Large Language Model‐Based Chatbots in Higher Education

Advanced Intelligent Systems, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 11, 2024

Large language models (LLMs) are artificial intelligence (AI) platforms capable of analyzing and mimicking natural processing. Leveraging deep learning, LLM capabilities have been advanced significantly, giving rise to generative chatbots such as Generative Pre‐trained Transformer (GPT). GPT‐1 was initially released by OpenAI in 2018. ChatGPT's release 2022 marked a global record speed technology uptake, attracting more than 100 million users two months. Consequently, the utility LLMs fields including engineering, healthcare, education has explored. The potential LLM‐based higher sparked significant interest ignited debates. can offer personalized learning experiences advance asynchronized potentially revolutionizing education, but also undermine academic integrity. Although concerns regarding AI‐generated output accuracy, spread misinformation, propagation biases, other legal ethical issues not fully addressed yet, several strategies implemented mitigate these limitations. Here, development LLMs, properties chatbots, applications discussed. Current challenges associated with AI‐based outlined. potentials chatbot use context settings

Language: Английский

---

AI-Based Metamaterial Design

ACS Applied Materials & Interfaces, Journal Year: 2024, Volume and Issue: 16(23), P. 29547 - 29569

Published: May 29, 2024

The use of metamaterials in various devices has revolutionized applications optics, healthcare, acoustics, and power systems. Advancements these fields demand novel or superior that can demonstrate targeted control electromagnetic, mechanical, thermal properties matter. Traditional design systems methods often require manual manipulations which is time-consuming resource intensive. integration artificial intelligence (AI) optimizing metamaterial be employed to explore variant disciplines address bottlenecks design. AI-based also enable the development by parameters cannot achieved using traditional methods. application AI leveraged accelerate analysis vast data sets as well better utilize limited via generative models. This review covers transformative impact for current challenges, emerging fields, future directions, within each domain are discussed.

Language: Английский

---

Photonic Nanomaterials for Wearable Health Solutions

Advanced Materials, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 3, 2025

Abstract This review underscores the transformative potential of photonic nanomaterials in wearable health technologies, driven by increasing demands for personalized monitoring. Their unique optical and physical properties enable rapid, precise, sensitive real‐time monitoring, outperforming conventional electrical‐based sensors. Integrated into ultra‐thin, flexible, stretchable formats, these materials enhance compatibility with human body, enabling prolonged wear, improved efficiency, reduced power consumption. A comprehensive exploration is provided integration devices, addressing material selection, light‐matter interaction principles, device assembly strategies. The highlights critical elements such as form factors, sensing modalities, data communication, representative examples skin patches contact lenses. These devices precise monitoring management biomarkers diseases or biological responses. Furthermore, advancements approaches have paved way continuum care systems combining multifunctional sensors therapeutic drug delivery mechanisms. To overcome existing barriers, this outlines strategies design, engineering, system integration, machine learning to inspire innovation accelerate adoption next‐generation health, showcasing their versatility digital applications.

Language: Английский

---

Mechanical Metamaterials for Bioengineering: In Vitro, Wearable, and Implantable Applications

Advanced Engineering Materials, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 21, 2025

Mechanical metamaterials represent a promising class of materials characterized by unconventional mechanical properties derived from their engineered architectures. In the realm bioengineering, these offer unique opportunities for applications spanning in vitro models, wearable devices, and implantable biomedical technologies. This review discusses recent advancements bioengineering contexts. metamaterials, tailored to mimic specific biological tissues, enhance fidelity relevance models disease modeling therapy testing. Integration into devices enables creation comfortable adaptive interfaces with human body. Utilization promotes tissue regeneration, supports biomechanical functions, minimizes host immune responses. Key design strategies material selection criteria critical optimizing performance biocompatibility are elucidated. Representative case studies demonstrating benchtop phantoms scaffolds (in platforms); footwear, architectured fabrics, epidermal sensors (wearables); cardiovascular, gastrointestinal, orthopedic multifunctional patches highlighted. Finally, challenges future directions field discussed, emphasizing potential transform research enabling novel functionalities improving outcomes across diverse use cases.

Language: Английский

---

Machine learning‐enabled optimization of melt electro‐writing three‐dimensional printing

Aggregate, Journal Year: 2024, Volume and Issue: 5(3)

Published: Jan. 4, 2024

Abstract Melt electrowriting (MEW) is a solvent‐free (i.e., no volatile chemicals), high‐resolution three‐dimensional (3D) printing method that enables the fabrication of semi‐flexible structures with rigid polymers. Despite its advantages, MEW process sensitive to changes in parameters (e.g., voltage, pressure, and temperature), which can cause fluid column breakage, jet lag, and/or fiber pulsing, ultimately deteriorating resolution quality. In spite commonly used error‐and‐trial determine most suitable parameters, here, we present machine learning (ML)‐enabled image analysis‐based for determining optimum through an easy‐to‐use graphical user interface (GUI). We trained five different ML algorithms using 168 3D print samples, among Gaussian regression model yielded 93% accuracy variability dependent variable, 0.12329 on root mean square error validation set 0.015201 predicting line thickness. Integration control feedback loop reduce steps prior process, decreasing time increasing overall throughput MEW) material waste improving cost‐effectiveness MEW). Moreover, embedding system GUI facilitates more straightforward use ML‐based optimization techniques industrial section users skills).

Language: Английский

---

Recent Advances in Machine Learning Assisted Hydrogel Flexible Sensing

Zeitschrift für anorganische und allgemeine Chemie, Journal Year: 2024, Volume and Issue: 650(13-14)

Published: May 11, 2024

Abstract Hydrogel flexible sensors are widely used in wearable devices, health care, intelligent robots and other fields due to their excellent flexibility, biocompatibility high sensitivity. With the development of single sensor multi‐channel multi‐mode network, data also presents characteristics multi‐dimension, complex massive. Traditional analysis methods can no longer meet requirements hydrogel networks. The introduction machine learning (ML) technology optimizes process analysis. continuous multi‐layer neural network improvement computer performance, deep (DL) algorithm is increasingly achieve higher efficiency accuracy, provides a powerful tool for sensor, accelerates equipment. This paper introduces classification working mechanism common algorithms ML, summarizes application ML assist care information recognition. review will provide inspiration reference integrating into field sensors.

Language: Английский

---

Structurally Transformable and Reconfigurable Hydrogel-Based Mechanical Metamaterials and Their Application in Biomedical Stents

ACS Applied Materials & Interfaces, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 2, 2025

Mechanical metamaterials exhibit several unusual mechanical properties, such as a negative Poisson's ratio, which impart additional capabilities to materials. Recently, hydrogels have emerged exceptional candidates for fabricating that offer enhanced functionality and expanded applications due their unique responsive characteristics. However, the adaptability of these remains constrained underutilized, they lack integration hydrogels' soft characteristics with metamaterial design. Here, we propose structurally transformable reconfigurable hydrogel-based through three-dimensional (3D) printing lattice structures composed multishape-memory poly(acrylic acid)-chitosan hydrogels. By incorporating reversible shape-memory mechanisms control structural arrangements lattice, can under various environmental conditions, including auxetic behavior, ratios switchable from zero or positive. These adaptable responses across different states arise changes in surpassing gradual observed conventional stimuli-responsive The application multimode biomedical stents demonstrates practical settings, allowing them transition between expandable, nonexpandable, shrinkable states, corresponding ratios. integrating materials design, significantly enhance functionality, advancing development smart biomaterials.

Language: Английский

---

Trends and Advances in Wearable Plasmonic Sensors Utilizing Surface-Enhanced Raman Spectroscopy (SERS): A Comprehensive Review

Sensors, Journal Year: 2025, Volume and Issue: 25(5), P. 1367 - 1367

Published: Feb. 23, 2025

Wearable sensors have appeared as a promising solution for real-time, non-invasive monitoring in diverse fields, including healthcare, environmental sensing, and wearable electronics. Surface-enhanced Raman spectroscopy (SERS)-based leverage the unique properties of SERS, such plasmonic signal enhancement, high molecular specificity, potential single-molecule detection, to detect identify wide range analytes with ultra-high sensitivity selectivity. However, it is important note that utilize various sensing mechanisms, not all rely on SERS technology, their design depends specific application. This comprehensive review highlights recent trends advancements technologies, focusing design, fabrication, integration into practical devices. Key innovations material selection, use nanomaterials flexible substrates, significantly enhanced sensor performance wearability. Moreover, we discuss challenges miniaturization, power consumption, long-term stability, along solutions address these issues. Finally, outlook technologies presented, emphasizing need interdisciplinary research drive next generation smart wearables capable real-time health diagnostics, monitoring, beyond.

Language: Английский

---

Transforming Healthcare: Intelligent Wearable Sensors Empowered by Smart Materials and Artificial Intelligence

Advanced Materials, Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

Intelligent wearable sensors, empowered by machine learning and innovative smart materials, enable rapid, accurate disease diagnosis, personalized therapy, continuous health monitoring without disrupting daily life. This integration facilitates a shift from traditional, hospital-centered healthcare to more decentralized, patient-centric model, where sensors can collect real-time physiological data, provide deep analysis of these data streams, generate actionable insights for point-of-care precise diagnostics therapy. Despite rapid advancements in learning, sensing technologies, there is lack comprehensive reviews that systematically examine the intersection fields. review addresses this gap, providing critical technologies advanced materials artificial Intelligence. The state-of-the-art materials-including self-healing, metamaterials, responsive materials-that enhance sensor functionality are first examined. Advanced methodologies integrated into devices discussed, their role biomedical applications highlighted. combined impact intelligent therapeutics also Finally, existing challenges, including technical compliance issues, information security concerns, regulatory considerations addressed, future directions advancing proposed.

Language: Английский

---

Machine learning in point-of-care testing: innovations, challenges, and opportunities

Nature Communications, Journal Year: 2025, Volume and Issue: 16(1)

Published: April 2, 2025

Language: Английский

---