Customizable wave tailoring nonlinear materials enabled by bilevel inverse design by Brianna MacNider, Haning Xiu, Caglar Tamur, Kai Qian, Ian Frankel, Maya Brandy, Hyunsun Alicia Kim, Nicholas Boechler

“…This study establishes a framework for broader passive nonlinear mechanical wave tailoring material design, with applications to computing, signal processing, shock mitigation, and autonomous materials.…”
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A Reinforced, Event-Driven, and Attention-Based Convolution Spiking Neural Network for Multivariate Time Series Prediction by Ying Li, Xikang Guan, Wenwei Yue, Yongsheng Huang, Bin Zhang, Peibo Duan

“…Despite spiking neural networks (SNNs) inherently exceling at processing time series due to their rich spatio-temporal information and efficient event-driven computing, the challenge of extracting complex correlations between variables in multivariate time series (MTS) remains to be addressed. …”
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Water quality prediction using LSTM with combined normalizer for efficient water management by N. Mahesh, J. Jagan Babu, K. Nithya, S.A. Arunmozhi

“…This allows for adaptive processing of multi-factor data while preserving the data's inherent characteristics. …”
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Digital Twin for Energy-Intelligent Bakery Operations: Concepts and Applications by Tsega Y. Melesse, Mohamed Shameer Peer, Suganthi Ramasamy, Vigneselvan Sivasubramaniyam, Mattia Braggio, Pier Francesco Orrù

“…This paper utilizes a narrative and integrative review approach, conceptually integrating emerging developments in using DT with respect toenergy management in the baking industry, including real-time energy monitoring, predictive maintenance, dynamic optimization of production processes, and the seamless integration of renewable energy sources. …”
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Artificial intelligence in higher education institutions: review of innovations, opportunities and challenges by Samuel Ocen, Joseph Elasu, Sylvia Manjeri Aarakit, Charles Olupot

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Multi-Satellite Task Parallelism via Priority-Aware Decomposition and Dynamic Resource Mapping by Shangpeng Wang, Chenyuan Zhang, Zihan Su, Limin Liu, Jun Long

“…Multi-satellite collaborative computing has achieved task decomposition and collaborative execution through inter-satellite links (ISLs), which has significantly improved the efficiency of task execution and system responsiveness. …”
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Towards 4D BIM: A Systematic Literature Review on Challenges, Strategies and Tools in Leveraging AI with BIM by Michael Awe, Avleen Malhi, Marcin Budka, Nicholas Mavengere, Bhargav Dave

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HE-BiDet: A Hardware Efficient Binary Neural Network Accelerator for Object Detection in SAR Images by Dezheng Zhang, Zehan Liang, Rui Cen, Zhihong Yan, Rui Wan, Dong Wang

“…Second, we design a BNN accelerator leveraging four-directions of parallelism and an on-chip data buffer with optimized addressing to feed the computing array efficiently. To accelerate post-processing, we introduce a hardware-based threshold filter to eliminate redundant anchor boxes early and a dedicated Non-Maximum Suppression (NMS) unit. …”
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BSDR: A Data-Efficient Deep Learning-Based Hyperspectral Band Selection Algorithm Using Discrete Relaxation by Mohammad Rahman, Shyh Wei Teng, Manzur Murshed, Manoranjan Paul, David Brennan

“…Hyperspectral band selection algorithms are crucial for processing high-dimensional data, which enables dimensionality reduction, improves data analysis, and enhances computational efficiency. …”
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Low Earth orbit satellite switching and recovery method based on generic congestion control algorithm by Hao Chen, Hang Yu, Hui Zhang, Huancheng Chen, Xiangsen Kong, Huaichuan Li, Demao Qing

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Simulation-based assessment of digital twin systems for immunisation by Leonardo de Oliveira El-Warrak, Claudio Miceli de Farias, Victor Hugo Dias Macedo De Azevedo Costa

“…The solution is based on Industry 4.0 concepts and technologies, such as IoT, machine learning, and cloud computing, and adheres to the ISO 23247 standard.MethodsThe system modelling is carried out using the Unified Modelling Language (UML) to define the workflows and processes involved, including vaccine storage temperature monitoring and population vaccination status tracking. …”
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Detection of Valvular Heart Diseases From PCG Signals Using Machine and Deep Learning Models: A Review by Ayappasamy Kannan, Manob Jyoti Saikia, Sushant Kumar, Sumit Datta

“…This study also aimed to provide an overview of ongoing research on PCG signal processing and to pinpoint areas that warrant additional investigation. …”
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Recent Advancements in Federated Learning: State of the Art, Fundamentals, Principles, IoT Applications and Future Trends by Christos Papadopoulos, Konstantinos-Filippos Kollias, George F. Fragulis

“…Nonetheless, the potential benefits are clear: FL can lower the costs associated with data storage and processing, while also helping organizations to meet strict privacy regulations like GDPR. …”
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Automated System for Evaluating Alternatives for Developing Innovative IT Projects by Iryna Pikh, Vsevolod Senkivskyy, Liubomyr Sikora, Nataliia Lysa, Alona Kudriashova

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Fine-Pruning: A biologically inspired algorithm for personalization of machine learning models by Joseph Bingham, Saman Zonouz, Dvir Aran

“…The bigger picture: Inspired by the concept of biological neural pruning, the Fine-Pruning approach addresses the need for large labeled datasets and extensive computational resources. It could enable real-time, on-device processing for small devices, including medical devices, without relying on cloud-based systems. …”
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A combinatory approach of non-chain ring and henon map for image encryption application by Salman Mohi Ud Din, Tariq Shah, Fahad Alblehai, Sameer Nooh, Sajjad Shaukat Jamal

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The Relevance of Virtual-Assisted Early Childhood Education and Occupational Psychotherapy Based on Emotional Interaction by Dingran Qie

“…Firstly, the analysis of young children’s emotions involves massive image processing, and the use of cloud computing architecture leads to high latency, while young children’s safety is a latency-sensitive service. …”
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Determination of 5-fluorouracil anticancer drug solubility in supercritical CO 2 using semi-empirical and machine learning models by Gholamhossein Sodeifian, Ratna Surya Alwi, Reza Derakhsheshpour, Nedasadat Saadati Ardestani

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Automating multi-task learning on optical neural networks with weight sharing and physical rotation by Shanglin Zhou, Yingjie Li, Weilu Gao, Cunxi Yu, Caiwen Ding

“…Abstract The democratization of AI encourages multi-task learning (MTL), demanding more parameters and processing time. To achieve highly energy-efficient MTL, Diffractive Optical Neural Networks (DONNs) have garnered attention due to extremely low energy and high computation speed. …”
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Optimising TinyML with quantization and distillation of transformer and mamba models for indoor localisation on edge devices by Thanaphon Suwannaphong, Ferdian Jovan, Ian Craddock, Ryan McConville

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