With the wide application of the Internet and smart systems, data centers (DCs) have become a hot spot of global concern. The energy saving for data centers is at the core of the related works. The thermal performance of a data center directly affects its total energy consumption, as cooling consumption accounts for nearly 50% of total energy consumption. Superior power distribution is a reliable method to improve the thermal performance of DCs. Therefore, analyzing the effects of different power distribution on thermal performance is a challenge for DCs. This paper analyzes the thermal performance numerically and experimentally in DCs with different power distribution. First, it uses Fluent simulate the temperature distribution and flow field distribution in the room, taking the cloud computing room as the research object. Then, it summarizes a formula based on the computing power distribution in a certain range by the numerical and experimental analysis. Finally, it calculates an optimal cooling power by analyzing the cooling power distribution. The results shows that it reduces the maximum temperature difference between the highest temperature of the cabinet from 5-7k to within 1.2k. In addition, the cooling energy consumption is reduced by more than 5%.

Under the developing trend of artificial intelligence (AI) technology gradually penetrating all aspects of society, the traditional language education industry is also greatly affected ^[1]. AI technology has had a positive impact on college English teaching, but it also presents challenges and negative impacts. On the positive side, AI technology can provide personalized learning experiences, real-time feedback, and autonomous learning opportunities, and so on. However, it may also lead to a lack of communication between students and humans, resulting in a decline in students’ interpersonal skills, and cause students’ dependence on online learning resources as well as possible risks to student data privacy and security, and other negative impacts. To address these challenges, teachers can adopt the following countermeasures: improving teachers’ skills in the use of AI technology incorporated in the classroom, offering personalized instruction to reduce students’ dependence on AI technologies, emphasizing the cultivation of students’ humanistic literacy and interpersonal communication ability. Additionally, colleges and technology providers should strengthen data security and privacy protection to ensure the safety and confidentiality of student data. By implementing comprehensive measures, we can maximize the advantages of AI technology in college English teaching while overcoming potential issues and challenges.

As the most basic cultural venue and an important public space in rural areas, the rural museum is not only an important carrier to realize the national strategy of rural revitalization, but also a key link to open up the "last kilometer" of spiritual prosperity in the common prosperity between urban and rural areas. As one of the three pilot provinces for the construction of rural museums under the State Administration of Cultural Heritage, Zhejiang Province has listed rural museums as one of the top ten practical matters for people's livelihood, and took the lead in issuing the Zhejiang Provincial Rural Museum Construction Guide (Trial) at the national level. The construction of rural museums in Zhejiang province has entered a new stage. The paper extracts and summarizes the common problems, successful experiences and typical cases existing in the actual development process of Zhejiang rural museums, and constructs the "Zhejiang paradigm" for the sustainable development of rural museums with promotion value and demonstration significance, providing Zhejiang experience for the sustainable development of national rural museums.

Oil spills (OS) in waters can have major consequences for the ecosystem and adjacent natural resources. Therefore, recognizing the OS spread pattern is crucial for supporting decision-making in disaster management. On 31 March 2018, an OS occurred in Balikpapan Bay, Indonesia, due to a ship's anchor rupturing a seafloor crude oil petroleum pipe. The purpose of this study is to investigate the propagation of crude OS using coupled three-dimensional (3D) model from DHI MIKE software and remote sensing data from Sentinel-1 SAR (Synthetic Aperture Radar). MIKE3 FM predicts and simulates the 3D sea circulation, while MIKE OS models the path of oil's fate concentration. The OS model could identify the temporal and spatial distribution of OS concentration in subsurface layers. To validate the model, in situ observations were made of oil stranded on the shore. On 1 April 2018, at 21:50 UTC, Sentinel-1 SAR detected an OS on the sea surface covering 203.40 km². The OS model measures 137.52 km². Both methods resulted in a synergistic OS exposure of 314.23 km². Wind dominantly influenced the OS propagation on the sea surface, as detected by the SAR image, while tidal currents primarily affected the oil movement within the subsurface simulated by the OS model. Thus, the two approaches underscored the importance of synergizing the DHI MIKE model with remote sensing data to comprehensively understand OS distribution in semi-enclosed waters like Balikpapan Bay detected by SAR.

The Ecuadorian electricity sector encompasses generation, transmission, distribution and sales. Since the change of the Constitution in Ecuador in 2008, the sector has opted to employ a centralized model. The present research aims to measure the efficiency level of the Ecuadorian electricity sector during the period 2012–2021, using a DEA-NETWORK methodology, which allows examining and integrating each of the phases defined above through intermediate inputs, which are inputs in subsequent phases and outputs of some other phases. These intermediate inputs are essential for analyzing efficiency from a global view of the system. For research purposes, the Ecuadorian electricity sector was divided into 9 planning zones. The results revealed that the efficiency of zones 6 and 8 had the greatest impact on the overall efficiency of the Ecuadorian electricity sector during the period 2012–2015. On the other hand, the distribution phase is the most efficient with an index of 0.9605, followed by sales with an index of 0.6251. It is also concluded that the most inefficient phases are generation and transmission, thus verifying the problems caused by the use of a centralized model.