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Magnetic resonance imaging (MRI) contrast agents are utilized adjunctively to enhance the contrast between normal and abnormal structures on MRI scans. Along with the rapid evolution of the field has come a new appreciation for the medicinal chemistry of this unique class of metallopharmaceuticals. The efficacy of MRI agents is a complex function of chemical, biophysical, and pharmacological properties, which must be married in a package of exquisite safety. This report illustrates the wide range of medicinal chemistry relevant to existing agents that are either approved or in clinical development, as well as concepts, which may result in exciting new pharmaceuticals in the future.

Blockchain technologies (BCT) are utilized in healthcare to facilitate a smart and secure transmission of patient data. BCT solutions, however, are unable to store data produced by IoT devices in smart healthcare applications because these applications need a quick consensus process, meticulous key management, and enhanced eprivacy standards. In this work, a smart and secure eHealth framework SSEHCET (Smart and Secure EHealth Framework using Cutting-edge Technologies) is proposed that leverages the potentials of modern cutting-edge technologies (IoT, 5G, mobile edge computing, and BCT), which comprises six layers: 1) The sensing layer-WBAN consists of medical sensors that normally are on or within the bodies of patients and communicate data to smartphones. 2) The edge layer consists of elements that are near IoT devices to collect data. 3) The Communication layer leverages the potential of 5G technology to transmit patients' data between multiple layers efficiently. 4) The storage layer consists of cloud servers or other powerful computers. 5) Security layer, which uses BCT to transmit and store patients' data securely. 6) The healthcare community layer includes healthcare professionals and institutions. For the processing of medical data and to guarantee dependable, safe, and private communication, a Smart Agent (SA) program was duplicated on all layers. The SA leverages the potential of BCT to protect patients' privacy when outsourcing data. The contribution is substantiated through a meticulous evaluation, encompassing security, ease of use, user satisfaction, and SSEHCET structure. Results from an in-depth case study with a prominent healthcare provider underscore SSEHCET's exceptional performance, showcasing its pivotal role in advancing the security, usability, and user satisfaction paradigm in modern eHealth landscapes.

IoT systems based on collaborative sensor networks are becoming increasingly common in various industries owing to the increased availability of low-cost sensors. The quality of the data provided by these sensors may be unknown. For these reasons, advanced data processing and sensor network self-calibration methods have become popular research topics. In terms of metrology, the self-calibration methods lack the traceability to the established measurement standards of National Metrology Institutes (NMIs) through an unbroken chain-link of calibration. This problem can be solved by the ongoing digitalization of the metrology infrastructure. We propose a conceptual solution based on Digital Calibration Certificates (DCCs), Digital SI (D-SI), and cryptographic digital identifiers, for validation of data quality and trustworthiness. The data that enable validation and traceability can be used to improve analytics, decision-making, and security in industrial applications. We discuss the applicability and benefits of our solutions in a selection of industrial use cases, where collaborative sensing has already been introduced. We present the remaining challenges in the digitization and standardization processes regarding digital metrology and the future work required to address them.

[Purpose/Significance] In the era of cloud computing, the Library Services Platform (LSP) failed to become a unified solution for libraries it promised to be. Now, it faces new development bottlenecks in the era of smart libraries. Its relatively rigid architecture, isolated data models, and limited intelligence level make it difficult to meet modern users' urgent demands for access to new resource ecosystems and proactive services. This limitation stems from the fact that existing LSPs are rooted in a resource management design philosophy. They lack native support for intelligence, personalization, and ecosystem integration, which hinders their ability to serve as a core component in the construction of smart libraries. [Method/Process] The rapid development of large language model (LLM) technology is promoting libraries to transition from digital intelligent phases into a new era of intelligent services. As AI agents are increasingly emerge as a core strategy for LLM applications, this paper proposes a next

This study investigates the transformative influence of artificial intelligence (AI) on accounting procedures in Saudi Arabia by examining accounting professionals’ attitudes, understanding, and practices regarding AI implementation. Using a questionnaire-based survey distributed among accounting professionals in Saudi Arabia, data analysis was conducted using the partial least squares (PLS) technique. We find significant direct relationships between AI awareness and usage, AI engagement and accountants, and the impact of AI and accounting procedures. This finding suggests that accountants who are knowledgeable about and utilize AI are more likely to be engaged in AI, leading to positive changes in accounting procedures. Moreover, the robust positive relationship between AI’s impact on accounting procedures and accounting efficiency indicates a significant positive influence. The outcomes revealed that AI engagement and impact played significant mediating roles in these relationships. These findings suggest that, while AI awareness and usage alone can lead to improved accounting outcomes, the effect is mediated by the level of engagement with AI and its impact. We provide compelling evidence that AI positively affects the accounting profession. Accountants who are aware of and use AI are more likely to engage in and experience positive changes in their accounting procedures. We provide theoretical insights into the influence of AI on accounting procedures, offer valuable resources to academics, and suggest areas for future research. It equips practitioners with effective strategies for incorporating AI into their practices, highlighting the harnessing of AI’s transformative potential of AI while addressing potential challenges.

PurposeThe purpose of this work is to bridge FL and blockchain technology through designing a blockchain-based smart agent system architecture and applying in FL. and blockchain technology through designing a blockchain-based smart agent system architecture and applying in FL. FL is an emerging collaborative machine learning technique that trains a model across multiple devices or servers holding private data samples without exchanging their data. The locally trained results are aggregated by a centralized server in a privacy-preserving way. However, there is an assumption where the centralized server is trustworthy, which is impractical. Fortunately, blockchain technology has opened a new era of data exchange among trustless strangers because of its decentralized architecture and cryptography-supported techniques.Design/methodology/approachIn this study, the author proposes a novel design of a smart agent inspired by the smart contract concept. Specifically, based on the proposed smart agent, a fully decentralized, privacy-preserving and fair deep learning blockchain-FL framework is designed, where the agent network is consistent with the blockchain network and each smart agent is a participant in the FL task. During the whole training process, both the data and the model are not at the risk of leakage.FindingsA demonstration of the proposed architecture is designed to train a neural network. Finally, the implementation of the proposed architecture is conducted in the Ethereum development, showing the effectiveness and applicability of the design.Originality/valueThe author aims to investigate the feasibility and practicality of linking the three areas together, namely, multi-agent system, FL and blockchain. A blockchain-FL framework, which is based on a smart agent system, has been proposed. The author has made several contributions to the state-of-the-art. First of all, a concrete design of a smart agent model is proposed, inspired by the smart contract concept in blockchain. The smart agent is autonomous and is able to disseminate, verify the information and execute the supported protocols. Based on the proposed smart agent model, a new architecture composed by these agents is formed, which is a blockchain network. Then, a fully decentralized, privacy-preserving and smart agent blockchain-FL framework has been proposed, where a smart agent acts as both a peer in a blockchain network and a participant in a FL task at the same time. Finally, a demonstration to train an artificial neural network is implemented to prove the effectiveness of the proposed framework.

Nowadays, AI has many applications in everyday human activities such as exercise, eating, sleeping, and automobile driving. Tech companies can apply AI to identify individual behaviors (e.g., walking, eating, driving), analyze them, and offer personalized feedback to help individuals make improvements accordingly. While offering personalized feedback is more beneficial for drivers, most smart driver systems in the current market do not use it. This paper presents AutoCoach, an intelligent AI agent that classifies drivers’ into different driving-personality groups to offer personalized feedback. We have built a cloud-based Android application to collect, analyze and learn from a driver’s past driving data to provide personalized, constructive feedback accordingly. Our GUI interface provides real-time user feedback for both warnings and rewards for the driver. We have conducted an on-the-road pilot user study. We conducted a pilot study where drivers were asked to use different agent versions to compare personality-based feedback versus non-personality-based feedback. The study result proves our design’s feasibility and effectiveness in improving the user experience when using a personality-based driving agent, with 61% overall acceptance that it is more accurate than non-personality-based.

This study aims to develop a quantum computing-based neurocognitive architecture that allows an agent to perform autonomous behaviors. Therefore, we present a brain-inspired cognitive architecture for autonomous agents that integrates a prefrontal cortex–inspired model with modern deep learning (a transformer-based reinforcement learning module) and quantum algorithms. In particular, our framework incorporates quantum computational routines (Deutsch–Jozsa, Bernstein–Vazirani, and Grover’s search) to enhance decision-making efficiency. As a novelty of this research, this comprehensive computational structure is empowered by quantum computing operations so that superiority in speed and robustness of learning compared to classical methods can be demonstrated. Another main contribution is that the proposed architecture offers some features, such as meta-cognition and situation awareness. The meta-cognition aspect is responsible for hierarchically learning sub-tasks, enabling the agent to achieve the master goal. The situation-awareness property identifies how spatial-temporal reasoning activities related to the world model of the agent can be extracted in a dynamic simulation environment with unstructured uncertainties by quantum computation-based machine learning algorithms with the explainable artificial intelligence paradigm. In this research, the Minecraft game-based simulation environment is utilized for the experimental evaluation of performance and verification tests within complex, multi-objective tasks related to the autonomous behaviors of a smart agent. By implementing several interaction scenarios, the results of the system performance and comparative superiority over alternative solutions are presented, and it is discussed how these autonomous behaviors and cognitive skills of a smart agent can be improved in further studies. Results show that the quantum-enhanced agent achieves 2× faster convergence to an 80% task success rate in exploration tasks and approximately 15% higher cumulative rewards compared to a classical deep RL baseline. These findings demonstrate the potential of quantum algorithms to significantly improve learning and performance in cognitive agent architectures. However, advantages are task-specific and less pronounced under high-uncertainty, reactive scenarios. Limitations of the simulation environment are acknowledged, and a structured future research roadmap is proposed involving high-fidelity simulation validation, hardware-in-the-loop robotic testing, and integration of advanced hybrid quantum-classical architectures.

After conversational agents have been made available to the broader public, we speculate that applying them as a mediator for adaptive environments reduces control complexity and increases user experience by providing a more natural interaction. We implemented and tested four agents, each of them differing in their system intelligence and input modality, as personal assistants for Mediated Atmospheres, an adaptive smart office prototype. They were evaluated in a user study ( N = 33 ) to collect subjective and objective measures. Results showed that a smartphone application was the most favorable system, followed by conversational text and voice agents that were perceived as being more engaging and intelligent than a non-conversational voice agent. Significant differences were observed between native and non-native speakers in both subjective and objective measures. Our findings reveal the potential of conversational agents for the interaction with adaptive environments to reduce work and information overload.

Nanoparticle drug delivery carriers, which can implement high performances of multi-functions, are of great interest, especially for improving cancer therapy. Herein, we reported a new approach to construct Mn2+-coordinated doxorubicin (DOX)-loaded poly(lactic-co-glycolic acid) (PLGA) nanoparticles as a platform for synergistic chemo-photothermal tumor therapy. DOX-loaded PLGA (DOX/PLGA) nanoparticles were first synthesized through a double emulsion-solvent evaporation method, and then modified with polydopamine (PDA) through self-polymerization of dopamine, leading to the formation of PDA@DOX/PLGA nanoparticles. Mn2+ ions were then coordinated on the surfaces of PDA@DOX/PLGA to obtain Mn2+-PDA@DOX/PLGA nanoparticles. In our system, Mn2+-PDA@DOX/PLGA nanoparticles could destroy tumors in a mouse model directly, by thermal energy deposition, and could also simulate the chemotherapy by thermal-responsive delivery of DOX to enhance tumor therapy. Furthermore, the coordination of Mn2+ could afford the high magnetic resonance (MR) imaging capability with sensitivity to temperature and pH. The results demonstrated that Mn2+-PDA@DOX/PLGA nanoparticles had a great potential as a smart theranostic agent due to their imaging and tumor-growth-inhibition properties.