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Search Results (633)

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30 pages, 3080 KiB  
Article
Unsupervised Multimodal Community Detection Algorithm in Complex Network Based on Fractal Iteration
by Hui Deng, Yanchao Huang, Jian Wang, Yanmei Hu and Biao Cai
Fractal Fract. 2025, 9(8), 507; https://doi.org/10.3390/fractalfract9080507 (registering DOI) - 2 Aug 2025
Viewed by 53
Abstract
Community detection in complex networks plays a pivotal role in modern scientific research, including in social network analysis and protein structure analysis. Traditional community detection methods face challenges in integrating heterogeneous multi-source information, capturing global semantic relationships, and adapting to dynamic network evolution. [...] Read more.
Community detection in complex networks plays a pivotal role in modern scientific research, including in social network analysis and protein structure analysis. Traditional community detection methods face challenges in integrating heterogeneous multi-source information, capturing global semantic relationships, and adapting to dynamic network evolution. This paper proposes a novel unsupervised multimodal community detection algorithm (UMM) based on fractal iteration. The core idea is to design a dual-channel encoder that comprehensively considers node semantic features and network topological structures. Initially, node representation vectors are derived from structural information (using feature vectors when available, or singular value decomposition to obtain feature vectors for nodes without attributes). Subsequently, a parameter-free graph convolutional encoder (PFGC) is developed based on fractal iteration principles to extract high-order semantic representations from structural encodings without requiring any training process. Furthermore, a semantic–structural dual-channel encoder (DC-SSE) is designed, which integrates semantic encodings—reduced in dimensionality via UMAP—with structural features extracted by PFGC to obtain the final node embeddings. These embeddings are then clustered using the K-means algorithm to achieve community partitioning. Experimental results demonstrate that the UMM outperforms existing methods on multiple real-world network datasets. Full article
22 pages, 2898 KiB  
Article
Genetic Variability and Trait Correlations in Lotus corniculatus L. as a Basis for Sustainable Forage Breeding
by Cristian Bostan, Nicolae Marinel Horablaga, Marius Boldea, Emilian Onișan, Christianna Istrate-Schiller, Dorin Rechitean, Luminita Cojocariu, Alina Laura Agapie, Adina Horablaga, Ioan Sarac, Sorina Popescu, Petru Rain and Ionel Samfira
Sustainability 2025, 17(15), 7007; https://doi.org/10.3390/su17157007 (registering DOI) - 1 Aug 2025
Viewed by 98
Abstract
Lotus corniculatus L. is a valuable fodder legume, recognized for its ecological adaptability and high potential for production and fodder quality. In this study, 18 genotypes collected from wild flora were analyzed to highlight genetic variability and facilitate the selection of genotypes with [...] Read more.
Lotus corniculatus L. is a valuable fodder legume, recognized for its ecological adaptability and high potential for production and fodder quality. In this study, 18 genotypes collected from wild flora were analyzed to highlight genetic variability and facilitate the selection of genotypes with superior potential. The collection area was in the western part of Romania and featured a diverse topography, including parts of the Banat Plain, the Banat Hills, and the Southern and Western Carpathians. The genotypes selected from the wild flora were cultivated and evaluated for morpho-productive and forage quality traits, including pod weight, average number of seeds/pods, green mass production, and protein percentage. PCA highlighted the main components explaining the variability, and K-means clustering allowed for the identification of groups of genotypes with similar performances. ANOVA showed statistically significant differences (p < 0.001) for all traits analyzed. According to the results, genotypes LV-LC-3, LV-LC-4, LV-LC-6, and LV-LC-16 showed high productive potential and were highlighted as the most valuable for advancing in the breeding program. The moderate relationships between traits confirm the importance of integrated selection. The identified genetic variability and selected genotypes support the implementation of effective breeding strategies to obtain high-performance Lotus corniculatus L., adapted to local soil and climate conditions and with a superior forage yield. Full article
(This article belongs to the Section Sustainable Agriculture)
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18 pages, 1777 KiB  
Article
Machine Learning in Sensory Analysis of Mead—A Case Study: Ensembles of Classifiers
by Krzysztof Przybył, Daria Cicha-Wojciechowicz, Natalia Drabińska and Małgorzata Anna Majcher
Molecules 2025, 30(15), 3199; https://doi.org/10.3390/molecules30153199 - 30 Jul 2025
Viewed by 154
Abstract
The aim was to explore using machine learning (including cluster mapping and k-means methods) to classify types of mead based on sensory analysis and aromatic compounds. Machine learning is a modern tool that helps with detailed analysis, especially because verifying aromatic compounds is [...] Read more.
The aim was to explore using machine learning (including cluster mapping and k-means methods) to classify types of mead based on sensory analysis and aromatic compounds. Machine learning is a modern tool that helps with detailed analysis, especially because verifying aromatic compounds is challenging. In the first stage, a cluster map analysis was conducted, allowing for the exploratory identification of the most characteristic features of mead. Based on this, k-means clustering was performed to evaluate how well the identified sensory features align with logically consistent groups of observations. In the next stage, experiments were carried out to classify the type of mead using algorithms such as Random Forest (RF), adaptive boosting (AdaBoost), Bootstrap aggregation (Bagging), K-Nearest Neighbors (KNN), and Decision Tree (DT). The analysis revealed that the RF and KNN algorithms were the most effective in classifying mead based on sensory characteristics, achieving the highest accuracy. In contrast, the AdaBoost algorithm consistently produced the lowest accuracy results. However, the Decision Tree algorithm achieved the highest accuracy value (0.909), demonstrating its potential for precise classification based on aroma characteristics. The error matrix analysis also indicated that acacia mead was easier for the algorithms to identify than tilia or buckwheat mead. The results show the potential of combining an exploratory approach (cluster map with the k-means method) with machine learning. It is also important to focus on selecting and optimizing classification models used in practice because, as the results so far indicate, choosing the right algorithm greatly affects the success of mead identification. Full article
(This article belongs to the Special Issue Analytical Technologies and Intelligent Applications in Future Food)
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24 pages, 3726 KiB  
Article
Telemedicine-Supported CPAP Therapy in Patients with Obstructive Sleep Apnea: Association with Treatment Adherence and Clinical Outcomes
by Norbert Wellmann, Versavia Maria Ancusa, Monica Steluta Marc, Ana Adriana Trusculescu, Camelia Corina Pescaru, Flavia Gabriela Martis, Ioana Ciortea, Alexandru Florian Crisan, Adelina Maritescu, Madalina Alexandra Balica and Ovidiu Fira-Mladinescu
J. Clin. Med. 2025, 14(15), 5339; https://doi.org/10.3390/jcm14155339 - 29 Jul 2025
Viewed by 188
Abstract
Background/Objectives: Obstructive sleep apnea (OSA) is a highly prevalent disorder that significantly impacts quality of life and daily functioning. While continuous positive airway pressure (CPAP) therapy is effective, long-term adherence remains a challenge. This single-arm observational study aimed to evaluate clinical outcomes and [...] Read more.
Background/Objectives: Obstructive sleep apnea (OSA) is a highly prevalent disorder that significantly impacts quality of life and daily functioning. While continuous positive airway pressure (CPAP) therapy is effective, long-term adherence remains a challenge. This single-arm observational study aimed to evaluate clinical outcomes and adherence patterns during telemedicine-supported CPAP therapy and identify distinct phenotypic response clusters in Romanian patients with OSA. Methods: This prospective observational study included 86 adults diagnosed with OSA, treated with ResMed Auto CPAP devices at “Victor Babeș” University Hospital in Timișoara, Romania. All patients were remotely monitored via the AirView™ platform and received monthly telephone interventions to promote adherence when necessary. Clinical outcomes were assessed through objective telemonitoring data. K-means clustering and t-distributed stochastic neighbor embedding (t-SNE) were employed to explore phenotypic response patterns. Results: During telemedicine-supported CPAP therapy, significant clinical improvements were observed. The apnea–hypopnea index (AHI) decreased from 42.0 ± 21.1 to 1.9 ± 1.3 events/hour. CPAP adherence improved from 75.5% to 90.5% over six months. Average daily usage increased from 348.4 ± 85.8 to 384.2 ± 65.2 min. However, post hoc analysis revealed significant concerns about the validity of self-reported psychological improvements. Self-esteem changes showed negligible correlation with objective clinical measures (r < 0.2, all p > 0.1), with only 3.3% of variance being explained by measurable therapeutic factors (R2 = 0.033). Clustering analysis identified four distinct adherence and outcome profiles, yet paradoxically, patients with lower adherence showed greater self-esteem improvements, contradicting therapeutic causation. Conclusions: Telemedicine-supported CPAP therapy with structured monthly interventions was associated with substantial clinical improvements, including excellent AHI reduction (22-fold) and high adherence rates (+15% after 6 months). Data-driven phenotyping successfully identified distinct patient response profiles, supporting personalized management approaches. However, the single-arm design prevents definitive attribution of improvements to telemonitoring versus natural adaptation or placebo effects. Self-reported psychological outcomes showed concerning patterns suggesting predominant placebo responses rather than therapeutic benefits. While the overall findings demonstrate the potential value of structured telemonitoring for objective CPAP outcomes, controlled trials are essential to establishing true therapeutic efficacy and distinguishing intervention effects from measurement bias. Full article
(This article belongs to the Special Issue Advances in Pulmonary Disease Management and Innovation in Treatment)
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25 pages, 2518 KiB  
Article
An Efficient Semantic Segmentation Framework with Attention-Driven Context Enhancement and Dynamic Fusion for Autonomous Driving
by Jia Tian, Peizeng Xin, Xinlu Bai, Zhiguo Xiao and Nianfeng Li
Appl. Sci. 2025, 15(15), 8373; https://doi.org/10.3390/app15158373 - 28 Jul 2025
Viewed by 310
Abstract
In recent years, a growing number of real-time semantic segmentation networks have been developed to improve segmentation accuracy. However, these advancements often come at the cost of increased computational complexity, which limits their inference efficiency, particularly in scenarios such as autonomous driving, where [...] Read more.
In recent years, a growing number of real-time semantic segmentation networks have been developed to improve segmentation accuracy. However, these advancements often come at the cost of increased computational complexity, which limits their inference efficiency, particularly in scenarios such as autonomous driving, where strict real-time performance is essential. Achieving an effective balance between speed and accuracy has thus become a central challenge in this field. To address this issue, we present a lightweight semantic segmentation model tailored for the perception requirements of autonomous vehicles. The architecture follows an encoder–decoder paradigm, which not only preserves the capability for deep feature extraction but also facilitates multi-scale information integration. The encoder leverages a high-efficiency backbone, while the decoder introduces a dynamic fusion mechanism designed to enhance information interaction between different feature branches. Recognizing the limitations of convolutional networks in modeling long-range dependencies and capturing global semantic context, the model incorporates an attention-based feature extraction component. This is further augmented by positional encoding, enabling better awareness of spatial structures and local details. The dynamic fusion mechanism employs an adaptive weighting strategy, adjusting the contribution of each feature channel to reduce redundancy and improve representation quality. To validate the effectiveness of the proposed network, experiments were conducted on a single RTX 3090 GPU. The Dynamic Real-time Integrated Vision Encoder–Segmenter Network (DriveSegNet) achieved a mean Intersection over Union (mIoU) of 76.9% and an inference speed of 70.5 FPS on the Cityscapes test dataset, 74.6% mIoU and 139.8 FPS on the CamVid test dataset, and 35.8% mIoU with 108.4 FPS on the ADE20K dataset. The experimental results demonstrate that the proposed method achieves an excellent balance between inference speed, segmentation accuracy, and model size. Full article
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24 pages, 2710 KiB  
Article
Spatial and Economic-Based Clustering of Greek Irrigation Water Organizations: A Data-Driven Framework for Sustainable Water Pricing and Policy Reform
by Dimitrios Tsagkoudis, Eleni Zafeiriou and Konstantinos Spinthiropoulos
Water 2025, 17(15), 2242; https://doi.org/10.3390/w17152242 - 28 Jul 2025
Viewed by 297
Abstract
This study employs k-means clustering to analyze local organizations responsible for land improvement in Greece, identifying four distinct groups with consistent geographic patterns but divergent financial and operational characteristics. By integrating unsupervised machine learning with spatial analysis, the research offers a novel perspective [...] Read more.
This study employs k-means clustering to analyze local organizations responsible for land improvement in Greece, identifying four distinct groups with consistent geographic patterns but divergent financial and operational characteristics. By integrating unsupervised machine learning with spatial analysis, the research offers a novel perspective on irrigation water pricing and cost recovery. The findings reveal that organizations located on islands, despite high water costs due to limited rainfall and geographic isolation, tend to achieve relatively strong financial performance, indicating the presence of adaptive mechanisms that could inform broader policy strategies. In contrast, organizations managing extensive irrigable land or large volumes of water frequently show poor cost recovery, challenging assumptions about economies of scale and revealing inefficiencies in pricing or governance structures. The spatial coherence of the clusters underscores the importance of geography in shaping institutional outcomes, reaffirming that environmental and locational factors can offer greater explanatory power than algorithmic models alone. This highlights the need for water management policies that move beyond uniform national strategies and instead reflect regional climatic, infrastructural, and economic variability. The study suggests several policy directions, including targeted infrastructure investment, locally calibrated water pricing models, and performance benchmarking based on successful organizational practices. Although grounded in the Greek context, the methodology and insights are transferable to other European and Mediterranean regions facing similar water governance challenges. Recognizing the limitations of the current analysis—including gaps in data consistency and the exclusion of socio-environmental indicators—the study advocates for future research incorporating broader variables and international comparative approaches. Ultimately, it supports a hybrid policy framework that combines data-driven analysis with spatial intelligence to promote sustainability, equity, and financial viability in agricultural water management. Full article
(This article belongs to the Special Issue Balancing Competing Demands for Sustainable Water Development)
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28 pages, 2549 KiB  
Article
A 25K Wheat SNP Array Revealed the Genetic Diversity and Population Structure of Durum Wheat (Triticum turgidum subsp. durum) Landraces and Cultivars
by Lalise Ararsa, Behailu Mulugeta, Endashaw Bekele, Negash Geleta, Kibrom B. Abreha and Mulatu Geleta
Int. J. Mol. Sci. 2025, 26(15), 7220; https://doi.org/10.3390/ijms26157220 - 25 Jul 2025
Viewed by 1085
Abstract
Durum wheat, the world’s second most cultivated wheat species, is a staple crop, critical for global food security, including in Ethiopia where it serves as a center of diversity. However, climate change and genetic erosion threaten its genetic resources, necessitating genomic studies to [...] Read more.
Durum wheat, the world’s second most cultivated wheat species, is a staple crop, critical for global food security, including in Ethiopia where it serves as a center of diversity. However, climate change and genetic erosion threaten its genetic resources, necessitating genomic studies to support conservation and breeding efforts. This study characterized genome-wide diversity, population structure (STRUCTURE, principal coordinate analysis (PCoA), neighbor-joining trees, analysis of molecular variance (AMOVA)), and selection signatures (FST, Hardy–Weinberg deviations) in Ethiopian durum wheat by analyzing 376 genotypes (148 accessions) using an Illumina Infinium 25K single nucleotide polymorphism (SNP) array. A set of 7842 high-quality SNPs enabled the assessments, comparing landraces with cultivars and breeding populations. Results revealed moderate genetic diversity (mean polymorphism information content (PIC) = 0.17; gene diversity = 0.20) and identified 26 loci under selection, associated with key traits like grain yield, stress tolerance, and disease resistance. AMOVA revealed 80.1% variation among accessions, with no significant differentiation by altitude, region, or spike density. Landraces formed distinct clusters, harboring unique alleles, while admixture suggested gene flow via informal seed exchange. The findings highlight Ethiopia’s rich durum wheat diversity, emphasizing landraces as reservoirs of adaptive alleles for breeding. This study provides genomic insights to guide conservation and the development of climate-resilient cultivars, supporting sustainable wheat production globally. Full article
(This article belongs to the Special Issue Latest Research on Plant Genomics and Genome Editing, 2nd Edition)
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23 pages, 1885 KiB  
Article
Applying Machine Learning to DEEC Protocol: Improved Cluster Formation in Wireless Sensor Networks
by Abdulla Juwaied and Lidia Jackowska-Strumillo
Network 2025, 5(3), 26; https://doi.org/10.3390/network5030026 - 24 Jul 2025
Viewed by 185
Abstract
Wireless Sensor Networks (WSNs) are specialised ad hoc networks composed of small, low-power, and often battery-operated sensor nodes with various sensors and wireless communication capabilities. These nodes collaborate to monitor and collect data from the physical environment, transmitting it to a central location [...] Read more.
Wireless Sensor Networks (WSNs) are specialised ad hoc networks composed of small, low-power, and often battery-operated sensor nodes with various sensors and wireless communication capabilities. These nodes collaborate to monitor and collect data from the physical environment, transmitting it to a central location or sink node for further processing and analysis. This study proposes two machine learning-based enhancements to the DEEC protocol for Wireless Sensor Networks (WSNs) by integrating the K-Nearest Neighbours (K-NN) and K-Means (K-M) machine learning (ML) algorithms. The Distributed Energy-Efficient Clustering with K-NN (DEEC-KNN) and with K-Means (DEEC-KM) approaches dynamically optimize cluster head selection to improve energy efficiency and network lifetime. These methods are validated through extensive simulations, demonstrating up to 110% improvement in packet delivery and significant gains in network stability compared with the original DEEC protocol. The adaptive clustering enabled by K-NN and K-Means is particularly effective for large-scale and dynamic WSN deployments where node failures and topology changes are frequent. These findings suggest that integrating ML with clustering protocols is a promising direction for future WSN design. Full article
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18 pages, 33092 KiB  
Article
Yarn Color Measurement Method Based on Digital Photography
by Jinxing Liang, Guanghao Wu, Ke Yang, Jiangxiaotian Ma, Jihao Wang, Hang Luo, Xinrong Hu and Yong Liu
J. Imaging 2025, 11(8), 248; https://doi.org/10.3390/jimaging11080248 - 22 Jul 2025
Viewed by 246
Abstract
To overcome the complexity of yarn color measurement using spectrophotometry with yarn winding techniques and to enhance consistency with human visual perception, a yarn color measurement method based on digital photography is proposed. This study employs a photographic colorimetry system to capture digital [...] Read more.
To overcome the complexity of yarn color measurement using spectrophotometry with yarn winding techniques and to enhance consistency with human visual perception, a yarn color measurement method based on digital photography is proposed. This study employs a photographic colorimetry system to capture digital images of single yarns. The yarn and background are segmented using the K-means clustering algorithm, and the centerline of the yarn is extracted using a skeletonization algorithm. Spectral reconstruction and colorimetric principles are then applied to calculate the color values of pixels along the centerline. Considering the nonlinear characteristics of human brightness perception, the final yarn color is obtained through a nonlinear texture-adaptive weighted computation. The method is validated through psychophysical experiments using six yarns of different colors and compared with spectrophotometry and five other photographic measurement methods. Results indicate that among the seven yarn color measurement methods, including spectrophotometry, the proposed method—based on centerline extraction and nonlinear texture-adaptive weighting—yields results that more closely align with actual visual perception. Furthermore, among the six photographic measurement methods, the proposed method produces most similar to those obtained using spectrophotometry. This study demonstrates the inconsistency between spectrophotometric measurements and human visual perception of yarn color and provides methodological support for developing visually consistent color measurement methods for textured textiles. Full article
(This article belongs to the Section Color, Multi-spectral, and Hyperspectral Imaging)
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24 pages, 6464 KiB  
Article
A Hybrid Model for Carbon Price Forecasting Based on Secondary Decomposition and Weight Optimization
by Yongfa Chen, Yingjie Zhu, Jie Wang and Meng Li
Mathematics 2025, 13(14), 2323; https://doi.org/10.3390/math13142323 - 21 Jul 2025
Viewed by 293
Abstract
Accurate carbon price forecasting is essential for market stability, risk management, and policy-making. To address the nonlinear, non-stationary, and multiscale nature of carbon prices, this paper proposes a forecasting framework integrating secondary decomposition, two-stage feature selection, and dynamic ensemble learning. Firstly, the original [...] Read more.
Accurate carbon price forecasting is essential for market stability, risk management, and policy-making. To address the nonlinear, non-stationary, and multiscale nature of carbon prices, this paper proposes a forecasting framework integrating secondary decomposition, two-stage feature selection, and dynamic ensemble learning. Firstly, the original price series is decomposed into intrinsic mode functions (IMFs), using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). The IMFs are then grouped into low- and high-frequency components based on multiscale entropy (MSE) and K-Means clustering. To further alleviate mode mixing in the high-frequency components, an improved variational mode decomposition (VMD) optimized by particle swarm optimization (PSO) is applied for secondary decomposition. Secondly, a two-stage feature-selection method is employed, in which the partial autocorrelation function (PACF) is used to select relevant lagged features, while the maximal information coefficient (MIC) is applied to identify key variables from both historical and external data. Finally, this paper introduces a dynamic integration module based on sliding windows and sequential least squares programming (SLSQP), which can not only adaptively adjust the weights of four base learners but can also effectively leverage the complementary advantages of each model and track the dynamic trends of carbon prices. The empirical results of the carbon markets in Hubei and Guangdong indicate that the proposed method outperforms the benchmark model in terms of prediction accuracy and robustness, and the method has been tested by Diebold Mariano (DM). The main contributions are the improved feature-extraction process and the innovative use of a sliding window-based SLSQP method for dynamic ensemble weight optimization. Full article
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23 pages, 9064 KiB  
Article
A Computational Thermo-Fluid Dynamics Simulation of Slot Jet Impingement Using a Generalized Two-Equation Turbulence Model
by Antonio Mezzacapo, Rossella D’Addio and Giuliano De Stefano
Energies 2025, 18(14), 3862; https://doi.org/10.3390/en18143862 - 20 Jul 2025
Viewed by 961
Abstract
In this study, a computational thermo-fluid dynamics simulation of a wide-slot jet impingement heating process is performed. The present configuration consists of a turbulent incompressible air jet impinging orthogonally on an isothermal cold plate at a Reynolds number of around 11,000. The two-dimensional [...] Read more.
In this study, a computational thermo-fluid dynamics simulation of a wide-slot jet impingement heating process is performed. The present configuration consists of a turbulent incompressible air jet impinging orthogonally on an isothermal cold plate at a Reynolds number of around 11,000. The two-dimensional mean turbulent flow field is numerically predicted by solving Reynolds-averaged Navier–Stokes (RANS) equations, where the two-equation eddy viscosity k-ω model is utilized for turbulence closure. As the commonly used shear stress transport variant overpredicts heat transfer at the plate due to excessive turbulent diffusion, the recently developed generalized k-ω (GEKO) model is considered for the present analysis, where the primary model coefficients are suitably tuned. Through a comparative analysis of the various solutions against one another, in addition to reference experimental and numerical data, the effectiveness of the generalized procedure in predicting both the jet flow characteristics and the heat transfer at the plate is thoroughly evaluated, while determining the optimal set of model parameters. By improving accuracy within the RANS framework, the importance of model adaptability and parameter tuning for this specific fluid engineering application is demonstrated. This study offers valuable insights for improving predictive capability in turbulent jet simulations with broad engineering implications, particularly for industrial heating or cooling systems relying on wide-slot jet impingement. Full article
(This article belongs to the Special Issue Computational Fluids Dynamics in Energy Conversion and Heat Transfer)
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26 pages, 8154 KiB  
Article
Investigation into the Efficient Cooperative Planning Approach for Dual-Arm Picking Sequences of Dwarf, High-Density Safflowers
by Zhenguo Zhang, Peng Xu, Binbin Xie, Yunze Wang, Ruimeng Shi, Junye Li, Wenjie Cao, Wenqiang Chu and Chao Zeng
Sensors 2025, 25(14), 4459; https://doi.org/10.3390/s25144459 - 17 Jul 2025
Viewed by 217
Abstract
Path planning for picking safflowers is a key component in ensuring the efficient operation of robotic safflower-picking systems. However, existing single-arm picking devices have become a bottleneck due to their limited operating range, and a breakthrough in multi-arm cooperative picking is urgently needed. [...] Read more.
Path planning for picking safflowers is a key component in ensuring the efficient operation of robotic safflower-picking systems. However, existing single-arm picking devices have become a bottleneck due to their limited operating range, and a breakthrough in multi-arm cooperative picking is urgently needed. To address the issue of inadequate adaptability in current path planning strategies for dual-arm systems, this paper proposes a novel path planning method for dual-arm picking (LTSACO). The technique centers on a dynamic-weight heuristic strategy and achieves optimization through the following steps: first, the K-means clustering algorithm divides the target area; second, the heuristic mechanism of the Ant Colony Optimization (ACO) algorithm is improved by dynamically adjusting the weight factor of the state transition probability, thereby enhancing the diversity of path selection; third, a 2-OPT local search strategy eliminates path crossings through neighborhood search; finally, a cubic Bézier curve heuristically smooths and optimizes the picking trajectory, ensuring the continuity of the trajectory’s curvature. Experimental results show that the length of the parallelogram trajectory, after smoothing with the Bézier curve, is reduced by 20.52% compared to the gantry trajectory. In terms of average picking time, the LTSACO algorithm reduces the time by 2.00%, 2.60%, and 5.60% compared to DCACO, IACO, and the traditional ACO algorithm, respectively. In conclusion, the LTSACO algorithm demonstrates high efficiency and strong robustness, providing an effective optimization solution for multi-arm cooperative picking and significantly contributing to the advancement of multi-arm robotic picking systems. Full article
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14 pages, 2239 KiB  
Article
Automatic Delineation of Resistivity Contrasts in Magnetotelluric Models Using Machine Learning
by Ever Herrera Ríos, Mateo Marulanda, Hernán Arboleda, Greg Soule, Erika Lucuara, David Jaramillo, Agustín Cardona, Esteban A. Taborda, Farid B. Cortés and Camilo A. Franco
Processes 2025, 13(7), 2263; https://doi.org/10.3390/pr13072263 - 16 Jul 2025
Viewed by 307
Abstract
The precise identification of hydrocarbon-rich zones is crucial for optimizing exploration and production processes in the oil industry. Magnetotelluric (MT) surveys play a fundamental role in mapping subsurface geological structures. This study presents a novel methodology for automatically delineating resistivity contrasts in MT [...] Read more.
The precise identification of hydrocarbon-rich zones is crucial for optimizing exploration and production processes in the oil industry. Magnetotelluric (MT) surveys play a fundamental role in mapping subsurface geological structures. This study presents a novel methodology for automatically delineating resistivity contrasts in MT models by employing advanced machine learning and computer vision techniques. This approach commences with data augmentation to enhance the diversity and volume of resistivity data. Subsequently, a bilateral filter was applied to reduce noise while preserving edge details within the resistivity images. To further improve image contrast and highlight significant resistivity variations, contrast-limited adaptive histogram equalization (CLAHE) was employed. Finally, k-means clustering was utilized to segment the resistivity data into distinct groups based on resistivity values, enabling the identification of color features in different centroids. This facilitated the detection of regions with significant resistivity contrasts in the reservoir. From the clustered images, color masks were generated to visually differentiate the groups and calculate the area and proportion of each group within the pictures. Key features extracted from resistivity profiles were used to train unsupervised learning models capable of generalizing across different geological settings. The proposed methodology improves the accuracy of detecting zones with oil potential and offers scalable applicability to different datasets with minimal retraining, applicable to different subsurface environments. Ultimately, this study seeks to improve the efficiency of petroleum exploration by providing a high-precision automated framework with segmentation and contrast delineation for resistivity analysis, integrating advanced image processing and machine learning techniques. During initial analyses using only k-means, the resulting optimal value of the silhouette coefficient K was 2. After using bilateral filtering together with contrast-limited adaptive histogram equalization (CLAHE) and validation by an expert, the results were more representative, and six clusters were identified. Ultimately, this study seeks to improve the efficiency of petroleum exploration by providing a high-precision automated framework with segmentation and contrast delineation for resistivity analysis, integrating advanced image processing and machine learning techniques. Full article
(This article belongs to the Section Energy Systems)
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43 pages, 190510 KiB  
Article
From Viewing to Structure: A Computational Framework for Modeling and Visualizing Visual Exploration
by Kuan-Chen Chen, Chang-Franw Lee, Teng-Wen Chang, Cheng-Gang Wang and Jia-Rong Li
Appl. Sci. 2025, 15(14), 7900; https://doi.org/10.3390/app15147900 - 15 Jul 2025
Viewed by 267
Abstract
This study proposes a computational framework that transforms eye-tracking analysis from statistical description to cognitive structure modeling, aiming to reveal the organizational features embedded in the viewing process. Using the designers’ observation of a traditional Chinese landscape painting as an example, the study [...] Read more.
This study proposes a computational framework that transforms eye-tracking analysis from statistical description to cognitive structure modeling, aiming to reveal the organizational features embedded in the viewing process. Using the designers’ observation of a traditional Chinese landscape painting as an example, the study draws on the goal-oriented nature of design thinking to suggest that such visual exploration may exhibit latent structural tendencies, reflected in patterns of fixation and transition. Rather than focusing on traditional fixation hotspots, our four-dimensional framework (Region, Relation, Weight, Time) treats viewing behavior as structured cognitive networks. To operationalize this framework, we developed a data-driven computational approach that integrates fixation coordinate transformation, K-means clustering, extremum point detection, and linear interpolation. These techniques identify regions of concentrated visual attention and define their spatial boundaries, allowing for the modeling of inter-regional relationships and cognitive organization among visual areas. An adaptive buffer zone method is further employed to quantify the strength of connections between regions and to delineate potential visual nodes and transition pathways. Three design-trained participants were invited to observe the same painting while performing a think-aloud task, with one participant selected for the detailed demonstration of the analytical process. The framework’s applicability across different viewers was validated through consistent structural patterns observed across all three participants, while simultaneously revealing individual differences in their visual exploration strategies. These findings demonstrate that the proposed framework provides a replicable and generalizable method for systematically analyzing viewing behavior across individuals, enabling rapid identification of both common patterns and individual differences in visual exploration. This approach opens new possibilities for discovering structural organization within visual exploration data and analyzing goal-directed viewing behaviors. Although this study focuses on method demonstration, it proposes a preliminary hypothesis that designers’ gaze structures are significantly more clustered and hierarchically organized than those of novices, providing a foundation for future confirmatory testing. Full article
(This article belongs to the Special Issue New Insights into Computer Vision and Graphics)
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20 pages, 5486 KiB  
Article
SE-TransUNet-Based Semantic Segmentation for Water Leakage Detection in Tunnel Secondary Linings Amid Complex Visual Backgrounds
by Renjie Song, Yimin Wu, Li Wan, Shuai Shao and Haiping Wu
Appl. Sci. 2025, 15(14), 7872; https://doi.org/10.3390/app15147872 - 14 Jul 2025
Viewed by 255
Abstract
Traditional manual inspection methods for tunnel lining leakage are subjective and inefficient, while existing models lack sufficient recognition accuracy in complex scenarios. An intelligent leakage identification model adaptable to complex backgrounds is therefore needed. To address these issues, a Vision Transformer (ViT) was [...] Read more.
Traditional manual inspection methods for tunnel lining leakage are subjective and inefficient, while existing models lack sufficient recognition accuracy in complex scenarios. An intelligent leakage identification model adaptable to complex backgrounds is therefore needed. To address these issues, a Vision Transformer (ViT) was integrated into the UNet architecture, forming an SE-TransUNet model by incorporating SE-Block modules at skip connections between the encoder-decoder and the ViT output. Using a hybrid leakage dataset partitioned by k-fold cross-validation, the roles of SE-Block and ViT modules were examined through ablation experiments, and the model’s attention mechanism for leakage features was analyzed via Score-CAM heatmaps. Results indicate: (1) SE-TransUNet achieved mean values of 0.8318 (IoU), 0.8304 (Dice), 0.9394 (Recall), 0.8480 (Precision), 0.9733 (AUC), 0.8562 (MCC), 0.9218 (F1-score), and 6.53 (FPS) on the hybrid dataset, demonstrating robust generalization in scenarios with dent shadows, stain interference, and faint leakage traces. (2) Ablation experiments confirmed both modules’ necessity: The baseline model’s IoU exceeded the variant without the SE module by 4.50% and the variant without both the SE and ViT modules by 7.04%. (3) Score-CAM heatmaps showed the SE module broadened the model’s attention coverage of leakage areas, enhanced feature continuity, and improved anti-interference capability in complex environments. This research may provide a reference for related fields. Full article
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