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Breast cancer is the most prevalent malignant neoplasm worldwide, necessitating the development of novel therapeutic strategies owing to the limitations posed by conventional treatment modalities. Immunotherapy is an innovative approach that has demonstrated significant efficacy in modulating a patient's innate immune system to combat tumor cells. In the era of precision medicine, adoptive immunotherapy for breast cancer has garnered widespread attention as an emerging treatment strategy, primarily encompassing cellular therapies such as tumor-infiltrating lymphocyte therapy, chimeric antigen receptor T/natural killer/M cell therapy, T cell receptor gene–engineered T cell therapy, lymphokine-activated killer cell therapy, cytokine-induced killer cell therapy, natural killer cell therapy, and γδ T cell therapy, among others. This treatment paradigm is based on the principles of immune memory and antigen specificity, involving the collection, processing, and expansion of the patient's immune cells, followed by their reintroduction into the patient's body to activate the immune system and prevent tumor recurrence and metastasis. Currently, multiple clinical trials are assessing the feasibility, effectiveness, and safety of adoptive immunotherapy in breast cancer. However, this therapeutic approach faces challenges associated with tumor heterogeneity, immune evasion, and treatment safety. This review comprehensively summarizes the latest advancements in adoptive immunotherapy for breast cancer and discusses future research directions and prospects, offering valuable guidance and insights into breast cancer immunotherapy. [ABSTRACT FROM AUTHOR]

To better suppress low-frequency vibrations of flexible manipulators induced by the rotation of motors and eccentricity, a novel type of tuned particle damper (TPD) is designed by combining the advantages of classical dynamic vibration absorber (DVA) and particle dampers (PD). Compared to traditional DVAs, this TPD can reduce additional mass and effectively broaden the frequency band of the DVA. Firstly, an equivalent theoretical model is established to describe the frequency tuning principle of the designed TPD. Based on the theory of a single particle damper, the equivalent damping and stiffness of the particles are calculated through an approximate approach. Then, a three-degree-of-freedom vibration model of the manipulator system with the TPD is built, and the dynamical characteristic of the primary resonance for the coupled system are analyzed by the perturbation method. Finally, the experimental platform is set up to verify the theoretical results. A manipulator is applied to test the low-frequency vibration absorption of the designed TPD, and the vibration suppression effect is discussed both in theoretical analysis and experiments. [ABSTRACT FROM AUTHOR]

For the channel estimation problem under α-stable distributed impulse interference, the traditional fixed-step adaptive filtering cannot satisfy the fast convergence speed and low steady-state error at the same time, whereas the variable-step method is able to effectively solve this contradiction. This paper proposes an improved variable step-size least mean p-power adaptive algorithm that offers good robustness against impulsive noise. The proposed algorithm takes into account the linkage between the errors and uses the adjustment of the step size based on the errors of the current moment and the previous k moments, thus overcoming the problems of poor anti-noise performance and large steady-state fluctuations of the fixed-step size algorithm. This algorithm ensures that the step size does not change abruptly when the system is disturbed by impulse noise and can achieve a lower steady-state error. The simulation results show that the algorithm has better convergence than the traditional fixed step-size algorithm and the existing variable step-size algorithm under the interference of impulsive noise. [ABSTRACT FROM AUTHOR]

In recent years, research on modulated signal recognition using deep learning (DL) has achieved remarkable success, allowing automatic modulation recognition (AMR) to play a crucial role in modern communication systems. The emergence of the attention mechanism has then rapidly led to a wide range of applications in image classification and speech recognition, which proves the effectiveness of the attention mechanism. In this paper, the authors propose a network model feed‐forward Attention mechanism with Residuals networks and Long Short‐Term Memory (RLADNN) based on feed‐forward attention mechanism with Residuals networks (Resnet) and Long Short‐Term Memory (LSTM), which takes the advantage that the Attention mechanism can effectively solve some long‐term memory problems in [−20:18] signal‐to‐noise ratio (SNR) for the recognition of 11 modulated signals with different SNR, and effectively improves the recognition rate. [ABSTRACT FROM AUTHOR]