This paper proposes joint sensing and beamforming solutions tailored for extremely large-scale MIMO (XL-MIMO) near-field systems under both static and dynamic scenarios. For static scenarios, we develop a novel Multi-Layer Reconstruction (MLR) mechanism to address the challenges of large-scale near-field dictionary matrix and coarse range grid spacing, and further propose a sparse sensing algorithm, MLR mechanism based Linear Approximation Variational Bayesian Inference (MLR-LA-VBI), to achieve precise user/target position and radar cross section (RCS) sensing with low pilot overhead. Building upon these sensing results, a beamforming scheme is proposed to optimize radiation patterns. For dynamic scenarios, we exploit near-field Doppler-frequency characteristics to propose the modified MLR-LA-VBI (MMLR-LA-VBI) algorithm for sensing and a predictive beamforming framework, where the former serves as the core module of the latter. Our sensing approach enables full motion status sensing of users/targets from a single echo without requiring prior information of the target motion model. By eliminating echo accumulation and leveraging correlations across consecutive coherent processing intervals (CPIs), it achieves high performance with low computational complexity. Moreover, the proposed predictive beamforming framework naturally inherits the aforementioned advantages of MMLR-LA-VBI, and leverages the sensed full motion status to achieve an efficient and seamless beam tracking scheme with Doppler frequency compensation. In addition, theoretical analysis is conducted to characterize the algorithmic complexity, highlighting the advantages of proposed algorithms in terms of efficiency. Finally, simulations and analyses validate the effectiveness of the proposed algorithms in both static and dynamic scenarios.

Extremely large-scale MIMO (XL-MIMO) is regarded as a pivotal enabler for achieving ultra-high spectral efficiency in 6G communications. Near-field channel models, which integrate both line-of-sight (LoS) and non-line-of-sight (NLoS) components, provide accurate characterizations of near-field XL-MIMO channels. However, existing channel estimation schemes encounter severe performance bottlenecks due to the high-dimensional nature of near-field XL-MIMO channels and their structured angular sparsity compared to far-field MIMO systems. To address these challenges, we propose a variance exploding stochastic differential equation (VE-SDE) generator based on an improved diffusion transformer (IDiT) network. The VE-SDE progressively maps the complex XL-MIMO channel distribution to a tractable prior distribution by gradually injecting noise. We utilize the patchify technique to decompose the perturbed angular domain channels into token sequences, which are then processed with diffusion transformer (DiT) blocks, substantially reducing floating-point operations (FLOPs). Additionally, a sparse self-attention mechanism is employed to enhance structured sparsity characterization learning, thereby improving estimation accuracy. Theoretical analysis and numerical experiments show that the VE-SDE generator exhibits strong generalizability and robustness across diverse channel distributions without requiring retraining. Simulation results reveal that the proposed method outperforms state-of-the-art estimation approaches, achieving high-fidelity channel estimation with only 20% pilot density.

The emergence of extremely large-scale MIMO (XLMIMO) has made target sensing in near-field environments crucial. However, the vast number of antennas and the big size of near-field dictionary matrix (DM) result in substantial pilot overhead for beam training algorithms and significant computational complexity for subspace algorithms. Moreover, there is few of work capable of accurately obtaining information beyond target location, such as radar cross-section (RCS). To this end, we propose a high-precision, low-pilot-overhead off-grid compressive sensing (CS) algorithm capable of jointly estimating target's location and RCS-the Multi-Layer Reconstruction Linear Approximation Variational Bayesian Inference (MLR-LA-VBI) algorithm. Specifically, the entire algorithm is divided into two phases. In the first phase, we propose the Multi-Layer Reconstruction (MLR) mechanism to reconstruct a surrogate DM. In the second phase, based on the surrogate DM, thus proposing the MLR-LA-VBI algorithm for joint estimation of target location and RCS. The final simulation results verify the superior performance of the proposed algorithm.

Protein structure prediction is a longstanding issue crucial for identifying new drug targets and providing a mechanistic understanding of protein functions. To enhance the progress in this field, a spectrum of computational methodologies has been cultivated. AlphaFold2 has exhibited exceptional precision in predicting wild-type protein structures, with performance exceeding that of other methods. However, predicting the structures of missense mutant proteins using AlphaFold2 remains challenging due to the intricate and substantial structural alterations caused by minor sequence variations in the mutant proteins. Molecular dynamics (MD) has been validated for precisely capturing changes in amino acid interactions attributed to protein mutations. Therefore, for the first time, a strategy entitled 'MoDAFold' was proposed to improve the accuracy and reliability of missense mutant protein structure prediction by combining AlphaFold2 with MD. Multiple case studies have confirmed the superior performance of MoDAFold compared to other methods, particularly AlphaFold2.

Accurate channel state information (CSI) acquisition with low pilot overhead has always been a problem for RIS-assisted massive MIMO systems. The design of phase shifts at RIS plays a key role in channel estimation (CE). In this paper, we incorporate the spatial feature of RIS into the design of a variational Bayesian inference-based CE algorithm, which is computational efficient and can exploit the sparse structure of cascaded channel. To optimally configure the phase shifts of RIS for CE, an optimal phase training algorithm and a simplified version are proposed by formulating the CE performance metric in terms of the RIS phases. The numerical simulations show that the proposed spatial feature aided optimal phase training of RIS as well as the compressive CE algorithm achieve superior CE performance with very low pilot overhead compared to the state-of-art baselines.