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Neurochallenges in smart cities: state-of-the-art, perspectives, and research directions.
Front Neurosci
December 2024
The Institute of Biomedical Engineering, Boğaziçi University, Istanbul, Türkiye.
Smart city development is a complex, transdisciplinary challenge that requires adaptive resource use and context-aware decision-making practices to enhance human functionality and capabilities while respecting societal and environmental rights, and ethics. There is an urgent need for action in cities, particularly to (i) enhance the health and wellbeing of urban residents while ensuring inclusivity in urban development (e.g.
View Article and Find Full Text PDFTemplate-Guided Nondeterministic Assembly of Organosilica Nanodots for Multifunctional Physical Unclonable Functions.
ACS Appl Mater Interfaces
January 2025
Jieyang Branch of Chemistry and Chemical Engineering Guangdong Laboratory, Jieyang 515200, China.
Optical physical unclonable functions (PUFs) are gaining attention as a robust security solution for identification in the expanding Internet of Things (IoT). To enhance the security and functionality of PUFs, integrating multiple optical responses─such as fluorescence and structural color─into a single system is essential. These diverse optical properties enable multilevel authentication, where different layers of security can be verified under varying light conditions, greatly reducing the risk of counterfeiting.
View Article and Find Full Text PDFEnhanced Acoustoelectric Energy Harvesting with TiCT MXene in an All-Fiber Nanogenerator.
ACS Appl Mater Interfaces
January 2025
Institute for Frontier Materials, Deakin University, Waurn Ponds, Victoria 3216, Australia.
Materials and devices that harvest acoustic energy can enable autonomous powering of microdevices and wireless sensors. However, traditional acoustic energy harvesters rely on brittle piezoceramics, which have restricted their use in wearable electronic devices. To address these limitations, this study involves the fabrication of acoustic harvesters using electrospinning of the piezoelectric polymer PVDF-TrFE onto fabric-based electrodes.
View Article and Find Full Text PDFAdaptive deep feature representation learning for cross-subject EEG decoding.
BMC Bioinformatics
December 2024
College of Computer and Information Engineering/College of Artificial Intelligence, Nanjing Tech University, Nanjing, 210093, China.
Background: The collection of substantial amounts of electroencephalogram (EEG) data is typically time-consuming and labor-intensive, which adversely impacts the development of decoding models with strong generalizability, particularly when the available data is limited. Utilizing sufficient EEG data from other subjects to aid in modeling the target subject presents a potential solution, commonly referred to as domain adaptation. Most current domain adaptation techniques for EEG decoding primarily focus on learning shared feature representations through domain alignment strategies.
View Article and Find Full Text PDFThe role of industry 4.0 enabling technologies for predicting, and managing of algal blooms: Bridging gaps and unlocking potential.
Mar Pollut Bull
December 2024
Institute for Water and Wastewater Technology, Durban University of Technology, Durban-4001, South Africa. Electronic address:
Recent advancements in data analytics, predictive modeling, and optimization have highlighted the potential of integrating algal blooms (ABs) with Industry 4.0 technologies. Among these innovations, digital twins (DT) have gained prominence, driven by the rapid development of artificial intelligence (AI) and machine learning (ML) technologies, particularly those associated with the Internet of Things (IoT).
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