penalties. With the current prevalent CO₂ price of 80 RMB/ton, a system configuration with 6.3 kW PV capacity and 5 kW·h/3 kW storage battery achieves an investment payback period of 2.5 years and an internal rate of return of around 40%. Sensitivity analysis reveals that the introduction of a carbon price promotes an increase in electricity sales and improves the financial viability of the system. The results demonstrate that carbon pricing not only increases the economic viability of the system but also promotes more efficient energy use through optimized trading strategies. MATLAB-based template for homes to take part in carbon markets while cutting electricity costs, presenting a route to a more sustainable and economical energy future.

This review summarizes the core advantages and limitations of berberine, a naturally derived alkaloid, around three key aspects: synthesis, mechanism of action, and application. In synthesis, its strength lies in mature natural extraction processes that support stable and low-cost production, while its limitation stems from difficult chemical synthesis and reliance on plant sources that bring sustainability concerns. In terms of mechanism, berberine shows value in multi-target regulation that enables diverse therapeutic effects, yet its efficacy is restricted by poor oral bioavailability that requires higher doses. In application, it demonstrates potential in managing metabolic disorders, infections, and inflammatory conditions, but is accompanied by common side effects, drug interaction risks, and insufficient long-term research data.

Paclitaxel, branded as Taxol and Abraxane, is a nature-derived chemotherapy drug originally extracted from the Pacific yew. In contrast to previous attempts at combating cancer, Paclitaxel can effectively stop cancer cells division by stabilizing mitotic spindles. That’s why it’s still one of the most widely used drugs in cancer treatment today. This paper explores Paclitaxel’s history, mechanism of action, synthesis routes, metabolism in the human body, side effects, and its success rate. In the future, scientists are looking forward to improving Paclitaxel’s delivery, reducing its side effects, and overcoming resistance in certain cancers. This paper does not intend to be a comprehensive report on this amazing molecule. It is meant to be an introductory article to help more people understand Paclitaxel.

Rear wings play a critical role in Formula 1 race cars by generating a large proportion of the total aerodynamic downforce, and this study examines the aerodynamics of a dual-element rear wing within a Formula 1 framework using a combined methodology that includes theoretical analysis, computational fluid dynamics (CFD), design of experiments (DoE), and wind-tunnel testing. A two-stage Taguchi approach is employed to efficiently identify and optimize key geometric parameters with the aim of improving the lift-to-drag ratio while maintaining sufficient downforce. The theoretical analysis provides a physical basis for interpreting the behavior of multi-element wing systems, while CFD results show that the optimized configuration achieves improved aerodynamic efficiency through balanced pressure recovery, controlled boundary-layer development, and reduced vortex-induced losses. Wind-tunnel experiments are used to validate the numerical predictions, and a close agreement in lift-to-drag ratio is observed despite differences in Reynolds number. Overall, the results indicate that the flap element angle of attack is the dominant factor affecting aerodynamic efficiency, whereas camber distribution mainly controls the trade-off between downforce and drag, with a moderate camber of approximately 4% offering the most favorable compromise. Flow-field analyses link superior performance to favorable pressure recovery, reduced viscous dissipation, and controlled vortex dynamics, while experiments highlight the sensitivity of dual-element wings to off-design angles of attack. In the end, through numerous optimization steps, this study produced a package of four rear wing profiles, all with a downforce-to-drag ratio above 10, with the best case having a ratio of 11.88. On top of the high efficiency, these rear wing profiles alone produced at least 564.68 Newtons and up to 816.91 Newtons of downforce at a Reynolds number of around1.2×106. The success in optimizing aerodynamic efficiency while generating substantial downforce verifies the viability and efficacy of the methods presented in this study. Future work should explore other potential factors impacting dual-element rear wing performance, including different camber and thickness parameters, concavity control, and gurneys, to develop a more comprehensive aerodynamic performance map.

In the context of global energy transition, lithium-ion batteries, as the core of the new energy industry, have always attracted much attention due to their performance and safety issues. The thermoelectrochemical coupling effect generated during the operation of lithium-ion batteries, as well as the characteristics of the solid electrolyte interface (SEI) film, have a direct and crucial impact on the lifespan, performance, and safety of the batteries. The world is currently promoting energy transition, and lithium-ion batteries are the core component in the new energy field. Everyone is very concerned about whether their performance is good and whether they are safe to use. In addition, during battery operation, there is a thermoelectrochemical interaction, and a substance called SEI film is involved. These two factors have particularly direct and crucial influences on how long the battery can be used, its performance, and its safety level. And numerical simulation methods not only require fewer resources but also have a shorter experimental cycle. Nowadays, they have become an important means for studying the thermal behavior during the discharge process of lithium-ion batteries.

This paper focuses on the need to control aerosols generated during pipeline cutting in nuclear facility decommissioning. A self-fracturing aerosol atomization fixing agent has been developed. Styrene (St) is used as the hard monomer, and methyl methacrylate (MMA) and methacrylic acid (MAA) are used as functional monomers. The fixing agent lotion and the properties of the formed film, as well as their impact on the capture performance of titanium dioxide aerosols, are investigated through emulsion polymerization synthesis. The results show that the fixing agent achieves a 93.17% capture rate for titanium dioxide aerosols within 6 hours. After being fixed by the fixing agent, the aerosol resuspension rate is only 5.10%. Moreover, the formed film can spontaneously fragment into pieces smaller than 4 cm² within 3 hours. This fixing agent possesses good capture efficiency and self-fracturing characteristics, providing an effective and easy-to-operate technical approach with minimal secondary pollution for the safe control of radioactive aerosols.

As mental health has moved to the core of public-health agendas, policy documents, media reports, service records, and citizen accounts together form a vast multimodal narrative space. However, heterogeneity of sources, loose structure, and the lack of unified encoding prevent these narratives from being systematically integrated into existing evidence frameworks, limiting the granularity and adaptiveness of mental-health policymaking. To address this problem, this study constructs a multimodal public-health narrative dataset for 2015–2025 that combines policy texts, news and social-media narratives, and strictly de-identified service statistics, and proposes a multimodal narrative-understanding framework centered on a large language model. By jointly modeling text, images, and structured indicators, the framework performs narrative entity and causal-chain extraction, generates evidence pointers, and assembles auditable policy-evidence packages. A multi-layer evaluation and audit pipeline is designed to cover factual consistency, narrative-structure quality, policy usability, and fairness. Experimental results show that the proposed framework substantially outperforms a strong baseline on source recall accuracy (91.2%), evidence coverage (81.7%), causal-chain completeness (0.79), and cross-modal consistency (0.88). Across three cities, double-blind expert review yields a mean structural score of 4.2 out of 5, while the maximum subgroup difference in factual-consistency scores is limited to 4.7 percentage points. These findings indicate that combining multimodal narrative understanding with traceable audit mechanisms can transform fragmented public-health narratives into structured and verifiable evidence for mental-health policy and offers a practical route for the cautious deployment of large models in high-risk policy contexts.