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.

Cobalt-ion batteries are considered a promising battery chemistry for renewable energy storage. However, there are indeed challenges associated with co-ion batteries that demonstrate undesirable side reactions due to hydrogen gas production. This study demonstrates the use of a nanocomposite electrolyte that provides stable performance cycling and high Co²⁺ conductivity (approximately 24 mS cm⁻¹). The desirable properties of the nanocomposite material can be attributed to its mechanical strength, which remains at nearly 68 MPa, and its ability to form bonds with H₂O. These findings offer potential solutions to address the challenges of co-dendrite, contributing to the advancement of co-ion batteries as a promising battery chemistry. The exceptional cycling stability of the co-metal anode, even at ultra-high rates, is a significant achievement demonstrated in the study using the nanocomposite electrolyte. The co-metal anode has a 3500-cycle current density of 80 mA cm⁻², which indicates excellent stability and durability. Moreover, the cumulative capacity of 15.6 Ah cm⁻² at a current density of 40 mA cm⁻² highlights the better energy storage capability. This performance is particularly noteworthy for energy storage applications where high capacity and long cycle life are crucial. The H₂O bonding capacity of the component in the nanocomposite electrolyte plays a vital role in reducing surface passivation and hydrogen evolution reactions. By forming strong bonds with H₂O molecules, the polyethyne helps prevent unwanted reactions that can deteriorate battery performance and efficiency. This mitigates issues typically associated with excess H₂O and ion presence in aqueous Co-ion batteries. Furthermore, the high-rate performance with excellent stability and cycling stability performance (>500 cycles at 8 C) of full Co||MnO₂ batteries fabricated with this electrolyte further validates its effectiveness in practical battery configurations. These results indicate the potential of the nanocomposite electrolyte as a valuable and sustainable option, simplifying the development of reliable and efficient energy storage systems and renewable energy applications.

To save patients’ lives, it is important to go for an early diagnosis of intracranial hemorrhage (ICH). For diagnosing ICH, the widely used method is non-contrast computed tomography (NCCT). It has fast acquisition and availability in medical emergency facilities. To predict hematoma progression and mortality, it is important to estimate the volume of intracranial hemorrhage. Radiologists can manually delineate the ICH region to estimate the hematoma volume. This process takes time and undergoes inter-rater variability. In this research paper, we develop and discuss a fine segmentation model and a coarse model for intracranial hemorrhage segmentations. Basically, two different models are discussed for intracranial hemorrhage segmentation. We trained a 2DDensNet in the first model for coarse segmentation and cascaded the coarse segmentation mask output in the fine segmentation model along with input training samples. A nnUNet model is trained in the second fine stage and will use the segmentation labels of the coarse model with true labels for intracranial hemorrhage segmentation. An optimal performance for intracranial hemorrhage segmentation solution is obtained.

Google Earth images in the Marche Region of Central Italy revealed a circular structure consisting of a ring system made up of concentric hills and valleys. Cartography, DEM, geological, and available geophysical data were used to constrain the possible origin of the structure. Located in the Messinian foredeep deposits of the Central Apennines, it has a rim diameter of 3.75 km and a central uplift connected to its southernmost part. As it was formed in the clays of the Lower Pliocene, and clays are believed to have emerged definitively after the Upper Pliocene, its age might be constrained to the Lower Pleistocene. Similar concentric structures are usually found in impact craters, sedimentary domes, and volcanic landforms. As salt domes and magmatic activity are not found in this region, this study seeks to validate the results of previous work that it was the result of an ancient impact crater of hydrological, brachyanticline, or clayey diapiric origins. Specifically, an observed second ring portion with a curvature radius about double the first in size will be investigated in this work. This second ring portion appears to be concentric to the first one and is visible along its northern and western parts. Although double concentric rings are usually due to impact craters, the absence of the ring portion in the other two directions and the probable deviation of a river, deduced by studying hydrography, support the hypothesis that it might be of clay diapir origin.

The rapid progress of information technology has made public online participation in policy formulation an inevitable product of modern government reshaping and reconstruction. However, compared with developed countries, citizens’ online participation in policy formulation in China started relatively late. Thus, in order to explore an effective and efficient method for Chinese citizens’ participation in policy formulation, this research made a brief review of the experiences from the typical developed country of United States of America at first, followed by some other developed countries such as Singapore, South Korea, and Japan in Asia with similar situations. Still, combined with the current situation of the China itself into consideration, this research further proposes targeted recommendations. It is expected that the findings in this research could provide some references for the Chinese government to form more effective and efficient theoretical frameworks targeted at the future development trends of the Chinese society and accordingly, to improve the construction of democracy in China.

Fire hazard is often mapped as a static conditional probability of fire characteristics’ occurrence. We developed a dynamic product for operational risk management to forecast the probability of occurrence of fire radiative power in the locally possible near-maximum fire intensity range. We applied standard machine learning techniques to remotely sensed data. We used a block maxima approach to sample the most extreme fire radiative power (FRP) MODIS retrievals in free-burning fuels for each fire season between 2001 and 2020 and associated weather, fuel, and topography features in northwestern south America. We used the random forest algorithm for both classification and regression, implementing the backward stepwise repression procedure. We solved the classification problem predicting the probability of occurrence of near-maximum wildfire intensity with 75% recall out-of-sample in ten annual test sets running time series cross validation, and 77% recall and 85% ROC-AUC out-of-sample in a twenty-fold cross-validation to gauge a realistic expectation of model performance in production. We solved the regression problem predicting FRP with 86% r² in-sample, but out-of-sample performance was unsatisfactory. Our model predicts well fatal and near-fatal incidents reported in Peru and Colombia out-of-sample in mountainous areas and unimodal fire regimes, the signal decays in bimodal fire regimes.