am a computer scientist in the interdisciplinary areas of computational social science and social computing. By complementing multimodal datastreams with social media, I adopt methods from machine learning, statistics, natural language
Nanoscience breakthroughs in almost every field of science and nanotechnologies make life easier in this era. Nanoscience and nanotechnology represent an expanding research area, which involves structures, devices, and systems with novel properties and functions due to the arrangement of their atoms on the 1–100 nm scale. The field was subject to a growing public awareness and controversy in the early 2000s, and in turn, the beginnings of commercial applications of nanotechnology. Nanotechnologies contribute to almost every field of science, including physics, materials science, chemistry, biology, computer science, and engineering. Notably, in recent years nanotechnologies have been applied to human health with promising results, especially in the field of cancer treatment. To understand the nature of nanotechnology, it is helpful to review the timeline of discoveries that brought us to the current understanding of this science. This review illustrates the progress and main principles of nanoscience and nanotechnology and represents the pre-modern as well as modern timeline era of discoveries and milestones in these fields.
John A. Keith, V. Vassilev-Galindo, Bingqing Cheng
et al.
Machine learning models are poised to make a transformative impact on chemical sciences by dramatically accelerating computational algorithms and amplifying insights available from computational chemistry methods. However, achieving this requires a confluence and coaction of expertise in computer science and physical sciences. This Review is written for new and experienced researchers working at the intersection of both fields. We first provide concise tutorials of computational chemistry and machine learning methods, showing how insights involving both can be achieved. We follow with a critical review of noteworthy applications that demonstrate how computational chemistry and machine learning can be used together to provide insightful (and useful) predictions in molecular and materials modeling, retrosyntheses, catalysis, and drug design.
Artificial Intelligence (AI) is rapidly transforming engineering fields, from robotics to aerospace, with applications in control systems for UAVs and satellites. This work builds on a previously developed AI attitude controller for the InnoCube 3U nanosatellite. Deploying complex Neural Networks (NNs) on resource-limited microcontrollers presents a significant challenge. To overcome this, we propose distilling a Multi-Layer Perceptron (MLP) trained with Deep Reinforcement Learning (DRL) for attitude control into a Kolmogorov–Arnold Network (KAN). We convert this numeric KAN into a symbolic KAN, where each edge represents a learnable mathematical function, and finally extract a concise symbolic formula. This symbolic representation dramatically reduces memory usage and computational complexity, making it ideal for pico- and nanosatellites. We evaluate and demonstrate the feasibility of this approach for inertial pointing with reaction wheels in simulation using a realistic model of the InnoCube satellite. Our results show that the highly compressed KANs successfully solve the attitude control problem, while reducing the required memory footprint and inference time on the InnoCube ADCS hardware by over an order of magnitude. Beyond attitude control, we believe symbolic KANs hold great potential in aerospace for neural network compression and interpretable, data-driven modeling and system identification in future space missions.
To model the response of neural networks to electromagnetic radiation in real-world environments, this study proposes a memristive dual-wing fractional-order Hopfield neural network (MDW-FOMHNN) model, utilizing a fractional-order memristor to simulate neuronal responses to electromagnetic radiation, thereby achieving complex chaotic dynamics. Analysis reveals that within specific ranges of the coupling strength, the MDW-FOMHNN lacks equilibrium points and exhibits hidden chaotic attractors. Numerical solutions are obtained using the Adomian Decomposition Method (ADM), and the system’s chaotic behavior is confirmed through Lyapunov exponent spectra, bifurcation diagrams, phase portraits, and time series. The study further demonstrates that the coupling strength and fractional order significantly modulate attractor morphologies, revealing diverse attractor structures and their coexistence. The complexity of the MDW-FOMHNN output sequence is quantified using spectral entropy, highlighting the system’s potential for applications in cryptography and related fields. Based on the polynomial form derived from ADM, a field programmable gate array (FPGA) implementation scheme is developed, and the expected chaotic attractors are successfully generated on an oscilloscope, thereby validating the consistency between theoretical analysis and numerical simulations. Finally, to link theory with practice, a simple and efficient MDW-FOMHNN-based encryption/decryption scheme is presented.
Abstract The application of the $$\Phi$$ -OTDR (Phase-Optical Time Domain Reflectometry) system in real-time monitoring of power grid infrastructure has been proven effective in identifying and classifying various anomalies, such as digging, watering, and shaking. However, previous deep learning-based methods for $$\Phi$$ -OTDR event classification are primarily designed for balanced classification problems, where the number of abnormal and normal event samples is relatively equal. In practical scenarios, the data for abnormal events are often much smaller than those for normal events (noise), resulting in a long-tailed distribution problem that poses significant challenges for accurate classification. To address this long-tailed imbalance issue in the practical application of $$\Phi$$ -OTDR data, we introduce the Controllable Diffusion (ConDiff) framework, which aims to generate high-quality synthetic samples for abnormal situations. The ConDiff framework is composed of three essential components: Feedback-guided $$\Phi$$ -OTDR Augmenter, the High-Quality Sample Selection module, and the Dynamic Threshold Adjustment module. The Feedback-guided $$\Phi$$ -OTDR Augmenter utilizes diffusion model to generate synthetic samples that simulate abnormal events. The High-Quality Sample Selection module evaluates the quality of the generated synthetic samples and selects high-Quality samples. The Dynamic Threshold Adjustment module provides real-time feedback to dynamically control the sample generation process of Feedback-guided $$\Phi$$ -OTDR Augmenter. Compared to current state-of-the-art baselines, our proposed ConDiff framework achieves a notable improvement in classification accuracy, with an increase ranging from 3.7% to 7.2% in the BJTU-OTDR-LT dataset. This improvement demonstrates the effectiveness of the proposed ConDiff framework in addressing the long-tailed imbalance problem in $$\Phi$$ -OTDR event classification. The code will be released upon acceptance.