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J, Volume 8, Issue 1 (March 2025) – 5 articles

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18 pages, 12590 KiB

Open AccessArticle

Muscle Activation–Deformation Correlation in Dynamic Arm Movements

by Bangyu Lan and Kenan Niu

J 2025, 8(1), 5; https://doi.org/10.3390/j8010005 - 1 Feb 2025

Viewed by 272

Abstract

Understanding the relationship between muscle activation and deformation is essential for analyzing arm movement dynamics in both daily activities and clinical settings. Accurate characterization of this relationship impacts rehabilitation strategies, prosthetic development, and athletic training by providing deeper insights into muscle functions. However, direct analysis of raw neuromuscular and biomechanical signals remains limited due to their complex interplay. Traditional research implicitly applied this relationship without exploring the intricacies of the muscle behavior. In contrast, in this study, we explored the relationship between neuromuscular and biomechanical signals via a motion classification task based on a proposed deep learning approach, which was designed to classify arm motions separately using muscle activation patterns from surface electromyography (sEMG) and muscle thickness deformation measured by A-mode ultrasound. The classification results were directly compared through the chi-square analysis. In our experiment, six participants performed a specified arm lifting motion, creating a general motion dataset for the study. Our findings investigated the correlation between muscle activation and deformation patterns, offering special insights into muscle contraction dynamics, and potentially enhancing applications in rehabilitation and prosthetics in the future. Full article

24 pages, 13159 KiB

Open AccessArticle

Plant Leaf Disease Detection Using Deep Learning: A Multi-Dataset Approach

by Manjunatha Shettigere Krishna, Pedro Machado, Richard I. Otuka, Salisu W. Yahaya, Filipe Neves dos Santos and Isibor Kennedy Ihianle

J 2025, 8(1), 4; https://doi.org/10.3390/j8010004 - 15 Jan 2025

Viewed by 789

Abstract

Agricultural productivity is increasingly threatened by plant diseases, which can spread rapidly and lead to significant crop losses if not identified early. Detecting plant diseases accurately in diverse and uncontrolled environments remains challenging, as most current detection methods rely heavily on lab-captured images that may not generalise well to real-world settings. This paper aims to develop models capable of accurately identifying plant diseases across diverse conditions, overcoming the limitations of existing methods. A combined dataset was utilised, incorporating the PlantDoc dataset with web-sourced images of plants from online platforms. State-of-the-art convolutional neural network (CNN) architectures, including EfficientNet-B0, EfficientNet-B3, ResNet50, and DenseNet201, were employed and fine-tuned for plant leaf disease classification. A key contribution of this work is the application of enhanced data augmentation techniques, such as adding Gaussian noise, to improve model generalisation. The results demonstrated varied performance across the datasets. When trained and tested on the PlantDoc dataset, EfficientNet-B3 achieved an accuracy of 73.31%. In cross-dataset evaluation, where the model was trained on PlantDoc and tested on a web-sourced dataset, EfficientNet-B3 reached 76.77% accuracy. The best performance was achieved with the combination of the PlanDoc and web-sourced datasets resulting in an accuracy of 80.19% indicating very good generalisation in diverse conditions. Class-wise F1-scores consistently exceeded 90% for diseases such as apple rust leaf and grape leaf across all models, demonstrating the effectiveness of this approach for plant disease detection. Full article

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19 pages, 2847 KiB

Open AccessArticle

Effective Mixed-Type Tissue Crusher and Simultaneous Isolation of RNA, DNA, and Protein from Solid Tissues Using a TRIzol-Based Method

by Kelly Karoline dos Santos, Isabelle Watanabe Daniel, Letícia Carani Delabio, Manoella Abrão da Costa, Júlia de Paula Dutra, Bruna Estelita Ruginsk, Jeanine Marie Nardin, Louryana Padilha Campos, Fabiane Gomes de Moraes Rego, Geraldo Picheth, Glaucio Valdameri and Vivian Rotuno Moure

J 2025, 8(1), 3; https://doi.org/10.3390/j8010003 - 13 Jan 2025

Viewed by 516

Abstract

One of the major challenges of studying biomarkers in tumor samples is the low quantity and quality of isolated RNA, DNA, and proteins. Additionally, the extraction methods ideally should obtain macromolecules from the same tumor biopsy, allowing better-integrated data interpretation. In this work, an in-house, low-cost, mixed-type tissue crusher combining blade and beating principles was made and the simultaneous isolation of macromolecules from human cells and tissues was achieved using TRIzol. RT-qPCR, genotyping, SDS-PAGE, and Western blot analysis were used to validate the approach. For tissue samples, RNA, DNA, and proteins resulted in an average yield of 677 ng/mg, 225 ng/mg, and 1.4 µg/mg, respectively. The same approach was validated using cell lines. The isolated macromolecule validation included the detection of mRNA levels of ATP-binding cassette (ABC) transporters through RT-qPCR, genotyping of TNFR1 (rs767455), and protein visualization through SDS-PAGE following Coomassie blue staining and Western blot. This work contributed to filling a gap in knowledge about TRIzol efficiency for the simultaneous extraction of RNA, DNA, and proteins from a single human tissue sample. A low-cost, high yield, and quality method was validated using target biomarkers of multidrug resistance mechanisms. This approach might be advantageous for future biomarker studies using different tissue specimens. Full article

(This article belongs to the Section Biology & Life Sciences)

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18 pages, 11896 KiB

Open AccessArticle

Temporal Evolution of the Hydrodynamics of a Swimming Eel Robot Using Sparse Identification: SINDy-DMD

by Mostafa Sayahkarajy and Hartmut Witte

J 2025, 8(1), 2; https://doi.org/10.3390/j8010002 - 12 Jan 2025

Viewed by 612

Abstract

Anguilliform swimming is one of the most complex locomotion modes, involving various interacting phenomena, necessitating multidisciplinary studies. Eel robots are designed to incorporate biological principles and achieve efficient locomotion by replicating natural anguilliform swimming. These robots are simpler to engineer and study compared to their natural counterparts. Nevertheless, characterizing the robot–environment interaction is complex, demanding computationally expensive fluid dynamics simulations. In this study, we employ machine learning strategies to investigate the temporal evolution of the system and discover a data-driven model. Three methods were studied, including dynamic mode decomposition (DMD), sparse system identification (SINDy using PySINDy package), and autoencoder neural network (AE NN), as a general function approximator. The models were simulated using MATLAB^® R2022 to obtain the prediction errors. The results show that the SINDy model presents less error within the regression range and performs better in extrapolation. Additionally, the SINDy model has a compact form and can explicitly formulate the coupling phenomena amongst the modes. Thus, instead of the standard DMD, we propose the SINDy-DMD approach to describe the anguilliform locomotion of the soft robot. The identified model was employed to recover the system state data matrix. It is concluded that the proposed model with quadratic terms provides a parsimonious representation of the system dynamics. Full article

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9 pages, 3858 KiB

Open AccessOpinion

Use Case of Non-Fungible Tokens (NFTs): A Blockchain Approach for Geological Data Dissemination

by Muhammad Aufaristama

J 2025, 8(1), 1; https://doi.org/10.3390/j8010001 - 7 Jan 2025

Viewed by 639

Abstract

The application of blockchain technology and Non-Fungible Tokens (NFTs) into geology offers potential for the preservation, management, and dissemination of geological data. This perspective paper explores the feasibility, benefits, and challenges of utilizing NFTs in managing geological data, particularly focusing on geology research materials. NFTs provide immutable, decentralized records that enhance data integrity, accessibility, and provenance, addressing long-standing issues in geological data management. This study outlines the key advantages of NFTs, including immutable record-keeping, enhanced accessibility, clear provenance and ownership, and interoperability across platforms. Specific use cases are highlighted, such as the creation of digital specimen collections, the development of interactive educational resources such as museums, and novel funding mechanisms for research. While the potential applications are promising, the discussion also addresses current limitations, including technical complexity, environmental concerns, and regulatory uncertainties. The opinion concludes with prospects, emphasizing the need for further research and technological advancements to fully realize the benefits of NFTs in geological data management, potentially revolutionizing the field of geology by making data more accessible, reliable, and secure. Full article

(This article belongs to the Section Earth Sciences)

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