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J, Volume 8, Issue 1 (March 2025) – 7 articles

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13 pages, 3133 KiB  
Article
Lippia sidoides Cham. Compounds Induce Biochemical Defense Mechanisms Against Curvularia lunata sp. in Maize Plants
by Bruna Leticia Dias, Talita Pereira de Souza Ferreira, Mateus Sunti Dalcin, Dalmarcia de Souza Carlos Mourão, Paulo Ricardo de Sena Fernandes, Taila Renata Neitzke, João Victor de Almeida Oliveira, Tiago Dias, Luis Oswaldo Viteri Jumbo, Eugênio Eduardo de Oliveira and Gil Rodrigues dos Santos
J 2025, 8(1), 7; https://doi.org/10.3390/j8010007 - 17 Feb 2025
Abstract
Corn (Zea mays L.) productivity is often compromised by phytosanitary challenges, with fungal disease like Curvularia leaf spot being particularly significant. While synthetic fungicides are commonly used, there is growing interest in exploring alternative compounds that are effective against pathogens, ensure food [...] Read more.
Corn (Zea mays L.) productivity is often compromised by phytosanitary challenges, with fungal disease like Curvularia leaf spot being particularly significant. While synthetic fungicides are commonly used, there is growing interest in exploring alternative compounds that are effective against pathogens, ensure food safety, and have low toxicity to non-target organisms. In this study, we examined the biochemical changes in corn plants treated with Lippia sidoides essential oil and its major compound, thymol. Both treatments serve as preventive measures for inoculated plants and induced resistance. We tested five concentrations of each product in in vivo experiments. After evaluating the area under the disease progress curve, we analyzed leaf samples for enzymatic activities, including superoxide dismutase, catalase, ascorbate peroxidase, and chitinase. Phytoalexin induction was assessed using soybean cotyledons and sorghum mesocotyls. Cytotoxicity tests revealed lower toxicity at concentrations below 50 µL/mL. Both essential oil and thymol stimulated the production of reactive oxygen species, with thymol primarily activating catalase and L. sidoides oil increasing ascorbate peroxidase levels. Both thymol and L. sidoides were also key activators of chitinase. These findings suggest that L. sidoides essential oil and thymol are promising candidates for developing biological control products to enhance plant defense against pathogens. Full article
(This article belongs to the Special Issue Feature Papers of J—Multidisciplinary Scientific Journal in 2024)
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<p>Area under the disease progress curve (AUDPC) for maize plants treated with <span class="html-italic">Lippia sidoides</span> essential oil and thymol at different concentrations (<b>A</b>), and the AUDPC over the time in the promissory concentration (50 µL/mL) in maize treated plants and untreated plants (<b>B</b>). Each symbol shows the mean (±SD) of three replicates.</p> Full article ">Figure 2
<p>Induction of phytoalexins in soybeans and sorghum treated with <span class="html-italic">Lippia sidoides</span> essential oil and thymol at different concentrations; production of glyceollin in soybean cotyledons (<b>A</b>) and production of 3-deoxyanthocyanin in sorghum mesocotyls (<b>B</b>). Each symbol shows the mean (±SD) of three replicates.</p> Full article ">Figure 3
<p>Enzymatic activity of superoxide dismutase (<b>A</b>), catalase (<b>B</b>), ascorbate peroxidase (<b>C</b>), and chitinase (<b>D</b>) in maize plants treated with essential oil (<span class="html-italic">Lippia sidoides</span>) and thymol. The comparison includes plants inoculated only with the pathogen (<span class="html-italic">Curvularia lunata)</span> or those that received preventive treatments with essential oil (<span class="html-italic">L. sidoides</span> + <span class="html-italic">C. lunata</span>) and thymol (Thymol + <span class="html-italic">C. lunata</span>). Bars represent the mean (±SD) of three replicates. Different letters indicate statistical differences according to Tukey’s test (<span class="html-italic">p</span> &lt; 0.050).</p> Full article ">Figure 4
<p>In vitro analysis of cytotoxicity in peripheral blood mononuclear cells (PBMC) at different concentrations of <span class="html-italic">L. sidoides</span> and thymol. The bars represent the mean (±SD) of five replicates. Connecting lines indicate a statistical difference in Tukey’s test (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">
19 pages, 3933 KiB  
Article
A Fully Coupled Electro-Vibro-Acoustic Benchmark Model for Evaluation of Self-Adaptive Control Strategies
by Thomas Kletschkowski
J 2025, 8(1), 6; https://doi.org/10.3390/j8010006 - 17 Feb 2025
Abstract
The reduction of noise and vibration is possible with passive, semi-active and active control strategies. Especially where self-adaptive control is required, it is necessary to evaluate the noise reduction potential before the control approach is applied to the real-world problem. This evaluation can [...] Read more.
The reduction of noise and vibration is possible with passive, semi-active and active control strategies. Especially where self-adaptive control is required, it is necessary to evaluate the noise reduction potential before the control approach is applied to the real-world problem. This evaluation can be based on a virtual model that contains all relevant sub-systems, transfer paths and coupling effects on the one hand. On the other hand, the complexity of such a model has to be limited to focus on principal findings such as convergence speed, power consumption, and noise reduction potential. The present paper proposes a fully coupled electro-vibro-acoustic model for the evaluation of self-adaptive control strategies. This model consists of discrete electrical and mechanical networks that are applied to model the electro-acoustic behavior of noise and anti-noise sources. The acoustic field inside a duct, terminated by these electro-acoustic sources, is described by finite elements. The resulting multi-physical model is capable of describing all relevant coupling effects and enables an efficient evaluation of different control strategies such as the local control of sound pressure or active control of acoustic absorption. It is designed as a benchmark model for the benefit of the scientific community. Full article
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<p>Topological model of system (top) and electro-vibro-acoustical model (bottom).</p> Full article ">Figure 2
<p>Resonance frequencies of the uncontrolled system.</p> Full article ">Figure 3
<p>Normalized mode shapes in resonance.</p> Full article ">Figure 4
<p>System input and system output without self-adaptive control.</p> Full article ">Figure 5
<p>IR and resonance frequencies of the uncontrolled system.</p> Full article ">Figure 6
<p>Modelling of system response without active control.</p> Full article ">Figure 7
<p>Active control of local sound pressure—time-history of simulation.</p> Full article ">Figure 8
<p>Frequency domain illustration of active control of local sound pressure.</p> Full article ">Figure 9
<p>Active control of local absorption—time-history of simulation.</p> Full article ">Figure 10
<p>Frequency domain illustration of active control of local absorption.</p> Full article ">
18 pages, 12596 KiB  
Article
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
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, [...] Read more.
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
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<p>The overview of this study for exploring the relationship between muscle activation and deformation. Several participants were invited to join the arm movement experiment and the recorded sEMG and A-mode ultrasound signals were collected. The joint angles were also recorded via an additional stereo camera (not displayed here). The arm motions were divided into two phases (lifting and putting down the arm), and the models were trained to classify motion phases from the separate recorded signals. The correlation between the two classifications was analyzed.</p> Full article ">Figure 2
<p>Three devices used in this study and their typical signals. The sEMG recorded muscle activation patterns. The A-mode ultrasound recorded the bone movements, which later were transformed to muscle deformation. The stereo camera recorded the RGBD images first (the image had been blurred and processed for privacy issues), then recognized the 3D joint movements through an algorithm, and calculated the joint angle from the joint positions in the end.</p> Full article ">Figure 3
<p>The preparation of each participant’s experiment. Six different participants (<b>A</b>–<b>F</b>) were invited to wear the sEMG device and A-mode ultrasound. The positions of the devices were first put on the approximate positions, then were adjusted to check the clear pattern locations. The right figure demonstrates how we validated the bone peak positions in A-mode signals, using the example from Participant B and Participant C.</p> Full article ">Figure 4
<p>The experiment’s setup and the specified movement of participants. In the left figure, the relative positions between the three devices are demonstrated, together with the participant’s position. In the right figure, the participant’s specified movement and the motion phases are demonstrated.</p> Full article ">Figure 5
<p>The proposed attention UNet for predicting the arm movement phase using ultrasound signals. The input is the recorded bio-signals, while the output from the encoder is the classification of movement transitions, and the final output is the motion types throughout the full sequence (red and green represent the two motion phases). The attention module has a horizontal cylindrical shape and is demonstrated on the right in detail.</p> Full article ">Figure 6
<p>This shows the classification results from the two models of participant A. A random segment of the motion cycles was selected from the test dataset, which contains several motion cycles (due to the repeatable motion). The solid curves represent the prediction results from the ultrasound model, while the dotted curves represent the results from the EMG model. The red color represents the class of lowering the arm, while the blue color represents the class of lifting the arm. The gray vertical dashed lines represent the prediction positions from the ultrasound model.</p> Full article ">Figure 7
<p>The contingency tables compare the full sequence classification only between the ultrasound and sEMG signals, regardless of the ground truth labels. The results of the six participants (<b>A</b>–<b>F</b>) are arranged in a zigzag pattern from top left to the bottom right. In each table, the row represents the classification of sEMG model (0 means putting down the arm, 1 means lifting up the arm), and the columns indicate the decisions from the ultrasound model. The number in each cell of the table denotes the counts of samples falling into this category (the ultrasound and sEMG had the same or different classification decisions). Note that these tables do not include any comparison with the ground truth labels. Thus, they can visually represent the relationships between the decisions from the two models.</p> Full article ">Figure 8
<p>The histograms show the distribution of the absolute shift errors between the transition locations of models’ classifications (determined by the post-processing from the full sequence classification results) and the transition locations from the ground truth labels, expressed as a percentage of the full motion cycle duration (e.g., 0.2 on the x-axis indicates an error of 20% of the full cycle duration). The results of participants <b>A</b> to <b>F</b> are arranged from top left to bottom right in a zigzag pattern. The lines connect the peak values of the bars within each distribution slot. The brown color represents the overlapping areas between the results of ultrasound and EMG models. The post-processing steps to obtain the transition locations from the models are described in <a href="#sec3dot2-J-08-00005" class="html-sec">Section 3.2</a>.</p> Full article ">Figure 9
<p>The histogram shows the transition locations of the two models’ classifications (determined by the post-processing from the full sequence classification results), regardless of ground truth labels, and expressed as a percentage over the single motion cycle (e.g., 0.2 on the x-axis indicates the 20% position of a full cycle duration). The participants <b>A</b> to <b>F</b> are displayed in a zigzag order from top left to bottom right. The lines connect the peak values of the bars within each distribution slot, illustrating the slot distribution from each participant. The post-processing steps to obtain the transition locations from the models are described in <a href="#sec3dot2-J-08-00005" class="html-sec">Section 3.2</a>.</p> Full article ">
24 pages, 13159 KiB  
Article
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 606
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 [...] Read more.
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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<p>The proposed architecture of the plant disease detection system, illustrating the modular design with four key components: data preprocessing, model architecture, training, and evaluation modules.</p> Full article ">Figure 2
<p>Class distribution of PlantDoc dataset.</p> Full article ">Figure 3
<p>PlantDoc apple leaf class.</p> Full article ">Figure 4
<p>Class distribution of web-sourced data (train).</p> Full article ">Figure 5
<p>Class distribution of web-sourced dataset (test).</p> Full article ">Figure 6
<p>Class distribution of combined dataset (train).</p> Full article ">Figure 7
<p>Class distribution of combined dataset (test).</p> Full article ">Figure 8
<p>Apple leaf rust (combined dataset).</p> Full article ">Figure 9
<p>Pipeline illustrating the image processing techniques, data augmentation, model training, testing, and evaluation steps.</p> Full article ">Figure 10
<p>Confusion matrix for EfficientNet-B0 (PlantDoc).</p> Full article ">Figure 11
<p>Confusion matrix for PlantDoc → web-sourced dataset.</p> Full article ">Figure 12
<p>Confusion matrix (trained on combined, tested on web-sourced).</p> Full article ">Figure 13
<p>Confusion matrix, web-sourced data.</p> Full article ">
19 pages, 2847 KiB  
Article
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 483
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, [...] Read more.
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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<p>The flow chart illustrates the steps in analyzing RNA, DNA, and proteins obtained from a single sample using a low-cost tissue crusher (in-house prototype) and TRIzol reagent.</p> Full article ">Figure 2
<p>Prototype and overview of TRIzol sample processing. (<b>A</b>) Photos of the tissue crusher, denominated as the prototype. Some components and details appear in zoom images. (<b>B</b>) Overview of the tissue disruption using the mixed-type tissue crusher combining blade and beating principles and the TRIzol strategy. The illustration shows the coupling of nylon in the grooves of the stainless steel rod, the steel beads, and the coupling of the stainless steel rod in the quick-change chuck from a drill motor.</p> Full article ">Figure 3
<p>RNA bands, amplification plots, and melting curves. Agarose gel electrophoresis of 28S and 18S ribosomal RNA (rRNA) bands from RNA samples extracted using the TRIzol method for HEK293-ABCG2 and H460 cells (<b>A</b>) and fragment of breast tissues (<b>D</b>). Amplification plots (relative fluorescence of SYBR green versus cycle number) represent the accumulation of product over the duration of the real-time PCR experiment. The horizontal line indicates the threshold placement. The targets are indicated by color in the figures, in which the same color represents a single gene. Different samples were analyzed, including cells (<b>B</b>), breast tissues (<b>E</b>), and breast or stomach tissues (<b>G</b>). A melting curve (60–95 °C; in increments of 0.5 °C) was generated to verify the specificity of primer amplification. Melting curves from different samples were analyzed, including cells (<b>C</b>), breast tissue (<b>F</b>), and breast or stomach tissues (<b>H</b>).</p> Full article ">Figure 4
<p>Allelic discrimination plot. Genotyping of rs767455 was performed using the TaqMan™ SNP genotyping method on a 7500 Fast Real-time PCR. The assay mix include unlabeled PCR primers and FAM™- and VIC<sup>®</sup> dye-labeled probes. Each dot corresponds to a different sample. Non-circled samples correspond to the DNA from human blood cells representing the genotypic controls to alleles CC (blue), TC (green), and TT (red). Circled samples correspond to five samples of human breast tissues (turquoise, AC297, AC305, AC188, AC270, and AC276). DNA was obtained using the TRIzol-based method.</p> Full article ">Figure 5
<p>SDS-PAGE and Western blot. (<b>A</b>) Comparison of protein recovery from HEK293-ABCG2 cells and tissue cancer samples (AC148, AC42, and AC297). Proteins were extracted with TRIzol, resuspended in 1% SDS and 8 M urea in Tris–HCl, pH 8.0, and then solubilized using sonication. Protein concentration was determined by BCA and gels were Coomassie Blue-stained. (<b>B</b>) Representative Western blot analysis of total ABCG2 and GAPDH from HEK293-ABCG2 cells. The experiments were repeated at least three times, and the most representative image is shown. Total protein was loaded on 8% SDS-PAGE. PVDF membranes were incubated with an antibody against ABCG2 or GAPDH.</p> Full article ">Figure 6
<p>Integrative review of experimental studies on human tumor tissue extracted by a TRIzol-based method and reported yield of each macromolecule. (<b>A</b>) The studies were searched in the PubMed and Web of Science databases with a search strategy with the words TRIzol reagent (and analogs), cancer, RNA, DNA, protein, human, and tissue (<span class="html-italic">n</span> = 45). Based on the search findings (<b>A</b>), the arrow indicates (<b>B</b>) the number of studies reporting the number of macromolecules yield was assessed (<span class="html-italic">n</span> = 7).</p> Full article ">
18 pages, 11896 KiB  
Article
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
Cited by 1 | Viewed by 556
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 [...] Read more.
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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<p>Actual swimming of the robot: (<b>a</b>) initial state; (<b>b</b>) the robot in motion.</p> Full article ">Figure 2
<p>Structure and main dimensions of the robot.</p> Full article ">Figure 3
<p>Two-dimensional view and arrangement of the contraction actuators. The gray lines represent the axes in the initial state.</p> Full article ">Figure 4
<p>Simulation of the model, representing lateral fluid velocity. The green elements represent the robot body (solid elements within the FSI analysis).</p> Full article ">Figure 5
<p>Simulation results with the linear mode. (<b>a</b>) The DMD prediction and original data; (<b>b</b>) the error values.</p> Full article ">Figure 6
<p>Simulation results with the SINDy model. (<b>a</b>) The SINDy prediction and original data, <math display="inline"><semantics> <mrow> <msup> <mi>X</mi> <mo>+</mo> </msup> </mrow> </semantics></math>; (<b>b</b>) the error values.</p> Full article ">Figure 7
<p>Simulation results with the AE NN nonlinear model. (<b>a</b>) The AE NN prediction and original data; (<b>b</b>) the error values.</p> Full article ">Figure 8
<p>Comparison of the prediction errors (red lines) within extrapolation: (<b>a</b>) The DMD prediction; (<b>b</b>) the SINDy model results; (<b>c</b>) the AE NN. Cyan bands approximate bands of DMD error, drawn for visual comparison.</p> Full article ">Figure 9
<p>Comparison of the measured values with the SINDy-DMD one-snapshot-forward prediction model: (<b>a</b>) The prediction and experimental data, <math display="inline"><semantics> <mi>Y</mi> </semantics></math>; (<b>b</b>) the prediction error.</p> Full article ">Figure 10
<p>Visualized data matrices: (<b>a</b>) the original data, <math display="inline"><semantics> <mrow> <msup> <mi>V</mi> <mi>T</mi> </msup> </mrow> </semantics></math>; (<b>b</b>) the SINDy-DMD reconstruction.</p> Full article ">Figure 11
<p>Data reconstruction within the extrapolation range: (<b>a</b>) the original data; (<b>b</b>) the SINDy-DMD recovered data; (<b>c</b>) the error (elementwise subtraction).</p> Full article ">Figure 12
<p>Time-evolution of the robot–environment states, <math display="inline"><semantics> <mover accent="true"> <mi>v</mi> <mo>→</mo> </mover> </semantics></math>, from the data matrix <math display="inline"><semantics> <mi>D</mi> </semantics></math> (SVD-truncated) in one period. The numbers, <span class="html-italic">n</span>, refer to the time steps, <span class="html-italic">nT/10</span>, where <span class="html-italic">T</span> is the period.</p> Full article ">Figure 13
<p>Corresponding reconstructed robot–environment states obtained by the SINDy-DMD.</p> Full article ">
9 pages, 3858 KiB  
Opinion
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 603
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 [...] Read more.
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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<p>(<b>a</b>) NFTs represent unique items with descriptive metadata, such as a rock sample, unlike fungible tokens like cryptocurrencies. (<b>b</b>) NFTs are non-interchangeable, while fungible tokens are exchangeable on a one-to-one basis. (<b>c</b>) Both can be traded using similar mechanisms. (adapted and modified concept from [<a href="#B8-J-08-00001" class="html-bibr">8</a>]).</p> Full article ">Figure 2
<p>Workflow for NFTs in Geological Data: (<b>a</b>) A researcher creates two NFT representations of a rock sample, with ownership identified as 0xx28. (<b>b</b>) Access to the NFTs is granted only when permission is provided by the researcher. (<b>c</b>) The blockchain authorizes decentralized application (DApp) participants to securely and transparently process the data.</p> Full article ">Figure 3
<p>Volcanoland NFTs Museum: One of the first interactive 3D displays of volcanoes, featuring detailed models of volcanoes.</p> Full article ">Figure 4
<p>Flow diagram of NFTs application in research funding, adapted and modified from governance process voting in blockchain [<a href="#B16-J-08-00001" class="html-bibr">16</a>].</p> Full article ">
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