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28 pages, 57356 KiB  
Article
Contrast-Invariant Edge Detection: A Methodological Advance in Medical Image Analysis
by Dang Li, Patrick Cheong-Iao Pang and Chi-Kin Lam
Appl. Sci. 2025, 15(2), 963; https://doi.org/10.3390/app15020963 (registering DOI) - 19 Jan 2025
Abstract
Edge detection methods are significant in medical imaging-assisted diagnosis. However, existing methods based on grayscale gradient computation still need to be optimized in practicality, especially in terms of actual visual quality and sensitivity to image contrast. To optimize the visualization and enhance the [...] Read more.
Edge detection methods are significant in medical imaging-assisted diagnosis. However, existing methods based on grayscale gradient computation still need to be optimized in practicality, especially in terms of actual visual quality and sensitivity to image contrast. To optimize the visualization and enhance the robustness of contrast changes, we propose the Contrast Invariant Edge Detection (CIED) method. CIED combines Gaussian filtering and morphological processing methods to preprocess medical images. It utilizes the three Most Significant Bit (MSB) planes and binary images to detect and extract significant edge information. Each bit plane is used to detect edges in 3 × 3 blocks by the proposed algorithm, and then the edge information from each plane is fused to obtain an edge image. This method is generalized to common types of images. Since CIED is based on binary bit planes and eliminates complex pixel operations, it is faster and more efficient. In addition, CIED is insensitive to changes in image contrast, making it more flexible in its application. To comprehensively evaluate the performance of CIED, we develop a medical image dataset and conduct edge image and contrast evaluation experiments based on these images. The results show that the average precision of CIED is 0.408, the average recall is 0.917, and the average F1-score is 0.550. The results indicate that CIED is not only more practical in terms of visual effects but also robust in terms of contrast invariance. The comparison results with other methods also confirm the advantages of CIED. This study provides a novel approach for edge detection within medical images. Full article
22 pages, 4925 KiB  
Article
Nonlinear Dynamic Response Analysis of Cable–Buoy Structure Under Marine Environment
by Qiufu Xie, Binghan Liu, Junxian Zhang and Yaobing Zhao
J. Mar. Sci. Eng. 2025, 13(1), 176; https://doi.org/10.3390/jmse13010176 (registering DOI) - 19 Jan 2025
Abstract
The nonlinear dynamics of the cable–buoy structure in marine engineering present significant analytical challenges due to the complex motion of the buoy, which impacts the system’s dynamic response. The drag force acting on the structure can be categorized into the absolute velocity and [...] Read more.
The nonlinear dynamics of the cable–buoy structure in marine engineering present significant analytical challenges due to the complex motion of the buoy, which impacts the system’s dynamic response. The drag force acting on the structure can be categorized into the absolute velocity and relative velocity models, distinguished by their reference frames. The absolute velocity model incorporates flow velocity coupling terms, offering higher accuracy but at the expense of increased computational complexity. In contrast, the relative velocity model is computationally simpler and therefore more widely adopted. Nevertheless, the accuracy and applicability of these simplified models remain open to further in-depth investigation. To address these challenges, this study derives coupled differential equations for the cable–buoy structure based on the two drag force models. Galerkin discretization is then employed to construct coupled systems that account for nonlinear buoy motion, as well as decoupled systems assuming linear buoy motion. The modulation equations for the system’s primary resonance response are derived using the method of multiple scales. Numerical results indicate that changes in cable parameters lead to complex modal coupling behaviors in the system. The flow velocity coupling terms in the absolute velocity drag force model enhance the system’s damping effect, and the relative velocity drag force model, which omits these coupling terms, results in increased system response amplitudes. Although neglecting nonlinear buoy motion has little impact on the cable’s dynamic response, it significantly reduces the amplitude of the buoy’s dynamic motion. The relative velocity drag force model and the decoupled system can serve as effective simplifications for analyzing the dynamic responses of cable–buoy systems, providing a balance between computational efficiency and result accuracy. Variations in system parameters cause both qualitative and quantitative changes in the system’s nonlinear stiffness characteristics. Full article
(This article belongs to the Section Ocean Engineering)
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20 pages, 10254 KiB  
Article
Discernible Orientation for Tortuosity During Oxidative Precipitation of Fe(II) in Porous Media: Laboratory Experiment and Micro-CT Imaging
by Wenran Cao, Ekaterina Strounina, Harald Hofmann and Alexander Scheuermann
Minerals 2025, 15(1), 91; https://doi.org/10.3390/min15010091 (registering DOI) - 19 Jan 2025
Abstract
In the mixing zone, where submarine groundwater carrying ferrous iron [Fe(II)] meets seawater with dissolved oxygen (DO), the oxidative precipitation of Fe(II) occurs at the pore scale (nm~μm), and the resulting Fe precipitation significantly influences the seepage properties at the Darcy scale (cm~m). [...] Read more.
In the mixing zone, where submarine groundwater carrying ferrous iron [Fe(II)] meets seawater with dissolved oxygen (DO), the oxidative precipitation of Fe(II) occurs at the pore scale (nm~μm), and the resulting Fe precipitation significantly influences the seepage properties at the Darcy scale (cm~m). Previous studies have presented a challenge in upscaling fluid dynamics from a small scale to a large scale, thereby constraining our understanding of the spatiotemporal variations in flow paths as porous media evolve. To address this limitation, this study simulated subsurface mixing by injecting Fe(II)-rich freshwater into a DO-rich saltwater flow within a custom-designed syringe packed with glass beads. Micro-computed tomography imaging at the representative elementary volume scale was utilized to track the development of Fe precipitates over time and space. Experimental observations revealed three distinct stages of Fe hydroxides and their effects on the flow dynamics. Initially, hydrous Fe precipitates were characterized by a low density and exhibited mobility, allowing temporarily clogged pathways to intermittently reopen. As precipitation progressed, the Fe precipitates accumulated, forming interparticle bonding structures that redirected the flow to bypass clogged pores and facilitated precipitate flushing near the syringe wall. In the final stage, a notable reduction in the macroscopic capillary number from 3.0 to 0.05 indicated a transition from a viscous- to capillary-dominated flow, which led to the construction of ramified, tortuous flow channels. This study highlights the critical role of high-resolution imaging techniques in bridging the gap between pore-scale and continuum-scale analyses of multiphase flows in hydrogeochemical processes, offering valuable insights into the complex groundwater–seawater mixing. Full article
(This article belongs to the Special Issue Mineral Dissolution and Precipitation in Geologic Porous Media)
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39 pages, 2913 KiB  
Review
Multi-UAV Task Assignment in Dynamic Environments: Current Trends and Future Directions
by Shahad Alqefari and Mohamed El Bachir Menai
Drones 2025, 9(1), 75; https://doi.org/10.3390/drones9010075 (registering DOI) - 19 Jan 2025
Abstract
The rapid advancement of unmanned aerial vehicles (UAVs) has transformed a wide range of applications, including military operations, disaster response, agricultural monitoring, and infrastructure inspection. Deploying multiple UAVs to work collaboratively offers significant advantages in terms of enhanced coverage, redundancy, and operational efficiency. [...] Read more.
The rapid advancement of unmanned aerial vehicles (UAVs) has transformed a wide range of applications, including military operations, disaster response, agricultural monitoring, and infrastructure inspection. Deploying multiple UAVs to work collaboratively offers significant advantages in terms of enhanced coverage, redundancy, and operational efficiency. However, as UAV missions become more complex and operate in dynamic environments, the task assignment problem becomes increasingly challenging. Multi-UAV dynamic task assignment is critical for optimizing mission success. It involves allocating tasks to UAVs in real-time while adapting to unpredictable changes, such as sudden task appearances, UAV failures, and varying mission requirements. A key contribution of this article is that it provides a comprehensive study of state-of-the-art solutions for dynamic task assignment in multi-UAV systems from 2013 to 2024. It also introduces a comparative framework to evaluate algorithms based on metrics such as responsiveness, robustness, and scalability in handling real-world dynamic conditions. Our analysis reveals distinct strengths and limitations across three major approaches: market-based, intelligent optimization, and clustering-based solutions. Market-based solutions excel in distributed coordination and real-time adaptability, but face challenges with communication overhead. Intelligent optimization solutions, including evolutionary and swarm intelligence, provide high flexibility and performance in complex scenarios but require significant computational resources. Clustering-based solutions efficiently group and allocate tasks geographically, reducing overlap and improving efficiency, although they struggle with adaptability in dynamic environments. By identifying these strengths, limitations, and emerging trends, this article not only offers a detailed comparative analysis but also highlights critical research gaps. Specifically, it underscores the need for scalable algorithms that can efficiently handle larger UAV fleets, robust methods to adapt to sudden task changes and UAV failures, and multi-objective optimization frameworks to balance competing goals such as energy efficiency and task completion. These insights serve as a guide for future research and a valuable resource for developing resilient and efficient strategies for multi-UAV dynamic task assignment in complex environments. Full article
55 pages, 18951 KiB  
Article
Structured Dynamics in the Algorithmic Agent
by Giulio Ruffini, Francesca Castaldo and Jakub Vohryzek
Entropy 2025, 27(1), 90; https://doi.org/10.3390/e27010090 (registering DOI) - 19 Jan 2025
Abstract
In the Kolmogorov Theory of Consciousness, algorithmic agents utilize inferred compressive models to track coarse-grained data produced by simplified world models, capturing regularities that structure subjective experience and guide action planning. Here, we study the dynamical aspects of this framework by examining how [...] Read more.
In the Kolmogorov Theory of Consciousness, algorithmic agents utilize inferred compressive models to track coarse-grained data produced by simplified world models, capturing regularities that structure subjective experience and guide action planning. Here, we study the dynamical aspects of this framework by examining how the requirement of tracking natural data drives the structural and dynamical properties of the agent. We first formalize the notion of a generative model using the language of symmetry from group theory, specifically employing Lie pseudogroups to describe the continuous transformations that characterize invariance in natural data. Then, adopting a generic neural network as a proxy for the agent dynamical system and drawing parallels to Noether’s theorem in physics, we demonstrate that data tracking forces the agent to mirror the symmetry properties of the generative world model. This dual constraint on the agent’s constitutive parameters and dynamical repertoire enforces a hierarchical organization consistent with the manifold hypothesis in the neural network. Our findings bridge perspectives from algorithmic information theory (Kolmogorov complexity, compressive modeling), symmetry (group theory), and dynamics (conservation laws, reduced manifolds), offering insights into the neural correlates of agenthood and structured experience in natural systems, as well as the design of artificial intelligence and computational models of the brain. Full article
14 pages, 2795 KiB  
Article
Research on Fire Detection of Cotton Picker Based on Improved Algorithm
by Zhai Shi, Fangwei Wu, Changjie Han and Dongdong Song
Sensors 2025, 25(2), 564; https://doi.org/10.3390/s25020564 (registering DOI) - 19 Jan 2025
Viewed by 44
Abstract
According to the physical characteristics of cotton and the work characteristics of cotton pickers in the field, during the picking process, there is a risk of cotton combustion. The cotton picker working environment is complex, cotton ignition can be hidden, and fire is [...] Read more.
According to the physical characteristics of cotton and the work characteristics of cotton pickers in the field, during the picking process, there is a risk of cotton combustion. The cotton picker working environment is complex, cotton ignition can be hidden, and fire is difficult to detect. Therefore, in this study, we designed an improved algorithm for multi-sensor data fusion; built a cotton picker fire detection system by using infrared temperature sensors, CO sensors, and the upper computer; and proposed a BP neural network model based on improved mutation operator hybrid gray wolf optimizer and particle swarm optimization (MGWO-PSO) algorithm based on the BP neural network model. This algorithm includes the introduction of a mutation operator in the gray wolf algorithm to improve the search ability of the algorithm, and, at the same time, we introduce the PSO algorithm idea. The improved fusion algorithm is used as a learning algorithm to optimize the BP neural network, and the optimized network is used to process and predict the data collected from temperature and gas sensors, which effectively improves the accuracy of fire prediction. The sensor measurements were compared with the actual values to verify the effectiveness of the GWO-PSO-optimized BP neural network model. Once experimentally verified, the improved GWO-PSO algorithm achieves a correlation coefficient R of 0.96929, a prediction accuracy rate of 96.10%, and a prediction error rate of only 3.9%, while the system monitors an accurate early warning rate of 96.07%, and the false alarm and omission rates are both less than 5%. This study can detect cotton picker fires in real time and provide timely warnings, which provides a new method for the accurate detection of fires during the field operation of cotton pickers. Full article
(This article belongs to the Section Smart Agriculture)
20 pages, 7483 KiB  
Article
An Enhanced LiDAR-Based SLAM Framework: Improving NDT Odometry with Efficient Feature Extraction and Loop Closure Detection
by Yan Ren, Zhendong Shen, Wanquan Liu and Xinyu Chen
Processes 2025, 13(1), 272; https://doi.org/10.3390/pr13010272 (registering DOI) - 19 Jan 2025
Viewed by 300
Abstract
Simultaneous localization and mapping (SLAM) is crucial for autonomous driving, drone navigation, and robot localization, relying on efficient point cloud registration and loop closure detection. Traditional Normal Distributions Transform (NDT) odometry frameworks provide robust solutions but struggle with real-time performance due to the [...] Read more.
Simultaneous localization and mapping (SLAM) is crucial for autonomous driving, drone navigation, and robot localization, relying on efficient point cloud registration and loop closure detection. Traditional Normal Distributions Transform (NDT) odometry frameworks provide robust solutions but struggle with real-time performance due to the high computational complexity of processing large-scale point clouds. This paper introduces an improved NDT-based LiDAR odometry framework to address these challenges. The proposed method enhances computational efficiency and registration accuracy by introducing a unified feature point cloud framework that integrates planar and edge features, enabling more accurate and efficient inter-frame matching. To further improve loop closure detection, a parallel hybrid approach combining Radius Search and Scan Context is developed, which significantly enhances robustness and accuracy. Additionally, feature-based point cloud registration is seamlessly integrated with full cloud mapping in global optimization, ensuring high-precision pose estimation and detailed environmental reconstruction. Experiments on both public datasets and real-world environments validate the effectiveness of the proposed framework. Compared with traditional NDT, our method achieves trajectory estimation accuracy increases of 35.59% and over 35%, respectively, with and without loop detection. The average registration time is reduced by 66.7%, memory usage is decreased by 23.16%, and CPU usage drops by 19.25%. These results surpass those of existing SLAM systems, such as LOAM. The proposed method demonstrates superior robustness, enabling reliable pose estimation and map construction in dynamic, complex settings. Full article
(This article belongs to the Section Manufacturing Processes and Systems)
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33 pages, 12439 KiB  
Article
DSFA-SwinNet: A Multi-Scale Attention Fusion Network for Photovoltaic Areas Detection
by Shaofu Lin, Yang Yang, Xiliang Liu and Li Tian
Remote Sens. 2025, 17(2), 332; https://doi.org/10.3390/rs17020332 (registering DOI) - 18 Jan 2025
Viewed by 442
Abstract
Precise statistics on the spatial distribution of photovoltaics (PV) are essential for advancing the PV industry, and integrating remote sensing with artificial intelligence technologies offers a robust solution for accurate identification. Currently, numerous studies focus on the detection of single-type PV installations through [...] Read more.
Precise statistics on the spatial distribution of photovoltaics (PV) are essential for advancing the PV industry, and integrating remote sensing with artificial intelligence technologies offers a robust solution for accurate identification. Currently, numerous studies focus on the detection of single-type PV installations through aerial or satellite imagery. However, due to the variability in scale and shape of PV installations in complex environments, the detection results often fail to capture detailed information and struggle to scale for multi-scale PV systems. To tackle these challenges, a detection method known as Dynamic Spatial-Frequency Attention SwinNet (DSFA-SwinNet) for multi-scale PV areas is proposed. First, this study proposes the Dynamic Spatial-Frequency Attention (DSFA) mechanism, the Pyramid Attention Refinement (PAR) bottleneck structure, and optimizes the feature propagation method to achieve dynamic decoupling of the spatial and frequency domains in multi-scale representation learning. Secondly, a hybrid loss function has been developed with weights optimized employing the Bayesian Optimization algorithm to provide a strategic method for parameter tuning in similar research. Lastly, the fixed window size of Swin-Transformer is dynamically adjusted to enhance computational efficiency and maintain accuracy. The results on two PV datasets demonstrate that DSFA-SwinNet significantly enhances detection accuracy and scalability for multi-scale PV areas. Full article
21 pages, 2757 KiB  
Article
Lightweight Transmission Line Outbreak Target Obstacle Detection Incorporating ACmix
by Junbo Hao, Guangying Yan, Lidong Wang, Honglan Pei, Xu Xiao and Baifu Zhang
Processes 2025, 13(1), 271; https://doi.org/10.3390/pr13010271 (registering DOI) - 18 Jan 2025
Viewed by 329
Abstract
Abstract: To address challenges such as the frequent misdetection of targets, missed detections of multiple targets, high computational demands, and poor real-time detection performance in the video surveillance of external breakage obstacles on transmission lines, we propose a lightweight target detection algorithm incorporating [...] Read more.
Abstract: To address challenges such as the frequent misdetection of targets, missed detections of multiple targets, high computational demands, and poor real-time detection performance in the video surveillance of external breakage obstacles on transmission lines, we propose a lightweight target detection algorithm incorporating the ACmix mechanism. First, the ShuffleNetv2 backbone network is used to reduce the model parameters and improve the detection speed. Next, the ACmix attention mechanism is integrated into the Neck layer to suppress irrelevant information, mitigate the impact of complex backgrounds on feature extraction, and enhance the network’s ability to detect small external breakage targets. Additionally, we introduce the PC-ELAN module to replace the ELAN-W module, reducing redundant feature extraction in the Neck network, lowering the model parameters, and boosting the detection efficiency. Finally, we adopt the SIoU loss function for bounding box regression, which enhances the model stability and convergence speed due to its smoothing characteristics. The experimental results show that the proposed algorithm achieves an mAP of 92.7%, which is 3% higher than the baseline network. The number of model parameters and the computational complexity are reduced by 32.3% and 44.9%, respectively, while the detection speed is improved by 3.5%. These results demonstrate that the proposed method significantly enhances the detection performance. Full article
(This article belongs to the Section Advanced Digital and Other Processes)
13 pages, 1256 KiB  
Review
Lung Cancers Associated with Cystic Airspaces
by Clara Valsecchi, Francesco Petrella, Stefania Freguia, Milo Frattini, Gianluca Argentieri, Carla Puligheddu, Giorgio Treglia and Stefania Rizzo
Cancers 2025, 17(2), 307; https://doi.org/10.3390/cancers17020307 (registering DOI) - 18 Jan 2025
Viewed by 334
Abstract
Lung cancer, the second most common malignancy in both men and women, poses a significant health burden. Early diagnosis remains pivotal in reducing lung cancer mortality. Given the escalating number of computed tomography (CT) examinations in both outpatient and inpatient settings, radiologists play [...] Read more.
Lung cancer, the second most common malignancy in both men and women, poses a significant health burden. Early diagnosis remains pivotal in reducing lung cancer mortality. Given the escalating number of computed tomography (CT) examinations in both outpatient and inpatient settings, radiologists play a crucial role in identifying early-stage pulmonary cancers, particularly non-nodular cancers. Screening programs have been instituted to achieve this goal, and they have raised attention within the scientific community to lung cancers associated with cystic airspaces. These cancers, although they have been known for at least a decade, remain understudied. Limited investigations with small sample sizes have estimated their prevalence and explored their radiological and pathological features. Lung cancers associated with cystic airspaces exhibit varying complexities within their cystic components and demonstrate suspicious changes over time. Adenocarcinoma is the predominant histological type, often with a peripheral location. Differential diagnosis on CT scans includes inflammatory processes or emphysema-related changes. Unfortunately, prospective studies specifically analyzing the prevalence of cystic airspace-associated lung cancers are lacking. However, it is estimated that they constitute approximately one-fourth of delayed radiological diagnoses. Increased awareness among radiologists could lead to more timely identification and potentially reduce lung cancer mortality in a cost-effective manner. Full article
(This article belongs to the Section Cancer Causes, Screening and Diagnosis)
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27 pages, 3968 KiB  
Article
Dissipation Alters Modes of Information Encoding in Small Quantum Reservoirs Near Criticality
by Krai Cheamsawat and Thiparat Chotibut
Entropy 2025, 27(1), 88; https://doi.org/10.3390/e27010088 (registering DOI) - 18 Jan 2025
Viewed by 308
Abstract
Quantum reservoir computing (QRC) has emerged as a promising paradigm for harnessing near-term quantum devices to tackle temporal machine learning tasks. Yet, identifying the mechanisms that underlie enhanced performance remains challenging, particularly in many-body open systems where nonlinear interactions and dissipation intertwine in [...] Read more.
Quantum reservoir computing (QRC) has emerged as a promising paradigm for harnessing near-term quantum devices to tackle temporal machine learning tasks. Yet, identifying the mechanisms that underlie enhanced performance remains challenging, particularly in many-body open systems where nonlinear interactions and dissipation intertwine in complex ways. Here, we investigate a minimal model of a driven-dissipative quantum reservoir described by two coupled Kerr-nonlinear oscillators, an experimentally realizable platform that features controllable coupling, intrinsic nonlinearity, and tunable photon loss. Using Partial Information Decomposition (PID), we examine how different dynamical regimes encode input drive signals in terms of redundancy (information shared by each oscillator) and synergy (information accessible only through their joint observation). Our key results show that, near a critical point marking a dynamical bifurcation, the system transitions from predominantly redundant to synergistic encoding. We further demonstrate that synergy amplifies short-term responsiveness, thereby enhancing immediate memory retention, whereas strong dissipation leads to more redundant encoding that supports long-term memory retention. These findings elucidate how the interplay of instability and dissipation shapes information processing in small quantum systems, providing a fine-grained, information-theoretic perspective for analyzing and designing QRC platforms. Full article
(This article belongs to the Special Issue Quantum Computing in the NISQ Era)
20 pages, 6192 KiB  
Article
Novel Assignment of Gene Markers to Hematological and Immune Cells Based on Single-Cell Transcriptomics
by Enrique De La Rosa, Natalia Alonso-Moreda, Alberto Berral-González, Elena Sánchez-Luis, Oscar González-Velasco, José Manuel Sánchez-Santos and Javier De Las Rivas
Int. J. Mol. Sci. 2025, 26(2), 805; https://doi.org/10.3390/ijms26020805 (registering DOI) - 18 Jan 2025
Viewed by 341
Abstract
There are many different cells that perform highly specialized functions in the human hematological and immune systems. Due to the relevance of their activity, in this work we investigated the cell types and subtypes that form this complex system, using single-cell RNA sequencing [...] Read more.
There are many different cells that perform highly specialized functions in the human hematological and immune systems. Due to the relevance of their activity, in this work we investigated the cell types and subtypes that form this complex system, using single-cell RNA sequencing (scRNA-seq) to dissect and assess the markers that best define each cell population. We first developed an optimized computational workflow for analyzing large scRNA-seq datasets. We then used it to find gene markers of the different cell types present in bone marrow (BM) and peripheral blood (PB). We analyzed three different single-cell datasets to find specific cell markers using this strategy: first, we searched in the CD marker genes and then in the genes encoding membrane proteins and finally in all detected protein-coding genes. This allowed us not only to confirm known CDs that best mark some cell types (e.g., monocytes, B cells, NK cells, etc.) but also to test the ability of new genes to distinguish specific cell types. Finally, we applied a machine learning method (Random Forest) to test the accuracy of the different markers we found. As a result of all this work, we have found and propose specific and robust gene signatures to identify different types and subtypes of hematological and immune cells. Full article
(This article belongs to the Special Issue Molecular Biomarkers in Cancer and Their Applications 2.0)
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19 pages, 4102 KiB  
Article
On Fractional Discrete Memristive Model with Incommensurate Orders: Symmetry, Asymmetry, Hidden Chaos and Control Approaches
by Hussein Al-Taani, Ma’mon Abu Hammad, Mohammad Abudayah, Louiza Diabi and Adel Ouannas
Symmetry 2025, 17(1), 143; https://doi.org/10.3390/sym17010143 (registering DOI) - 18 Jan 2025
Viewed by 315
Abstract
Memristives provide a high degree of non-linearity to the model. This property has led to many studies focusing on developing memristive models to provide more non-linearity. This article studies a novel fractional discrete memristive system with incommensurate orders using ϑi-th Caputo-like [...] Read more.
Memristives provide a high degree of non-linearity to the model. This property has led to many studies focusing on developing memristive models to provide more non-linearity. This article studies a novel fractional discrete memristive system with incommensurate orders using ϑi-th Caputo-like operator. Bifurcation, phase portraits and the computation of the maximum Lyapunov Exponent (LEmax) are used to demonstrate their impact on the system’s dynamics. Furthermore, we employ the sample entropy approach (SampEn), C0 complexity and the 0-1 test to quantify complexity and validate chaos in the incommensurate system. Studies indicate that the discrete memristive system with incommensurate fractional orders manifests diverse dynamical behaviors, including hidden chaos, symmetry, and asymmetry attractors, which are influenced by the incommensurate derivative values. Moreover, a 2D non-linear controller is presented to stabilize and synchronize the novel system. The work results are provided by numerical simulation obtained using MATLAB R2024a codes. Full article
(This article belongs to the Special Issue Symmetry/Asymmetry in Chaos Theory and Application)
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20 pages, 10114 KiB  
Article
Design and Experimental Characterization of Developed Human Knee Joint Exoskeleton Prototypes
by Michał Olinski
Machines 2025, 13(1), 70; https://doi.org/10.3390/machines13010070 (registering DOI) - 18 Jan 2025
Viewed by 174
Abstract
This paper focuses on the experimental testing and characterisation of two designed and constructed prototypes of a human knee joint mechanism. The aim of the mechanical systems, presented as kinematic diagrams and 3D CAD drawings, is to reproduce the knee joint’s complex movement, [...] Read more.
This paper focuses on the experimental testing and characterisation of two designed and constructed prototypes of a human knee joint mechanism. The aim of the mechanical systems, presented as kinematic diagrams and 3D CAD drawings, is to reproduce the knee joint’s complex movement, in particular the flexion/extension in the sagittal plane, within a given range and constraints, while taking into account the trajectory of the joint’s instantaneous centre of rotation. The first prototype can simulate different movements by modifying its dimensions in real time using a linearly adjustable crossed four-bar mechanism. The second prototype has interchangeable cooperating components, with cam profiles that can be adapted to specific requirements. Both devices are built from 3D-printed parts and their characteristics are determined experimentally. Although many types of tests have been carried out, this research mainly aims to conduct experiments with volunteers. To this end, the IMU sensors measure the mechanisms’ movements, but the main source of the data is video analysis of the colour markers. For the purposes of postprocessing, the results in the form of numerical values and figures were computed by Matlab 2019b. To illustrate the prototypes’ capabilities, the results are shown as motion trajectories of selected tibia/femur points and the calculated knee joint’s flexion/extension angle. Full article
21 pages, 7738 KiB  
Article
High-Accuracy and Efficient Simulation of Numerical Control Machining Using Tri-Level Grid and Envelope Theory
by Zhengwen Nie and Yanzheng Zhao
Machines 2025, 13(1), 69; https://doi.org/10.3390/machines13010069 (registering DOI) - 18 Jan 2025
Viewed by 165
Abstract
Virtual simulation of high-resolution multi-axis machining processes nowadays plays an important role in the production of complex parts in various industries. In order to improve the surface quality and productivity, process parameters, such as spindle speed, feedrate, and depth of cut, need to [...] Read more.
Virtual simulation of high-resolution multi-axis machining processes nowadays plays an important role in the production of complex parts in various industries. In order to improve the surface quality and productivity, process parameters, such as spindle speed, feedrate, and depth of cut, need to be optimized by using an accurate process model of milling, which requires both the fast virtual prototyping of machined part geometry for tool path verification and accurate determination of cutter–workpiece engagement for cutting force predictions. Under these circumstances, this paper presents an effective volumetric method that can accurately provide the required geometric information with high and stable computational efficiency under the condition of high grid resolution. The proposed method is built on a tri-level grid, which applies two levels of adaptive refinement in space decomposition to abolish the adverse effect of a large fine-level branching factor on its efficiency. Since hierarchical space decomposition is used, this multi-level representation enables the batch processing of affected voxels and minimal intersection calculations, achieving fast and accurate modeling results. To calculate the instantaneous engagement region, the immersion angles are obtained by fusing the intersection points between the bottom-level voxel edges and the cutter surface, which are then trimmed by feasible contact arcs determined using envelope theory. In a series of test cases, the proposed method shows higher efficiency than the tri-dexel model and stronger applicability in high-precision machining than the two-level grid. Full article
(This article belongs to the Section Advanced Manufacturing)
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