Smart materials are kinds of designed materials whose properties are controllable with the application of external stimuli such as the magnetic field, electric field, stress, and heat. Smart materials whose rheological properties are controlled by externally applied magnetic field are known as magneto-rheological materials. Magneto-rheological materials actively used for engineering applications include fluids, foams, grease, elastomers, and plastomers. In the last two decades, magneto-rheological materials have gained great attention of researchers significantly because of their salient controllable properties and potential applications to various fields such as automotive industry, civil environment, and military sector. This article offers a recent progressive review on the magneto-rheological materials technology, especially focusing on numerous application devices and systems utilizing magneto-rheological materials. Conceivable limitations, challenges, and comparable advantages of applying these magneto-rheological materials in various sectors are analyzed critically, which provides a clear pathway to the researchers in selecting and utilizing these materials. The review starts with an introduction to the elementary description of magneto-rheological materials and their significant contribution in various fields. Following this, different types of the magneto-rheological materials, modeling of the magneto-rheological materials, magneto-rheological material–based devices, and their applications have been extensively reviewed to promote practical use of magneto-rheological materials in a wide spectrum of the application from the automobile to medical device.

本文对各种磁流变阻尼器的优化设计、制造和智 能应用以及自供电和自感应技术的最新进展进 行了综述。本文讨论了磁流变阻尼器的基本设计 和结构以及各种类型的配置,以了解它们在各种 环境和目的下的多功能性。为了应对不同的应 用,本文介绍了设计的修改、优化和改进。节能 是当前的终极需求,是对现代技术的挑战。磁流 变阻尼器需要改进,以确保较低的电流供应得到 较高的效力。这项工作将有助于在各种结构中使 用磁流变

Data stream has been the underlying challenge in the age of big data because it calls for real-time data processing with the absence of a retraining process and/or an iterative learning approach. In realm of fuzzy system community, data stream is handled by algorithmic development of self-adaptive neurofuzzy systems (SANFS) characterized by the single-pass learning mode and the open structure property which enables effective handling of fast and rapidly changing natures of data streams. The underlying bottleneck of SANFSs lies in its design principle which involves a high number of free parameters (rule premise and rule consequent) to be adapted in the training process. This figure can even double in the case of type-2 fuzzy system. In this work, a novel SANFS, namely parsimonious learning machine (PALM), is proposed. PALM features utilization of a new type of fuzzy rule based on the concept of hyperplane clustering which significantly reduces the number of network parameters because it has no rule premise parameters. PALM is proposed in both type-1 and type-2 fuzzy systems where all of which characterize a fully dynamic rule-based system. That is, it is capable of automatically generating, merging and tuning the hyperplane-based fuzzy rule in the single pass manner. Moreover, an extension of PALM, namely recurrent PALM (rPALM), is proposed and adopts the concept of teacher-forcing mechanism in the deep learning literature. The efficacy of PALM has been evaluated through numerical study with six real-world and synthetic data streams from public database and our own real-world project of autonomous vehicles. The proposed model showcases significant improvements in terms of computational complexity and number of required parameters against several renowned SANFSs, while attaining comparable and often better predictive accuracy.

Large Language Models (LLMs) are advancing rapidly and promising transformation across fields but pose challenges in oversight, ethics, and user trust. This review addresses trust issues like unintentional harms, opacity, vulnerability, misalignment with values, and environmental impact, all of which affect trust. Factors undermining trust include societal biases, opaque processes, misuse potential, and technology evolution challenges, especially in finance, healthcare, education, and policy. Recommended solutions include ethical oversight, industry accountability, regulation, and public involvement to reshape AI norms and incorporate ethics into development. A framework assesses trust in LLMs, analyzing trust dynamics and providing guidelines for responsible AI development. The review highlights limitations in building trustworthy AI, aiming to create a transparent and accountable ecosystem that maximizes benefits and minimizes risks, offering guidance for researchers, policymakers, and industry in fostering trust and ensuring responsible use of LLMs. We validate our frameworks through comprehensive experimental assessment across seven contemporary models, demonstrating substantial improvements in trustworthiness characteristics and identifying important disagreements with existing literature. Both theoretical foundations and empirical validation are provided in comprehensive supplementary materials.