Current Issue : January-March Volume : 2026 Issue Number : 1 Articles : 5 Articles
Artificial intelligence (AI) techniques have propelled biomedical sensors beyond measuring physiological markers to interpreting subjective states like stress, pain, or emotions. Despite these technological advances, user trust is not guaranteed and is inadequately addressed in extant research. This review proposes the Cognition–Context–Contrast (CCC) conceptual framework to explain the trust and acceptance of AI-enabled sensors. First, we map cognition, comprising the expectations and stereotypes that humans have about machines. Second, we integrate task context by situating sensor applications along an intellective-to-judgmental continuum and showing how demonstrability predicts tolerance for sensor uncertainty and/or errors. Third, we analyze contrast effects that arise when automated sensing displaces familiar human routines, heightening scrutiny and accelerating rejection if roll-out is abrupt. We then derive practical implications such as enhancing interpretability, tailoring data presentations to task demonstrability, and implementing transitional introduction phases. The framework offers researchers, engineers, and clinicians a structured conceptual framework for designing and implementing the next generation of AI biosensors....
The growing demand for innovative home security solutions has accelerated the integration of advanced technologies to enhance safety, convenience, and operational efficiency. Artificial intelligence (AI) has become a pivotal element in revolutionizing home security systems by enabling real-time threat detection, automated surveillance, and intelligent decision-making. This study employs a systematic literature review (SLR) to explore recent advancements in AI-driven technologies, such as machine learning, computer vision, natural language processing, and the Internet of Things (IoT). These innovations enhance security by providing features like facial recognition, anomaly detection, voice-activated controls, and predictive analysis, delivering more accurate and responsive security solutions. Furthermore, this study addresses challenges related to data privacy, cybersecurity threats, and cost considerations while emphasizing AI’s potential to deliver scalable, efficient, and user-friendly systems. The findings demonstrate AI’s vital role in the evolution of home security technologies, paving the way for smarter and safer living environments....
Nowadays, Graphical Processor Units (GPUs) are a great technology to implement Artificial Intelligence (AI) processes; however, a challenge arises when the inclusion of a GPU is not feasible due to the cost, power consumption, or the size of the hardware. This issue is particularly relevant for portable devices, such as laptops or smartphones, where the inclusion of a dedicated GPU is not the best option. One possible solution to that problem is the use of a CPU with AI capabilities, i.e., parallelism and high performance. In particular, RISC-V architecture is considered a good open-source candidate to support such tasks. These capabilities are based on vector operations that, by definition, operate over many elements at the same time, allowing for the execution of SIMD instructions that can be used to implement typical AI routines and procedures. In this context, the main purpose of this proposal is to develop an ASIC Vector Engine RISC-V architecture compliant that implements a minimum set of the Vector Extension capable of the parallel processing of multiple data elements with a single instruction. These instructions operate on vectors and involve addition, multiplication, logical, comparison, and permutation operations. Especially, the multiplication was implemented using the Vedic multiplication algorithm. Contributions include the description of the design, synthesis, and validation processes to develop the ASIC, and a performance comparison between the FPGA implementation and the ASIC using different nanometric technologies, where the best performance of 110 MHz, and the best implementation in terms of silicon area, was achieved by 7 nm technology....
Artificial intelligence (AI)-generated content (AIGC) is an innovative technology that utilizes machine learning, AI models, reward modeling, and natural language processing (NLP) to create diverse digital content such as videos, images, and text. It has the potential to support various human activities with significant implications in teaching and learning, facilitating heuristic teaching for educators. By using AIGC, teachers can create extensive knowledge content and effectively design instructional strategies to guide students, aligning with heuristic teaching. However, incorporating AIGC into heuristic teaching has controversies and concerns, which potentially mislead outcomes. Nevertheless, leveraging AIGC greatly benefits teachers in enhancing heuristic teaching. When integrating AIGC to support heuristic teaching, challenges and risks must be acknowledged and addressed. These challenges include the need for users to possess sufficient knowledge reserves to identify incorrect information and content generated by AIGC, the importance of avoiding excessive reliance on AIGC, ensuring users maintain control over their actions rather than being driven by AIGC, and the necessity of scrutinizing and verifying the accuracy of information and knowledge generated by AIGC to preserve its effectiveness....
The integration of large language models (LLMs) into structural engineering workflows presents both a transformative opportunity and a critical challenge. While LLMs enable intuitive, natural language interactions with complex data, their limited arithmetic reasoning, contextual fragility, and lack of verifiability constrain their application in safety-critical domains. This study introduces a novel automation pipeline that couples generative AI with finite element modelling through the Model Context Protocol (MCP)—a modular, context-aware architecture that complements language interpretation with structural computation. By interfacing GPT-4 with OpenSeesPy via MCP (JSON schemas, API interfaces, communication standards), the system allows engineers to specify and evaluate 3D frame structures using conversational prompts, while ensuring computational fidelity and code compliance. Across four case studies, the GPT+MCP framework demonstrated predictive accuracy for key structural parameters, with deviations under 1.5% compared to reference solutions produced using conventional finite element analysis workflows. In contrast, unconstrained LLM use produces deviations exceeding 400%. The architecture supports reproducibility, traceability, and rapid analysis cycles (6–12 s), enabling real-time feedback for both design and education. This work establishes a reproducible framework for trustworthy AI-assisted analysis in engineering, offering a scalable foundation for future developments in optimisation and regulatory automation....
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