Accurate segmentation of brain tumor regions from multi-modal magnetic resonance imaging (MRI) is critical for clinical diagnosis. However, missing modalities is a common issue in clinical practice, where the unavailability of certain imaging modalities complicates the extraction and integration of complementary information across multiple modalities, leading to a decline in segmentation accuracy. Many existing models fail to adequately address subtle structural changes and boundary information within tumor regions when faced with missing modalities, limiting their ability to effectively adapt to complex tumor morphologies. To tackle these issues, The Multi-Scale Adaptively-Aware and Recalibration Network (MARNet) proposed in this paper can adaptively and fully explore the potential of multi-modal data under different combinations in the presence of missing modalities. MARNet incorporates a Feature Recalibration and Enhancement Module (FREM) that recalibrates and enhances the three-dimensional feature representation, emphasizing important fine-grained features of brain tumors. Subsequently, the Adaptive Shape-Aware Fusion Module (ASFM) fully exploits available modality information, achieving adaptive feature fusion for varying tumor locations and shapes, thereby compensating for information loss due to missing modalities. Furthermore, the Global and Multiscale Feature Integration Module (GMFIM) is designed to effectively capture long-range dependencies of tumors, particularly under conditions of missing modalities, aiding in the restoration and reconstruction of complete tumor structures. Extensive experiments on the BraTS2020, BraTS2018 and BraTS2015 datasets demonstrate that the proposed method surpasses several advanced brain tumor segmentation approaches in the context of missing modalities.

Neurological dysfunction encompasses a variety of diseases resulting from neural damage. Accurate assessment of neurological function is critical for diagnosis and the development of effective treatment plans. A significant number of patients with neurological disorders exhibit ocular abnormalities. Analyzing ocular status through eye movement capture plays a pivotal role in understanding various neurological dysfunctions. However, current methods of analyzing ocular status for neurological function assessments lack precision and objectivity, often relying heavily on physicians’ subjective judgment. This work proposes the Ocular-enhanced Face Keypoints Network (OFKNet), a facial keypoint detection model based on deep convolutional neural networks. OFKNet employs ConvNeXt as its backbone network and introduces a multi-scale input enhancement strategy. Additionally, a region enhancement module based on MobileNetV3 is designed to optimize features in the canthus area. Multiscale feature fusion and channel weighting are achieved through an improved Path Aggregation Network and Squeeze-and-Excitation modules. To validate OFKNet’s accuracy, we compared it with state-of-the-art models, including MediaPipe FaceLandmarker, InsightFace, Dlib68, and Dlib81, using a patient dataset we collected. Experimental results demonstrate that OFKNet outperforms existing models, particularly in calibration accuracy around the eyes. By monitoring eye movements in real-time, OFKNet ensures high-precision extraction of key points in each frame, accurately reflecting changes in patients’ ocular movements.

The automatic intelligent identification of open-pit mines could improve the efficiency in the supervision of mines. In this paper, a massive sample database of open-pit mines was firstly established with remote sensing resolution of 0.5 m, 2 m from 2018 to 2021. Then, an intelligent identification algorithm for damaged land of open-pit mines was constructed with space pyramid, cavity convolution and other structures considered in the design part. Taking Zhongliang mountain of Chongqing as a study area, the manual visual interpretation results were applied to validate the cavity convolution model. The result showed that total precision of algorithm was more than 85% which improved the identification and extraction ability of open-pit mines. The precision with remote sensing image resolution of 0.5m were higher than that of 2m. The efficiency was 18 times compared with manual visual interpretation.

The study on the influence of urban building forms on land surface temperature (LST) can provide a scientific reference for building distribution optimization in mountain cities. This paper obtained the urban building properties such as building types, building floors from field investigation based on high-resolution remote sensing images. The LST was calculated by the Radiative Transfer Equation method based on Landsat-8 data in 2021. We constructed five factors of urban building forms (average building density, average building height, floor area ratio, average building volume, and sky view factor) to analyze the correlation between urban building forms and LST. The result showed, only average building density had a certain correlation with LST, while other factors had a small correlation which indicated some factors of urban building forms may not fully reflect the characteristics of LST in mountain cities. By constructing multiple linear regression models between five factors of urban building forms and LST, we found that when the number of factors increased, the more accurate the models were, with higher the computational complexity. However, the increase of model accuracy gradually slowed down when the number of factors exceeded 3. It was appropriate to choose linear regression models of 2-factors or 3-factors to simulate LST.

The monitoring of vegetation recovery was an important factor to evaluate the effectiveness of ecological restoration of open-pit mines. Lidar data can obtain the horizontal and vertical structures of trees that was wildly used to characterize the 3-dimensional (3D) structure of trees. In this paper, taking Ba’nan District of Chongqing as a study area, high density lidar data was applied to obtain the vegetation vertical structure parameters based on marker-controlled watershed segmentation method with IPTD filtering algorithm. The result showed, average tree height, diameter at breast height (DBH), crown diameter of open-pit mines was relatively lower compared with other areas outside as the trees of open-pit mines were mainly artificial planting and the growth time of trees was about 3-5 years. The trees had recovered well in the process of restoration. It indicated that the ecological restoration and planting of trees in open-pit mines were still in the process of recovery.

In recent years, researchers have increasingly directed their attention towards modulating light fields through the unique properties of two-dimensional materials and the free designability of meta-structures. Graphene, transition metal sulfides, transition metal nitrides, and other two-dimensional materials have emerged as star materials in recent years due to their extraordinary properties that are vastly different from those of traditional three-dimensional materials. As a result, these materials hold immense potential for further exploration and research. Taking advantage of the free designability of meta-structures can be an effective means of unlocking the full potential of 2D materials. Accordingly, this review presents an overview of recent research progress in the area of light field modulation achieved by combining 2D materials with meta-structures. The review initially covers the properties of 2D materials, followed by the concepts, principles, design, and preparation of meta-structures. Then the review delves into the concrete examples of the impact and effect of the combination on light field modulation. Lastly, the review concludes with a comprehensive summary and analysis of the current challenges and potential future developments of combining 2D materials with meta-structures.

Constructing a scientific evaluation system of geological environment and restoration can provide a reliable theoretical basis for mine geological environment protection. In this paper, we firstly constructed the evaluation system of 10 indicators and 30 sub-indicators as the index of ecological environment and restoration. The hierarchical analysis method was used to evaluate each mine according to the actual situation in Chongqing. We took Mengte mine as a study area and obtained the parameters of soil, water, vegetation, and biology by field experiment. Analytic hierarchy process was applied to this paper and the weight of each indicator was calculated based on expert scoring method and the experience method. This research provided technical support for geological environmental assessment in other mines.

At present, there are two methods to calculate the microwave extinction cross-section, one is based on the energy conservation which could be calculated from backscattering cross-section, another is based on forward scattering theory in accordance with the imaginary part of the scattering amplitude in the forward direction. In this study, the extinction cross-section of two methods are employed to obtain single scattering albedo and optical depth. The 0th order Radiative Transfer Model which considers the vegetation layer as a uniform medium is used to calculate the brightness temperature of the vegetation covered ground. The simulating results are validated by observation data based on the experiments of typical vegetation (cotton and soybean). The result shows, the simulations of both methods matches the field measurements well, but the forward scattering theory seems more accurate. However, when the incident angle becomes bigger, the results of two methods tend to be very different, the energy conservation method is lower than the observation while the forward scattering method is higher.