Extraction of water bodies from high-resolution remote sensing imagery based on a deep semantic segmentation network.

The precise delineation of urban aquatic features is of paramount importance in scrutinizing water resources, monitoring floods, and devising water management strategies. Addressing the challenge of indistinct boundaries and the erroneous classification of shadowed regions as water in high-resolution remote sensing imagery, we introduce WaterDeep, which is a novel deep learning framework inspired by the DeepLabV3 + architecture and an innovative fusion mechanism for high- and low-level features. This methodology first creates a comprehensive dataset of high-resolution remote sensing images, then progresses through the Xception baseline network for low-level feature extraction, and harnesses densely connected Atrous Spatial Pyramid Pooling (ASPP) modules to assimilate multi-scale data into sophisticated high-level features. Subsequently, the network decoder amalgamates the elemental and intricate features and applies dual-line interpolation to the amalgamated dataset to extract aqueous formations from the remote images. Experimental evidence substantiates that WaterDeep outperforms its existing deep learning counterparts, achieving a stellar overall accuracy of 99.284%, FWIoU of 95.58%, precision of 97.562%, recall of 95.486%, and F1 score of 96.513%. It also excels in the precise demarcation of edges and the discernment of shadows cast by urban infrastructure. The superior efficacy of the proposed method in differentiating water bodies in complex urban environments has significant practical applications in real-world contexts.

A meta-analysis of the risk factors for neurosurgical surgical site infection following craniotomy.

The purpose of the meta-analysis was to evaluate and compare the risk factors for neurosurgical surgical site infection (SSI) after craniotomy. Using dichotomous or contentious random or fixed effect models, the odds ratio (OR) and mean difference (MD) with 95% confidence intervals (CIs) were computed based on the examination of the meta-analysis results. Eighteen analyses, covering 11 068 craniotomies between 2001 and 2023, were included in the current meta-analysis. Subjects with SSIs had a significantly younger age (MD, -2.49; 95% CI, -2.95 to -2.04, p < 0.001), longer operation duration (MD, 10.21; 95% CI, 6.49-13.94, p < 0.001) and longer length of postoperative hospital stay (MD, 1.52; 95% CI, 0.45-2.60, p = 0.006) compared to subjects with no SSI with craniotomy. However, no significant difference was found between craniotomy subjects with SSIs and with no SSI in gender (OR, 0.90; 95% CI, 0.76-1.07, p = 0.23), and combination with other infection (OR, 3.93; 95% CI, 0.28-56.01, p = 0.31). The data that were looked at showed that younger age, longer operation duration and longer length of postoperative hospital stay can be considered as risk factors of SSI in subjects with craniotomy; however, gender and combination with other infections are not. Nonetheless, consideration should be given to their values because several studies only involved a small number of patients, and there are not many studies available for some comparisons.

Compact plasmon modulator based on the spatial control of carrier density in indium tin oxide.

To keep pace with the demands of semiconductor integration technology, a semiconductor device should offer a small footprint. Here, we demonstrate a compact electro-optic modulator by controlling the spatial distribution of carrier density in indium tin oxide (ITO). The proposed structure is mainly composed of a symmetrical metal electrode layer, calcium fluoride dielectric layer, and an ITO propagating layer. The carrier density on the surface of the ITO exhibits a periodical distribution when the voltage is applied on the electrode, which greatly enhances the interaction between the surface plasmon polaritons (SPPs) and the ITO. This structure can not only effectively improve the modulation depth of the modulator, but also can further reduce the device size. The numerical results indicate that when the length, width, and height of the device are 14 µm, 5 µm, and 8 µm, respectively, the modulation depth can reach 37.1 dB at a wavelength of 3.66 µm. The structure can realize a broadband modulation in theory only if we select a different period of the electrode corresponding to the propagating wavelength of SPPs because the modulator is based on the scattering effect principle. This structure could potentially have high applicability for optoelectronic integration, optical communications, and optical sensors in the future.

A multi-parameter tunable plasmon modulator.

Multi-parameter control of light is a key functionality to modulate optical signals in photonic integrated circuits for various applications. However, the traditional optical modulators can only control one or two properties of light at the same time. Herein, we propose a hybrid structure which can modulate the amplitude, wavelength and phase of surface plasmon polaritons (SPPs) simultaneously to overcome these limitations. The numerical results show that when the Fermi level of graphene changes from 0.3 to 0.9 eV, the variation of optical transmission, wavelength and phase are 32.7 dB, 428 nm and 306°, respectively. The demonstrated structure triggers an approach for the realization of ultracompact modulation and has potential applications in the fields of optical switches, communications and photo-detection.

[CT and MRI fusion based on generative adversarial network and convolutional neural networks under image enhancement].

Aiming at the problems of missing important features, inconspicuous details and unclear textures in the fusion of multimodal medical images, this paper proposes a method of computed tomography (CT) image and magnetic resonance imaging (MRI) image fusion using generative adversarial network (GAN) and convolutional neural network (CNN) under image enhancement. The generator aimed at high-frequency feature images and used double discriminators to target the fusion images after inverse transform; Then high-frequency feature images were fused by trained GAN model, and low-frequency feature images were fused by CNN pre-training model based on transfer learning. Experimental results showed that, compared with the current advanced fusion algorithm, the proposed method had more abundant texture details and clearer contour edge information in subjective representation. In the evaluation of objective indicators, Q AB/F, information entropy (IE), spatial frequency (SF), structural similarity (SSIM), mutual information (MI) and visual information fidelity for fusion (VIFF) were 2.0%, 6.3%, 7.0%, 5.5%, 9.0% and 3.3% higher than the best test results, respectively. The fused image can be effectively applied to medical diagnosis to further improve the diagnostic efficiency.

Comprehensive evaluation for the sustainable development of fresh agricultural products logistics enterprises based on combination empowerment-TOPSIS method.

To solve the problems of environmental pollution and resource waste caused by the rapid development of cold chain logistics of fresh agricultural products and improve the competitiveness of logistics enterprises in the market, a performance evaluation method of cold chain logistics enterprises based on the combined empowerment-TOPSIS was proposed. Firstly, from the five dimensions of cold supply chain capacity, service quality, economic efficiency, informatization degree and development ability, a comprehensive evaluation system of logistics enterprises' sustainable development is constructed, which consists of 16 indicators, such as storage and preservation capacity, distribution accuracy, and equipment input rate. Then, G1 method and entropy weight method are used to calculate the subjective and objective weights of the evaluation indicators, and the combined weights are calculated with the objective of minimizing the deviation of the subjective and objective weighted attributes. Finally, the TOPSIS method is used to calculate the comprehensive evaluation indicators. The results show that the established performance evaluation model can effectively evaluate the performance of fresh agricultural products logistics enterprises and provide theoretical basis for enterprise logistics management.

A Smoothed Matrix Multivariate Elliptical Distribution-Based Projection Method for Feature Extraction.

Big data has the traits such as "the curse of dimensionality," high storage cost, and heavy computation burden. Self-representation-based feature extraction methods cannot effectively deal with the image-level structural noise in the data, so how to character a better relationship of reconstruction representation is very important. Recently, sparse representation with smoothed matrix multivariate elliptical distribution (SMED) using structural information to handle low-rank error images caused by illumination or occlusion has been proposed. Based on SMED, we present a new method named SMEDP for feature extraction. SMEDP firstly utilizes SMED to automatically construct an adjacency graph and then obtains an optimal projection matrix by maximizing the ratio of the local scatter matrix and the total scatter matrix in the PCA subspace. Experiments on the COIL-20 object database, ORL face database, and CMU PIE face database prove that SMEDP works well and can achieve considerable visual and recognition performance than the relevant methods.