For massive multiple-input multiple-output (MIMO) communications, the minimum mean square error (MMSE) scheme provides close to optimal recognition but involves inverting the high-dimensional matrix. A Gauss-Seidel (GS) detector based on conjugate gradient and Jacobi iteration (CJ) joint processing is introduced to address this problem. Firstly, the signal is initialized with the best of the three initialization regimes for faster algorithm convergence. Secondly, the signal is processed together with CJ. Finally, the pre-processed result is transferred to the GS detector. The simulation results indicate that the proposed iterative algorithm has a lower BER performance than the GS and improved iterative scheme based on GS in channels with different correlation levels.

Estimating Visual Quality of Street Space (VQoSS) is pivotal for urban design, environmental sustainability, civic engagement, etc. Recent advancements, notably in deep learning, have enabled large-scale analysis. However, traditional deep learning approaches are hampered by extensive data annotation requirements and limited adaptability across diverse VQoSS tasks. Multimodal Large Language Models (MLLMs) have recently demonstrated proficiency in various computer vision tasks, positioning them as promising tools for automated VQoSS assessment. In this paper, we pioneer the application of MLLMs to VQoSS change estimation, with our empirical findings affirming their effectiveness. In addition, we introduce Street Quality GPT (SQ-GPT), a model that distills knowledge from the current most powerful but inaccessible (not free) GPT-4, requiring no human efforts. SQ-GPT approaches GPT-4’s performance and is viable for large-scale VQoSS change estimation. In a case study of Nanjin, we showcase the practicality of SQ-GPT and knowledge distillation pipeline. Our work promises to be a valuable asset for future urban studies research.

Porous MnO/C microspheres have been successfully fabricated by a fast co-precipitation method in a T-shaped microchannel reactor. The structures, compositions and electrochemical performances of the obtained MnO/C microspheres are characterized by X-ray diffraction, emission scanning electron microscopy, transmission electron microscopy (HRTEM), Brunauer–Emmett–Teller analysis, charge-discharge testing, cyclic voltammograms, and electrochemical impedance spectra. Experimental results reveal that the as-prepared MnO/C, with a specific surface area of 96.66 m²·g^–1 and average pore size of 24.37 nm, exhibits excellent electrochemical performance, with a discharge capacity of 655.4 mAh·g^–1 after cycling 50 times at 1 C and capacities of 808.3, 743.7, 642.6, 450.1, and 803.1 mAh·g^–1 at 0.2, 0.5, 1, 2, and 0.2 C, respectively. Moreover, the controlled method of using a micro-channel reactor, which can produce larger specific surface area porous MnO/C with improved cycling performance by shortening lithium-ion diffusion distances, can be easily applied in real production on a large-scale.