Search

Recently, large‐scale language models (LLMs) such as chat generative pre‐trained transformer and generative pre‐trained transformer 4 have demonstrated remarkable performance in the general domain. However, inadaptability in a particular domain has led to hallucination for these LLMs when responding in specific domain contexts. The issue has attracted widespread attention, existing domain‐centered fine‐tuning efforts have predominantly focused on sectors like medical, financial, and legal, leaving critical areas such as power energy relatively unexplored. To bridge this gap, this paper introduces a novel power energy chat model called PowerPulse. Built upon the open and efficient foundation language models (LLaMA) architecture, PowerPulse is fine‐tuned specifically on Chinese Power Sector Domain Knowledge. This work marks the inaugural application of the LLaMA model in the field of power energy. By leveraging pertinent pre‐training data and instruction fine‐tuning datasets tailored for the power energy domain, the PowerPulse model showcases exceptional performance in tasks such as text generation, summary extraction, and topic classification. Experimental results validate the efficacy of the PowerPulse model, making significant contributions to the advancement of specialized language models in specific domains. [ABSTRACT FROM AUTHOR]

Energy demand forecast has an important practical significance for the sustainable development of the national economy, the reasonable allocation of resources, and the construction of modernization goals. This study is based on the analysis of coal, electricity, natural gas, and other energy data in Yunnan Province from 2011 to 2018 and uses long short-term memory, sequence to sequence, deep learning, and ridge regression coupling methods to construct an energy demand forecast model in Yunnan Province. Forecast results show the following. The total energy consumption of Yunnan Province from 2021 to 2025 will continue to increase. Moreover, the coal consumption of Yunnan Province will continue to decline from 2021 to 2025. Furthermore, the electricity consumption of Yunnan will increase by about 8.02% year-on-year from 2021 to 2025. The experiment proves that the forecasting effect of the energy demand forecast model proposed in this study is excellent.

In order to forecast the number of electric vehicles in Yunnan Province, based on BASS model, this paper uses extensive analogy method to explore the acquisition of m, p and q model parameters, forecasts the purchasing power of the market, and estimates the innovation coefficient and imitation coefficient from three aspects of high potential scenario, base potential scenario and low potential scenario. The number of new energy electric vehicles in Yunnan Province in three scenarios from 2022 to 2035 is predicted. The forecast results show that under the condition of high potential development, the number of new energy vehicles in Yunnan Province will reach 409,600 in 2022; in the case of benchmark potential development, the number of new energy vehicles will reach 291,400 in 2022; in the case of low potential development, the number of new energy vehicles in Yunnan Province will reach 155,400 in 2022.

Background: With the extended life expectancy of the Chinese population and improvements in surgery and anesthesia techniques, the number of aged patients undergoing surgery has been increasing annually. However, safety, effectiveness, and quality of life of aged patients undergoing surgery are facing major challenges. In order to standardize the perioperative assessment and procedures, we have developed a perioperative evaluation and auxiliary decision-making system named "Aged Patient Perioperative Longitudinal Evaluation-Multidisciplinary Trial (APPLE-MDT)".Methods: We will conduct a perioperative risk evaluation and targeted intervention, with follow-ups at 1, 3, and 6 months after surgery. The primary objective of the study is to evaluate the effectiveness of the "Aged Patient Perioperative Longitudinal Evaluation-Multiple Disciplinary Trial Path" (hereinafter referred to as the APPLE-MDT path) in surgical decision-making for aged patients (≥75 years) undergoing elective surgery under non-local anesthesia in the operating room. The secondary objectives of the study are to evaluate the postoperative outcome and health economics of the APPLE-MDT path applied to the surgical decision-making of aged patients (≥75 years) undergoing elective surgery under non-local anesthesia and to optimize intervention strategies for aged patients undergoing surgery to reduce the occurrence of postoperative complications and improve the quality of life after surgery.Discussion: It is necessary to formulate a reliable, effective, and concise evaluation tool, which can effectively predict the perioperative complications and mortality of aged patients, support targeted intervention strategies, and allow for a more comprehensive risk and benefit analysis, thereby forming an effective senile perioperative surgery management path. It is expected that the implementation of this protocol can reduce the occurrence of postoperative complications, improve the postoperative quality of life, shorten hospital stay, reduce hospitalization expenses, reduce social burden, and allow the elderly to have a good quality of life after surgery.Trial Registration: ChiCTR, ChiCTR1800020363 , Registered 15 December 2018. [ABSTRACT FROM AUTHOR]