In the environment of dual-carbon target, based on the design concept of zero-carbon substation, a kind of photovoltaic integrated prefabricated peripheral wall panel integrating high efficiency and low consumption, energy saving and environmental protection. The enclosed wall board can achieve the goal of zero carbon from three aspects: production, construction and use. In terms of production, recycled concrete is used; prefabricated wall panels are used for construction; in terms of use, prefabricated photovoltaic modules are integrated. On this basis, firstly, the bearing capacity analysis and test verification of the sandwich panel to provide a more comprehensive understanding of the stress performance of the sandwich panel and the wind load standard value to ensure the reliability of the photovoltaic modules, and finally, the integrated prefabricated peripheral wall panels to prove the feasibility of production and construction.

This collection of papers investigates different uses of IMU (Inertial Measurement Unit) technology in areas such as robotics, industry, and rehabilitation. In applications involving the compensation of robot flexibility and energetic balance control, IMU sensors, when paired with geometric models and Extended Kalman Filters (EKF), substitute conventional force sensors. This substitution enhances the real-time estimation of contact forces and moments. These methods, which operate at high frequencies, greatly strengthen the robot's balance and its ability to withstand external disturbances. Another key focus of the research is joint state estimation and posture control in robots. Here, IMU data, combined with other models, allows for accurate motion tracking and boosts energetic stability. Importantly, the fusion of IMU with vision and force sensors further improves motion capture accuracy. Utilizing IMU technology is not limited to robotics. In pediatric rehabilitation, it enhances motion recognition and engagement by giving therapists real-time feedback, thereby overcoming the limitations of vision-based systems such as Kinect. Similarly, in industrial contexts, wearable devices that use IMUs surpass traditional vision systems in gesture recognition within complex environments, yielding greater accuracy and energy efficiency. This illustrates the adaptability of IMUs across various domains, including robotic control, healthcare, and manufacturing processes.

Self-healing material has been widely used in many fields with its excellent properties and long material life. Some key parts of wearable devices such as smart sensors require the excellent properties of self-healing materials. Therefore, the application of self-healing materials in wearable devices has attracted more and more attention and brought destructive innovation in recent years. This review introduces the application of self-healing materials in wearable devices and discusses the chemical rationale in various application scenarios. So far, the most prevalent solution is to create multi-material matrices, strengthen the material, and sometimes perform extra properties. Research in this field is still in the initial stages and although there are still many technical hurdles to overcome to meet the needs of real-world applications, it presents a promising future and will eventually change people’s lives. This paper demonstrates that the cutting-edge technologies developed in self-healing wearable devices area make a great contribution to people’s life.

Graphene, a two-dimensional carbon nanomaterial with a single atomic layer, has piqued the interest of materials scientists for its remarkable mechanical strength, high thermal conductivity, and electron mobility. Electronic gadgets, composite materials, energy storage, and biomedicine have all exhibited significant interest in graphene since its initial successful synthesis through mechanical peeling in 2004 due to its unique features. Nevertheless, there are still challenges to using pure graphene due to its poor dispersibility, environmental stability, and electrical characteristics. In recent years, the introduction of specific functional groups or doping elements through chemical and biological modification has become a key method to improve the properties of graphene, including REDOX method, surface functional group modification, and biological macromolecular modification strategies. These modification methods significantly improve the hydrophicity, conductivity and biocompatibility of graphene and broaden its application prospects in flexible electronics, composite materials and energy storage devices. Therefore, the study provides a comprehensive overview of the most important functionalized modification approaches for graphene and assesses the benefits and drawbacks of using them in various industries. Research has shown that rational functional modification of graphene's physical and chemical characteristics can significantly enhance its potential for use in emerging electronic devices and energy storage systems.

Stock price prediction has always been a complex and challenging task in the financial field. This article proposes a novel method to optimize long short-term memory networks (LSTM) through a triangular topology aggregation optimizer, in order to improve the accuracy of stock price prediction. This method combines deep learning and advanced optimization techniques, aiming to provide more effective support for investment decisions in the stock market. We first introduced an optimized LSTM model and used a dataset for predicting stock prices. By observing the changes in loss between the training and validation sets, we found that the loss value of the training set gradually decreased from 0.04 to below 0.005 and approached convergence. Meanwhile, the loss value of the validation set remained below 0.005. This indicates that the performance of this model in stock price prediction is quite outstanding during both the training and validation phases. After analyzing the test set, the results showed that the predicted stock prices of this model were very close to the actual values, and could accurately predict market trends both numerically and trendwise. In addition, the MSE (mean square error) results obtained through the evaluation indicators of the model show that the MSE of the training set is 1.938, the MSE of the testing set is 1.944, and the RMSE (root mean square error) of the training set is 1.392, and the RMSE of the validation set is 1.394. These results indicate that the evaluation metrics of the training set and the test set are not significantly different, further proving that the model has strong generalization ability and can continue to demonstrate good predictive performance on new datasets. In summary, this article proposes an effective stock price prediction method by combining a triangular topology aggregation optimizer with an LSTM model, and verifies its efficiency and practicality in the training, validation, and testing stages. This type of research not only provides new ideas for analyzing the stock market, but also provides strong support for investors to make more reasonable decisions in a dynamic market environment.

This paper reviews the research progress on nanomaterials used as food packaging materials, focusing on their structural and functional characteristics. The study initially discusses the properties of organic nanomaterials, such as nanocellulose, and inorganic nanomaterials, including nano metal oxides and nano silicon dioxide, emphasizing how their unique properties differentiate them from conventional packaging materials. Nanomaterials exhibit superior mechanical strength, barrier properties, antibacterial activity, and environmental friendliness compared to traditional petroleum-based packaging, which is challenging to degrade and environmentally polluting. Organic nanomaterials, such as nanocellulose, have demonstrated particular promise for enhancing food quality and safety, while inorganic options provide additional stability and durability. Despite these advantages, nanomaterial applications in food packaging raise potential toxicological concerns and environmental health risks that require further investigation and regulation. Technical and cost-related challenges also remain as barriers to their widespread adoption.

This data analysis focuses on examining the trends and patterns that currently exist within animal migration and population dynamics in response to the change in global climate. Climate change is known as one of the most concerning issues that have come along with the rapid development of infrastructure, technology, and advancements. This research paper was conducted using the Ecological Niche Model theoretical framework. Due to the large amounts of resources available online, one of the well-documented taxonomic groups and most sensitive to climate change was selected: Anatidae. After a thorough analysis, it was found that the trend of migratory patterns, breeding patterns, and population dynamics, with the increase in global temperature, have been through a significant shift in Anatidae.

As the scale of power systems keeps expanding and load characteristics are growing more complex, precise power load forecasting has emerged as a crucial link in guaranteeing the safe, stable and economical operation of power systems. Deep learning algorithms, with their powerful feature learning and complex non-linear relationship processing capabilities, have achieved remarkable results in the field of power load forecasting. This paper comprehensively reviews the applications of various deep learning algorithms in power load forecasting by combining the characteristics of power systems. It evaluates the accuracy and feasibility of the forecasts, aiming to provide a reference for subsequent research and promote further development in this field.

Pb in traditional brazing materials Sn-Pb is highly toxic, and leaded brazing materials have been gradually banned in order to minimize the hazards. Research on lead-free solder has made great progress in the past decades, among which SAC alloys are the most representative. SAC has gained wide attention and research because of its relatively good performance, this paper analyzes the performance of SAC alloys with different compositions and alloys doped with different elements, and gives an overview of the latest research on SAC brazing materials in recent years. Due to the problem of the high melting temperature of SAC braze in the field of complex architecture packaging, this paper also classifies and summarizes the low-temperature brazes proposed to solve the high melting temperature of SAC, analyzes the advantages and shortcomings of two kinds of brazes, Sn-based and In-based, and lists the ones that have been previously doped with different elements in different systems, and discusses the possible directions of future research.

Green hydrogen is key as a sustainable energy solution amid fossil fuel use, transitioning from harmful finite resources. However, the instability of renewable energy sources and the high cost of the production process constrain the further development of green hydrogen. This study presents an optimization model of a tidal-wind hybrid system for hydrogen production with a focus on maximizing the economic benefits. By modelling an off-grid renewable energy system in the Scottish region, the results of the study show that the hybrid tidal-wind system has the lowest annual total cost of 5,000.19 M$ and the hydrogen cost is 12.347 $/kg, which are both better than other single energy systems. In addition, the CO2 emissions of the hybrid system are significantly lower than other blue-hydrogen production systems, helping to drive the transition to sustainable energy. Sensitivity analysis shows that the efficiency of electrolyzers and fuel cells has the most significant impact on system costs. Also, the hybrid tidal-wind system stabilizes power output by reducing the need for expensive auxiliary power generation and storage systems. This hybrid approach improves economic viability in remote areas and advances the goal of a sustainable energy transition.