Real-world adoption of closed-loop insulin delivery systems (CLIDS) in type 1 diabetes remains low, driven not by technical failure, but by diverse behavioral, psychosocial, and social barriers. We introduce ChatCLIDS, the first benchmark to rigorously evaluate LLM–driven persuasive dialogue for health behavior change. Our framework features a library of expert-validated virtual patients, each with clinically grounded, heterogeneous profiles and realistic adoption barriers, and simulates multi-turn interactions with nurse agents equipped with a diverse set of evidence-based persuasive strategies. ChatCLIDS uniquely supports longitudinal counseling and adversarial social influence scenarios, enabling robust, multi-dimensional evaluation. Our findings reveal that while larger and more reflective LLMs adapt strategies over time, all models struggle to overcome resistance, especially under realistic social pressure. These results highlight critical limitations of current LLMs for behavior change, and offer a high-fidelity, scalable testbed for advancing trustworthy persuasive AI in healthcare and beyond.

Real-world noise often exhibits complex non-Gaussian characteristics, frequently manifesting as heavy-tailed distributions contaminated by outliers. The effectiveness of filtering algorithms can vary significantly across these diverse noise types. To this end, this letter presents an adaptive filtering algorithm based on the Student's-t mixture model (SMM). The proposed approach systematically employs SMM to model the noise distribution, optimizes the mixture model's hyper-parameters via the Expectation-Maximization (EM) algorithm, and ultimately derives the filter by maximizing the log-likelihood function of the parameterized SMM. Leveraging the inherent heavy-tailed properties of SMM, the algorithm demonstrates strong robustness against extreme outliers and is suitable for a wide range of noise environments. Simulation results confirm its superior performance under various noise disturbances.

In this paper, we investigate a reconfigurable intelligent surface (RIS) aided spectrum sharing scheme between a multiple input multiple-output (MIMO) radar and MIMO multiuser communication system under non-homogeneous interference scenarios, including the interference from scattering points, the mutual interference between the two systems, and the interference among multiple users. We consider a flexibly-weighted framework for joint resource allocation, aiming at maximizing the mutual information of both the radar and the communication systems under the usual constraints on the transmit power and on the compatibility of the space spectral. To deal with the resulting triple degrees of freedom non-convex framework, a sub-optimal procedure, based on iterative alternating maximization of three suitably derived subproblems, is proposed and analyzed. Each yields a closed-form solution. In particular, to address the constant modulus constraint imposed by the RIS, we propose two different optimization strategies, based on the Minorization-Maximization framework in conjunction with the Alternating Direction Penalty Method and the Element Block Coordinate Descent formulations, namely, MM-ADPM and MM-EBCD, respectively. Finally, simulation results compare the effectiveness and advantages of the two algorithms.

This paper considers a passive intelligent reflecting surface (IRS)-assisted integrated radar and communication system for multi-target detection and multi-user communications. To balance the communication and sensing performance, we propose an alternating optimization algorithm to optimize the worst-case weighted sum of the radar waveform minimum mean square error (MSE) and Multiuser interference (MUI) in Communication, under the spectrum compatibility and power constraints. The proposed algorithm utilizes a novel Tchebycheff optimization framework that decomposes the multi-objective optimization problem into three subproblems by optimizing the radar transmitted sequences, communication transmitted sequences, and IRS phase configuration. We propose an alternating optimization algorithm which incorporates alternating direction penalty method (ADPM) and element-wise block coordinate descent (E-BCD) frameworks to efficiently solve the optimization problem. Extensive numerical simulations validate the effectiveness of the proposed method, demonstrating significant performance improvements in both minimizing radar MSE and communication MUI and better convergence speed.

Semantic communication (SC), by compressing raw data at the semantic level, significantly improves the information entropy of transmitted data and is considered as one of the key enabling technologies for the next-generation communication. However, most current research underestimates the impact of channel interference on SC systems. As an innovative generative artificial intelligence technique, the diffusion model (DM) has demonstrated remarkable performance in image denoising and enhancement. In this paper, we focus on the effects of wireless channels on SC image transmission and propose an unmanned aerial vehicle (UAV)-enhanced SC framework, termed diffusion joint source-channel coding (D-JSCC). Initially, we deploy a ground-to-air SC system on UAVs, utilizing the aerial advantage to provide favorable channels. Subsequently, we employ DM for intelligent signal processing, adaptively denoising channel interferences and optimizing received images with respect to numerical errors and perceptual loss. The results show that DJSCC consistently exhibits superior performance across various metrics over different channel conditions.

Autonomous aerial vehicles (AAVs), as the key Internet of Things (IoT) devices, play a dominant position in low-altitude environments. Semantic communication (SC), as the next-generation communication technology, serves as a bridge for surpassing the Shannon limit toward the 6G wireless network. Establishing air-ground SC to provide intelligent IoT services is a crucial initiative for building future smart cities. In this article, we propose an AAV-based SC framework, named diffusion joint source-channel coding (D-JSCC). Abandoning traditional convolutional neural networks, we use transformers as the backbone and innovatively incorporate the diffusion model (DM) for image enhancement, achieving an optimal balance between image distortion and human perception. To accurately capture the numerical and perceptual loss induced by wireless channels and seamlessly amalgamate the DM with SC, we integrate channel states as strong prior information to refine the sampling process. Furthermore, we employ the gradient guidance strategy, which counteracts the randomness of sampling ensuring high robustness in harsh communication conditions. Additionally, we strike a balance between performance and sampling steps, ensuring both efficient computation and high-quality image enhancement. Comprehensive experiments demonstrate the advantages of D-JSCC across different communication environments.

Both the minimum error entropy (MEE) and mixture MEE (MMEE) are extensively employed in distributed adaptive filters, exhibiting their robustness against non-Gaussian noise by capturing high-order statistical information from network data. However, the fixed shape of the Gaussian kernel function existing in MEE and MMEE restricts their flexibility, leading to reduced robustness and deteriorated performance. To address this issue, a novel diffusion generalized minimum total error entropy (DGMTE) algorithm is first proposed in this letter, using a generalized MEE criterion to significantly improve the performance of error-in-variables models-based algorithms under non-Gaussian noise. Moreover, as a special case of DGMTE, a generalized minimum total error entropy (GMTE) algorithm is also proposed, and the local convergence analysis of DGMTE is given. Finally, simulations show the superiorities of DGMTE in comparison with other representative algorithms.

Kernel adaptive filters (KAFs) are a class of nonlinear adaptive filters developed in the reproducing kernel Hilbert space, and are particularly suitable for addressing signal processing issues involving data streams and unknown nonlinearities. However, KAFs endure the issue of network structure growth as the number of training samples increases. To this end, a novel structural sparsification method for KAFs, i.e., Gauss Hermite Fourier features (GHFF) method, is first proposed by combining Gauss Hermite quadrature integration rule and Fourier transform. Subsequently, the GHFF method is integrated with the maximum correntropy criterion in filter design, leading to the development of two new adaptive filtering algorithms, i.e., GHFF maximum correntropy (GHFFMC) algorithm and stochastic batch GHFFMC (SB-GHFFMC) algorithm. The proposed GHFFMC and SB-GHFFMC algorithms are expected to exhibit excellent capabilities in characterizing the unknown nonlinear relationships within the data, along with robustness to outliers. Meanwhile, SB-GHFFMC is anticipated to exhibit superior filtering performance in comparison with GHFFMC, as it leverages a general and flexible batch gradient descent method for model optimization. Simulations on nonlinear system identification and time-series prediction of Chua’s circuits confirm the performance superiorities of the proposed algorithms compared to other robust KAFs and RFF-based filters.

This paper investigates the RadCom system with multiple input multiple output (MIMO) structure under mutual spectrally crowded environment, where the radar system acts as the primary function, while the communication system is the secondary function. An alternative formulation aims at maximizing signal-to-interference-plus noise ratio (SINR) with multiple constraints to control the peak-to-average power ratio (PAR), spectrally compatibility and transmit power. The design degrees of freedom are the space-time waveform for radar and communication system. Then we develop an optimal scheme that tackles the original non-convex formulation into a series of sub-problems with alternating iteration, Taylor expansion, and the double alternating direction method of multipliers (DADMM) design. Numerical examples are performed to assess the merits of the derived solutions.

The filtered-x least mean square (FxLMS) algorithm has been proposed for an active noise control (ANC) system. However, due to the used mean square error (MSE) criterion, FxLMS suffers from performance degeneration for non-Gaussian noises, dramatically. To address this issue, a novel robust generalized hyperbolic tangent (GHT) criterion is first constructed in this paper. Then, the random Fourier features (RFF) method and the conjugate gradient (CG) method are used to address the nonlinearity existing in ANC and solve the quadratic optimization problem induced by the GHT criterion, respectively. Finally, a novel robust random Fourier conjugate gradient filtered-x generalized hyperbolic tangent (RFCGFxGHT) algorithm is proposed for ANC. The theoretical analyses regarding the convergence and computational complexity of RFCGFxGHT are also derived. Simulation experiments on nonlinear ANC systems corrupted by the synthetic logistic chaotic and$\alpha$-stable noises, as well as real-world functional magnetic resonance imaging (fMRI) and server room noises, are conducted to confirm the effectiveness, robustness, and desirable nonlinear learning ability of the proposed algorithm.

This article presents a novel sensor array optimization scheme for multisensor electronic nose detection system. A system architecture with multisensor is first proposed to implement the medical detection, including the bacterial culture medium detection and animal wound infection detection. The system efficiency is evaluated by comparing with the field asymmetric ion mobility spectrometry (FAIMS) system. To further improve the detection effect and reduce the number of sensors of the electronic nose system, we then derive two sensor array optimization procedures based on factor analysis and Hilbert–Schmidt independence criterion, respectively. Specifically, the weighted factor analysis method and nonweighted factor analysis method are proposed via factor analysis. Besides, the Hilbert–Schmidt independence criterion optimization design of linear kernel function and Gaussian kernel function are also exploited. The experimental results highlight that compared with the existing approaches, the proposed weighted factor analysis optimization method and Hilbert–Schmidt independence criterion optimization method (Gaussian kernel function) can achieve a significant system performance.

In this paper, a new robust Kalman filtering framework for a linear system with non-Gaussian heavy-tailed and/or skewed state and measurement noises is proposed through modeling one-step prediction and likelihood probability density functions as Gaussian scale mixture (GSM) distributions. The state vector, mixing parameters, scale matrices, and shape parameters are simultaneously inferred utilizing standard variational Bayesian approach. As the implementations of the proposed method, several solutions corresponding to some special GSM distributions are derived. The proposed robust Kalman filters are tested in a manoeuvring target tracking example. Simulation results show that the proposed robust Kalman filters have a better estimation accuracy and smaller biases compared to the existing state-of-the-art Kalman filters.