Forecasting serves as a critical cornerstone for strategic planning, operational efficiency, and risk mitigation across modern civilization. By converting historical data into actionable forward-looking insights, it enables organizations and governments to anticipate market shifts, optimize resource distribution, and safeguard against systemic uncertainties. Predictive modeling is a fundamental task in many fields, such as finance, economics, engineering, and artificial intelligence. The aim of this paper is to summarize the methods of statistics and machine learning, outline their inherent challenges, and project future research directions. This paper mainly discusses traditional statistical methods (including Autoregressive Integrated Moving Average [ARIMA] and regression analysis), machine learning approaches (such as Random Forest and Support Vector Machines [SVM]), and deep learning models (such as Long Short-Term Memory [LSTM] networks and hybrid time series-ML models). Nowadays, as these interconnected fields become increasingly complicated, practitioners face severe challenges regarding data quality, computational complexity, and mathematical interpretability. This paper comprehensively reviews these methodologies, establishes a comparative taxonomy, and delineates the evolutionary trajectory of future forecasting applications.

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As global energy demand rises, fossil fuel combustion has caused severe CO₂ emissions and environmental pollution. Hydrogen energy has gained growing attention. Fuel cell as a new technology, has developed fast recently. The paper mainly introduces two types of fuel cell. Direct methanol fuel cell and proton membrane fuel cell. The proton exchange membrane fuel cell (PEMFC), which converts hydrogen into electricity and produces only water and heat as by-products, offers high efficiency and zero emissions, making it a key technology for energy transformation. However, PEMFC's commercialization faces three major barriers: high platinum catalyst costs, loss of membrane conductivity above 80 °C, and limited durability. This paper reviews recent progress over the past five years, it also briefly discusses global fuel cell developments in Japan, the United States, Europe, and South Korea, as well as direct methanol fuel cells (DMFCs) as a related technology. Future challenges include cost reduction, limited durability and material recycling. These advances have laid a solid foundation for PEMFCs to play a significant role in the clean energy transition.

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Carbon neutral countries have listed photovoltaic technology as an energy priority. Solar cells have attracted much attention as important photoelectric conversion devices. Therefore, this article introduces the basic physics of photovoltaic effect, common methods for classifying solar cells, and the true advantages and limitations of major photovoltaic technologies. Research has found that crystalline silicon maintains its market leading position thanks to mature production lines and long-life modules. However, perovskite exhibits impressive rapid efficiency growth and supports solution coatings with adjustable band gaps, although stability issues and risks associated with heavy metal lead remain unresolved. In addition, organic, dye-sensitized, and quantum dot batteries have unique advantages, especially for flexible or semi-transparent devices. In the future, solar cells should maintain higher efficiency, lower costs, and wider adoption. Researchers can develop improved series architectures, scalable large-area coating technologies, stronger stability, and robust lifecycle assessment frameworks. The aim is simply to give researchers and engineers a short, physically grounded picture of how solar cells work and where they seem to be heading.

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With the rapid development of electric vehicles and large-scale energy storage systems, conventional lithium-ion batteries may be incapable of meeting the gradually increasing energy demands. Accordingly, lithium-sulfur batteries, which feature an ultra-high theoretical energy density, have emerged as a research hotspot worldwide. This review focuses on various strategies for modifying lithium-sulfur batteries using nanomaterials to mitigate the internal polysulfide shuttle effect. This study aims to systematically sort out various effective modification approaches and clarify their underlying functional mechanisms. By adopting the literature analysis method, this work collects, organizes and compares experimental research findings from the past decade concerning two major modification schemes: nanostructured cathode hosts and functional separators. The study reveals that carbon nanotubes combined with polar metal composite materials can achieve a synergistic effect involving a physical barrier, chemical polysulfide trapping and electrocatalysis, which substantially reduces the capacity decay rate and improves the Coulombic efficiency. This review also summarizes several widely recognized bottlenecks for the industrialization of lithium-sulfur batteries and proposes a few directions for future research based on existing literature. Additionally, it aims to accelerate the commercialization process of lithium-sulfur batteries.

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