In this article, we introduce a distributed optimization framework for directed graph networks that addresses composite objective functions with smooth local components and a shared convex regularizer. Our method employs a novel primal-dual algorithm that incorporates time-varying adaptive coefficient weights and operates efficiently under the constraints of directed network topologies. The proposed approach achieves provable convergence guarantees under strong convexity conditions. Unlike existing methods, our approach guarantees linear convergence under strong convexity assumptions and introduces new step-size bounds, improving both stability and performance. The adaptive weights dynamically optimize information sharing among agents, significantly reducing communication costs and enhancing robustness. We theoretically establish the convergence properties and demonstrate the superior performance of the proposed algorithm through simulations, showcasing faster convergence and improved accuracy compared to the methods in the literature. These results highlight the potential of the proposed method for scalable and efficient distributed optimization in directed networks.

Electromagnetic stimulated magnetic nanoparticles (MNPs) have emerged as a promising multifunctional platform for disease treatment, particularly in oncology. While magnetic hyperthermia and nanoparticle-mediated drug delivery have been extensively studied, they are often explored as separate modalities, overlooking the substantial synergistic effects achievable when combined, especially under electromagnetic stimulation. Recent studies reveal that integrating magnetic hyperthermia with electromagnetic stimulated drug delivery enhances spatial and temporal therapeutic precision and significantly increases cancer cell apoptosis, outperforming either approach alone. Despite growing evidence supporting this dual-function strategy, the literature lacks a comprehensive evaluation of the underlying mechanisms, therapeutic outcomes, and translational challenges associated with electrostimulated MNPs. This review addresses that gap by critically examining the dual role of electrostimulated MNPs in delivering localized heat and controlled drug release. We further explore innovative strategies to overcome clinical limitations such as tissue penetration and targeting depth. This review also summarizes recent synthetic methods and functionalization strategies for magnetic nanoparticles, evaluates their effectiveness in electromagnetic stimulated drug release and hyperthermia, and discusses clinical translation barriers. Through this integrated perspective, we aim to advance the understanding and application of electromagnetic stimulated MNPs as a breakthrough approach in cancer therapy.

This article presents a way to improve the Diffusion LMS algorithm in practical scenarios while maintaining data privacy. It focuses on reducing external errors and ensuring accurate estimation of the parameter vector through distributed and adaptive privacy protection. By combining these improvements, the overall performance of the algorithm can be significantly enhanced without compromising Mean Square Deviation (MSD). One important aspect is tuning the privacy noise power based on data sensitivity. This work provides a novel solution for improving the performance and privacy of the Diffusion LMS algorithm with implications for future research. The mean square error and evolution behavior of the algorithms are also analyzed, showing their effectiveness and robustness in preserving privacy and MSD over the network.

In this brief, we propose a new diffusion normalized maximum Versoria criterion (d-NMVC) algorithm, which is based on maximization of the normalized maximum Versoria criterion (MVC) cost function to enhance the performance of the distributed estimation over networks in the presence of non-Gaussian noise. Convergence of the proposed algorithm, in the mean square sense and evolution behavior, under impulsive noise environment is also analyzed. Simulation results show the robustness of the proposed algorithm under impulsive noise environment against various non-Gaussian noise distributions.

Rabies virus as a neurotropic agent causes rabies in humans and animals. Rabies virus transmission usually occurs through direct contact with saliva of rabid animals. However, serological and molecular tests commonly are used in diagnosing rabies but all the detection methods of rabies have some limitations. It is necessary to develop a rapid, effective, and low-cost biosensor as an alternative tool to detect rabies virus. In this review, we studied related biosensor researches to rabies virus detection for comparing it with other detection test including serological and molecular methods. Given that very limited studies have been conducted in this field, biosensors as quick, effective, and high sensitivity tools can be used in diagnostic of rabies as an alternative tool instead of other detection methods. According to the important role of rapid detection of rabies in the control of infection and public health measures, development of a biosensor as a quick tool can be very significant in the diagnosis of rabies.

This paper proposes robust diffusion maximum versoria criterion algorithms to enhance the performance of the distributed estimation in a network of agents under impulsive noise environment. The diffusion maximum versoria criterion is a novel algorithm, under time-dependent constraint on the squared norm of the intermediate update at each node. To develop the robust version of the algorithm, the constraints are calculated by shared information which are collected from connected neighbors. The stability and steady-state performance of the proposed algorithms are also analyzed. We further exploit an altered dichotomous coordinate-descent (DCD) method to improve the performance and to reduce the complexity of the proposed algorithms in shifted-structure input regressors environment. Performance analysis and simulation results show the effectiveness and robustness of the proposed algorithms in various impulsive noise scenarios.

This paper aims to extend the proportionate adaptation concept to the design of a class of diffusion normalized subband adaptive filter (DNSAF) algorithms. This leads to four extensions of the algorithm associated with different step-size variations, namely diffusion proportionate normalized subband adaptive filter (DPNSAF), diffusion μ$$\mu $$-law PNSAF (DMPNSAF), diffusion improved PNSAF (DIPNSAF) and diffusion improved IPNSAF (DIIPNSAF). Subsequently, steady-state performance, stability conditions and computational complexity of the proposed algorithms are investigated. For each extension the performance has been evaluated using both real and simulated data, where the outcomes demonstrate the accuracy of the theoretical expressions and effectiveness of the proposed algorithms.

Demand side energy consumption scheduling is a well-known issue in the smart grid research area. However, there is lack of a comprehensive method to manage the demand side and consumer behavior in order to obtain an optimum solution. The method needs to address several aspects, including the scale-free requirement and distributed nature of the problem, consideration of renewable resources, allowing consumers to sell electricity back to the main grid, and adaptivity to a local change in the solution point. In addition, the model should allow compensation to consumers and ensurance of certain satisfaction levels. To tackle these issues, this paper proposes a novel autonomous demand side management technique which minimizes consumer utility costs and maximizes consumer comfort levels in a fully distributed manner. The technique uses a new logarithmic cost function and allows consumers to sell excess electricity (e.g. from renewable resources) back to the grid in order to reduce their electric utility bill. To develop the proposed scheme, we first formulate the problem as a constrained convex minimization problem. Then, it is converted to an unconstrained version using the segmentation-based penalty method. At each consumer location, we deploy an adaptive diffusion approach to obtain the solution in a distributed fashion. The use of adaptive diffusion makes it possible for consumers to find the optimum energy consumption schedule with a small number of information exchanges. Moreover, the proposed method is able to track drifts resulting from changes in the price parameters and consumer preferences. Simulations and numerical results show that our framework can reduce the total load demand peaks, lower the consumer utility bill, and improve the consumer comfort level.