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Correction: Subcellular and macrostructural immediate responders to airblast traumatic brain injury
Alkali Cations Promote CO <sub>2</sub> Electroreduction on Cu(100) Surfaces under Acidic Conditions by Suppressing Surface Hydrogen Passivation: A Multiscale Modeling Perspective
Macro- and micro-scale mechanical properties and energy evolution analysis during unidirectional freeze–thaw cycling of sandstone
Total Synthesis of (+)-Niduenes A and B
Nickel-incorporated MoS₂ nanoflowers as efficient bifunctional electrocatalysts for hydrogen and oxygen evolution reactions in alkaline media
Tricomponent α-Carbonyl Alkylation by a Cobalt-Catalyzed Polar–Radical and Radical–Polar Crossover Cascade
Integrated multi-omics analysis reveals apple mango leaf extract-induced dendritic cell maturation associated with Il1b upregulation and PU.1/ETS motif enrichment
Four-pixel sum-preserving encryption using the chaotic map for high thumbnail-preserving image security
Multidimensional Design of Backbones, Modulators, and Pore Spaces in Benzotrithiophene-Linked Metal–Organic Frameworks for Li–S Batteries
Hybrid deep learning with attention mechanism for monitoring and classifying physical exercise postures using sensor data
Abstract Accurate detection and classification of different physical exercise postures play a crucial role in monitoring fitness levels, preventing injuries, and personalizing workout routines. Traditional approaches using handcrafted feature extraction and shallow classifiers often suffer from low generalization and limited scalability. To address these limitations, this paper explores advanced deep learning models such as convolutional neural networks (CNN), recurrent neural networks (RNN), capsule networks (CapsNet), gated recurrent units (GRU), and multilayer perceptron (MLP), along with hybrid architectures, to accurately classify exercise quality categories. The models were trained on sensor data collected from smart devices, capturing motion dynamics and postural changes. Among the evaluated models, Capsule Network achieved the highest accuracy of 0.99, followed by hybrid MLP with CNN and transformer model with 0.97, demonstrating superior capability in recognizing complex activity patterns. The results show that deep learning models can effectively identify different exercise postures with high precision and recall, paving the way for intelligent fitness monitoring systems. Future work includes optimizing the models for real-time applications and extending the system to include a wider range of physical activities.
The forest of knowledge under global change
Abstract Amazonia harbours more than 10% of the terrestrial biodiversity of the Earth 1 and more than 400 Indigenous groups 2 . So far, however, no study has assessed how climate change and the loss of Indigenous languages may simultaneously impact its biological and cultural heritage. Here, to bridge this gap, we first assembled a database of 90,536 reports from 700 references to understand the societal benefits that native plants provide across all countries of the Amazon basin. We found that humans utilize 5,796 native plant species, which amounts to one-third of the known Amazon vascular seed plant flora. Next, analysing 8,429 species distribution models across three future climate scenarios (SSP1–2.6, SSP3–7.0 and SSP5–8.5), we show that climate change will produce a greater reduction in the ranges of utilized than of non-utilized species by 2060–2080. Locally, Indigenous cultures may lose an average of 28–34% of their utilized plant species and 18–23% of their associated services from climate change. Regionally, the loss of threatened Indigenous languages may result in a 26% reduction in the Amazonian knowledge pool. Overall, our results point to the strong climate and language vulnerability of Amazonian biocultural heritage. At the same time, these results—together with our publicly available dataset—may serve to guide biocultural restoration and reverse the growing global change effects on ecosystems and cultural traditions.