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Search Results (662)

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18 pages, 11340 KiB  
Article
CLSANet: Cognitive Learning-Based Self-Adaptive Feature Fusion for Multimodal Visual Object Detection
by Han Peng, Qionglin Liu, Riqing Ruan, Shuaiqi Yuan and Qin Li
Electronics 2025, 14(15), 3082; https://doi.org/10.3390/electronics14153082 (registering DOI) - 1 Aug 2025
Abstract
Multimodal object detection leverages the complementary characteristics of visible (RGB) and infrared (IR) imagery, making it well-suited for challenging scenarios such as low illumination, occlusion, and complex backgrounds. However, most existing fusion-based methods rely on static or heuristic strategies, limiting their adaptability to [...] Read more.
Multimodal object detection leverages the complementary characteristics of visible (RGB) and infrared (IR) imagery, making it well-suited for challenging scenarios such as low illumination, occlusion, and complex backgrounds. However, most existing fusion-based methods rely on static or heuristic strategies, limiting their adaptability to dynamic environments. To address this limitation, we propose CLSANet, a cognitive learning-based self-adaptive network that enhances detection performance by dynamically selecting and integrating modality-specific features. CLSANet consists of three key modules: (1) a Dominant Modality Identification Module that selects the most informative modality based on global scene analysis; (2) a Modality Enhancement Module that disentangles and strengthens shared and modality-specific representations; and (3) a Self-Adaptive Fusion Module that adjusts fusion weights spatially according to local scene complexity. Compared to existing methods, CLSANet achieves state-of-the-art detection performance with significantly fewer parameters and lower computational cost. Ablation studies further demonstrate the individual effectiveness of each module under different environmental conditions, particularly in low-light and occluded scenes. CLSANet offers a compact, interpretable, and practical solution for multimodal object detection in resource-constrained settings. Full article
(This article belongs to the Special Issue Digital Intelligence Technology and Applications)
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19 pages, 3294 KiB  
Article
Rotation- and Scale-Invariant Object Detection Using Compressed 2D Voting with Sparse Point-Pair Screening
by Chenbo Shi, Yue Yu, Gongwei Zhang, Shaojia Yan, Changsheng Zhu, Yanhong Cheng and Chun Zhang
Electronics 2025, 14(15), 3046; https://doi.org/10.3390/electronics14153046 - 30 Jul 2025
Abstract
The Generalized Hough Transform (GHT) is a powerful method for rigid shape detection under rotation, scaling, translation, and partial occlusion conditions, but its four-dimensional accumulator incurs prohibitive computational and memory demands that prevent real-time deployment. To address this, we propose a framework that [...] Read more.
The Generalized Hough Transform (GHT) is a powerful method for rigid shape detection under rotation, scaling, translation, and partial occlusion conditions, but its four-dimensional accumulator incurs prohibitive computational and memory demands that prevent real-time deployment. To address this, we propose a framework that compresses the 4-D search space into a concise 2-D voting scheme by combining two-level sparse point-pair screening with an accelerated lookup. In the offline stage, template edges are extracted using an adaptive Canny operator with Otsu-determined thresholds, and gradient-direction differences for all point pairs are quantized to retain only those in the dominant bin, yielding rotation- and scale-invariant descriptors that populate a compact 2-D reference table. During the online stage, an adaptive grid selects only the highest-gradient pixels per cell as a base points, while a precomputed gradient-direction bucket table enables constant-time retrieval of compatible subpoints. Each valid base–subpoint pair is mapped to indices in the lookup table, and “fuzzy” votes are cast over a 3 × 3 neighborhood in the 2-D accumulator, whose global peak determines the object center. Evaluation on 200 real industrial parts—augmented to 1000 samples with noise, blur, occlusion, and nonlinear illumination—demonstrates that our method maintains over 90% localization accuracy, matches the classical GHT, and achieves a ten-fold speedup, outperforming IGHT and LI-GHT variants by 2–3×, thereby delivering a robust, real-time solution for industrial rigid object localization. Full article
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19 pages, 3546 KiB  
Article
Loss and Early Recovery of Biomass and Soil Organic Carbon in Restored Mangroves After Paspalum vaginatum Invasion in West Africa
by Julio César Chávez Barrera, Juan Fernando Gallardo Lancho, Robert Puschendorf and Claudia Maricusa Agraz Hernández
Resources 2025, 14(8), 122; https://doi.org/10.3390/resources14080122 - 29 Jul 2025
Viewed by 99
Abstract
Invasive plant species pose an increasing threat to mangroves globally. This study assessed the impact of Paspalum vaginatum invasion on carbon loss and early recovery following four years of restoration in a mangrove forest with Rhizophora racemosa in Benin. Organic carbon was quantified [...] Read more.
Invasive plant species pose an increasing threat to mangroves globally. This study assessed the impact of Paspalum vaginatum invasion on carbon loss and early recovery following four years of restoration in a mangrove forest with Rhizophora racemosa in Benin. Organic carbon was quantified in the total biomass, including both aboveground and belowground components, as well as in the soil to a depth of −50 cm. In addition, soil gas fluxes of CO2, CH4, and N2O were measured. Three sites were evaluated: a conserved mangrove, a site degraded by P. vaginatum, and the same site post-restoration via hydrological rehabilitation and reforestation. Invasion significantly reduced carbon storage, especially in soil, due to lower biomass, incorporation of low C/N ratio organic residues, and compaction. Restoration recovered 7.8% of the total biomass carbon compared to the conserved mangrove site, although soil organic carbon did not rise significantly in the short term. However, improvements in deep soil C/N ratios (15–30 and 30–50 cm) suggest enhanced soil organic matter recalcitrance linked to R. racemosa reforestation. Soil CO2 emissions dropped by 60% at the restored site, underscoring restoration’s potential to mitigate early carbon loss. These results highlight the need to control invasive species and suggest that restoration can generate additional social benefits. Full article
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12 pages, 2500 KiB  
Article
Deep Learning-Based Optical Camera Communication with a 2D MIMO-OOK Scheme for IoT Networks
by Huy Nguyen and Yeng Min Jang
Electronics 2025, 14(15), 3011; https://doi.org/10.3390/electronics14153011 - 29 Jul 2025
Viewed by 221
Abstract
Radio frequency (RF)-based wireless systems are broadly used in communication systems such as mobile networks, satellite links, and monitoring applications. These systems offer outstanding advantages over wired systems, particularly in terms of ease of installation. However, researchers are looking for safer alternatives as [...] Read more.
Radio frequency (RF)-based wireless systems are broadly used in communication systems such as mobile networks, satellite links, and monitoring applications. These systems offer outstanding advantages over wired systems, particularly in terms of ease of installation. However, researchers are looking for safer alternatives as a result of worries about possible health problems connected to high-frequency radiofrequency transmission. Using the visible light spectrum is one promising approach; three cutting-edge technologies are emerging in this regard: Optical Camera Communication (OCC), Light Fidelity (Li-Fi), and Visible Light Communication (VLC). In this paper, we propose a Multiple-Input Multiple-Output (MIMO) modulation technology for Internet of Things (IoT) applications, utilizing an LED array and time-domain on-off keying (OOK). The proposed system is compatible with both rolling shutter and global shutter cameras, including commercially available models such as CCTV, webcams, and smart cameras, commonly deployed in buildings and industrial environments. Despite the compact size of the LED array, we demonstrate that, by optimizing parameters such as exposure time, camera focal length, and channel coding, our system can achieve up to 20 communication links over a 20 m distance with low bit error rate. Full article
(This article belongs to the Special Issue Advances in Optical Communications and Optical Networks)
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36 pages, 27306 KiB  
Article
Integrating Social Network and Space Syntax: A Multi-Scale Diagnostic–Optimization Framework for Public Space Optimization in Nomadic Heritage Villages of Xinjiang
by Hao Liu, Rouziahong Paerhati, Nurimaimaiti Tuluxun, Saierjiang Halike, Cong Wang and Huandi Yan
Buildings 2025, 15(15), 2670; https://doi.org/10.3390/buildings15152670 - 28 Jul 2025
Viewed by 141
Abstract
Nomadic heritage villages constitute significant material cultural heritage. Under China’s cultural revitalization and rural development strategies, these villages face spatial degradation driven by tourism and urbanization. Current research predominantly employs isolated analytical approaches—space syntax often overlooks social dynamics while social network analysis (SNA) [...] Read more.
Nomadic heritage villages constitute significant material cultural heritage. Under China’s cultural revitalization and rural development strategies, these villages face spatial degradation driven by tourism and urbanization. Current research predominantly employs isolated analytical approaches—space syntax often overlooks social dynamics while social network analysis (SNA) overlooks physical interfaces—hindering the development of holistic solutions for socio-spatial resilience. This study proposes a multi-scale integrated assessment framework combining social network analysis (SNA) and space syntax to systematically evaluate public space structures in traditional nomadic villages of Xinjiang. The framework provides scientific evidence for optimizing public space design in these villages, facilitating harmonious coexistence between spatial functionality and cultural values. Focusing on three heritage villages—representing compact, linear, and dispersed morphologies—the research employs a hierarchical “village-street-node” analytical model to dissect spatial configurations and their socio-functional dynamics. Key findings include the following: Compact villages exhibit high central clustering but excessive concentration, necessitating strategies to enhance network resilience and peripheral connectivity. Linear villages demonstrate weak systemic linkages, requiring “segment-connection point supplementation” interventions to mitigate structural elongation. Dispersed villages maintain moderate network density but face challenges in visual integration and centrality, demanding targeted activation of key intersections to improve regional cohesion. By merging SNA’s social attributes with space syntax’s geometric precision, this framework bridges a methodological gap, offering comprehensive spatial optimization solutions. Practical recommendations include culturally embedded placemaking, adaptive reuse of transitional spaces, and thematic zoning to balance heritage conservation with tourism needs. Analyzing Xinjiang’s unique spatial–social interactions provides innovative insights for sustainable heritage village planning and replicable solutions for comparable global cases. Full article
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19 pages, 3720 KiB  
Article
Improved of YOLOv8-n Algorithm for Steel Surface Defect Detection
by Qingqing Xiang, Gang Wu, Zhiqiang Liu and Xudong Zeng
Metals 2025, 15(8), 843; https://doi.org/10.3390/met15080843 - 28 Jul 2025
Viewed by 184
Abstract
To address the limitations in multi-scale feature processing and illumination sensitivity of existing steel surface defect detection algorithms, we proposed ADP-YOLOv8-n, enhancing accuracy and computational efficiency through advanced feature fusion and optimized network architecture. Firstly, an adaptive weighted down-sampling (ADSConv) module was proposed, [...] Read more.
To address the limitations in multi-scale feature processing and illumination sensitivity of existing steel surface defect detection algorithms, we proposed ADP-YOLOv8-n, enhancing accuracy and computational efficiency through advanced feature fusion and optimized network architecture. Firstly, an adaptive weighted down-sampling (ADSConv) module was proposed, which improves detector adaptability to diverse defects via the weighted fusion of down-sampled feature maps. Next, the C2f_DWR module was proposed, integrating optimized C2F architecture with a streamlined DWR design to enhance feature extraction efficiency while reducing computational complexity. Then, a Multi-Scale-Focus Diffusion Pyramid was designed to adaptively handle multi-scale object detection by dynamically adjusting feature fusion, thus reducing feature redundancy and information loss while maintaining a balance between detailed and global information. Experiments demonstrate that the proposed ADP-YOLOv8-n detection algorithm achieves superior performance, effectively balancing detection accuracy, inference speed, and model compactness. Full article
(This article belongs to the Special Issue Nondestructive Testing Methods for Metallic Material)
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24 pages, 5054 KiB  
Article
Technology for the Production of Energy Briquettes from Bean Stalks
by Krzysztof Mudryk, Jarosław Frączek, Joanna Leszczyńska and Mateusz Krotowski
Energies 2025, 18(15), 4009; https://doi.org/10.3390/en18154009 - 28 Jul 2025
Viewed by 194
Abstract
Biomass is gaining increasing importance as a renewable energy source in the global energy mix, offering a viable alternative to fossil fuels and contributing to the decarbonization of the energy sector. Among various types of biomass, agricultural residues such as bean stalks represent [...] Read more.
Biomass is gaining increasing importance as a renewable energy source in the global energy mix, offering a viable alternative to fossil fuels and contributing to the decarbonization of the energy sector. Among various types of biomass, agricultural residues such as bean stalks represent a promising feedstock for the production of solid biofuels. This study analyzes the impact of particle size and selected briquetting parameters (pressure and temperature) on the physical quality of briquettes made from bean stalks. The experimental procedure included milling the raw material using #8, #12, and #16 mesh screens, followed by compaction under pressures of 27, 37, and 47 MPa. Additionally, the briquetting die was heated to 90 °C to improve the mechanical durability of the briquettes. The results showed that both particle size and die temperature significantly influenced the quality of the produced briquettes. Briquettes made from the 16 mm fraction, compacted at 60 °C and 27 MPa, exhibited a durability of 55.76%, which increased to 82.02% when the die temperature was raised to 90 °C. Further improvements were achieved by removing particles smaller than 1 mm. However, these measures did not enable achieving a net calorific value above 14.5 MJ·kg−1. Therefore, additional work was undertaken, involving the addition of biomass with higher calorific value to the bean stalk feedstock. In the study, maize straw and miscanthus straw were used as supplementary substrates. The results allowed for determining their minimum proportions required to exceed the 14.5 MJ·kg−1 threshold. In conclusion, bean stalks can serve as a viable feedstock for the production of solid biofuels, especially when combined with other biomass types possessing more favorable energy parameters. Their utilization aligns with the concept of managing local agricultural residues within decentralized energy systems and supports the development of sustainable bioenergy solutions. Full article
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27 pages, 1739 KiB  
Article
Hybrid Small Modular Reactor—Renewable Systems for Smart Cities: A Simulation-Based Assessment for Clean and Resilient Urban Energy Transitions
by Nikolay Hinov
Energies 2025, 18(15), 3993; https://doi.org/10.3390/en18153993 - 27 Jul 2025
Viewed by 401
Abstract
The global transition to clean energy necessitates integrated solutions that ensure both environmental sustainability and energy security. This paper proposes a scenario-based modeling framework for urban hybrid energy systems combining small modular reactors (SMRs), photovoltaic (PV) generation, and battery storage within a smart [...] Read more.
The global transition to clean energy necessitates integrated solutions that ensure both environmental sustainability and energy security. This paper proposes a scenario-based modeling framework for urban hybrid energy systems combining small modular reactors (SMRs), photovoltaic (PV) generation, and battery storage within a smart grid architecture. SMRs offer compact, low-carbon, and reliable baseload power suitable for urban environments, while PV and storage enhance system flexibility and renewable integration. Six energy mix scenarios are evaluated using a lifecycle-based cost model that incorporates both capital expenditures (CAPEX) and cumulative carbon costs over a 25-year horizon. The modeling results demonstrate that hybrid SMR–renewable systems—particularly those with high nuclear shares—can reduce lifecycle CO2 emissions by over 90%, while maintaining long-term economic viability under carbon pricing assumptions. Scenario C, which combines 50% SMR, 40% PV, and 10% battery, emerges as a balanced configuration offering deep decarbonization with moderate investment levels. The proposed framework highlights key trade-offs between emissions and capital cost and seeking resilient and scalable pathways to support the global clean energy transition and net-zero commitments. Full article
(This article belongs to the Special Issue Challenges and Opportunities in the Global Clean Energy Transition)
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26 pages, 16392 KiB  
Article
TOSD: A Hierarchical Object-Centric Descriptor Integrating Shape, Color, and Topology
by Jun-Hyeon Choi, Jeong-Won Pyo, Ye-Chan An and Tae-Yong Kuc
Sensors 2025, 25(15), 4614; https://doi.org/10.3390/s25154614 - 25 Jul 2025
Viewed by 273
Abstract
This paper introduces a hierarchical object-centric descriptor framework called TOSD (Triplet Object-Centric Semantic Descriptor). The goal of this method is to overcome the limitations of existing pixel-based and global feature embedding approaches. To this end, the framework adopts a hierarchical representation that is [...] Read more.
This paper introduces a hierarchical object-centric descriptor framework called TOSD (Triplet Object-Centric Semantic Descriptor). The goal of this method is to overcome the limitations of existing pixel-based and global feature embedding approaches. To this end, the framework adopts a hierarchical representation that is explicitly designed for multi-level reasoning. TOSD combines shape, color, and topological information without depending on predefined class labels. The shape descriptor captures the geometric configuration of each object. The color descriptor focuses on internal appearance by extracting normalized color features. The topology descriptor models the spatial and semantic relationships between objects in a scene. These components are integrated at both object and scene levels to produce compact and consistent embeddings. The resulting representation covers three levels of abstraction: low-level pixel details, mid-level object features, and high-level semantic structure. This hierarchical organization makes it possible to represent both local cues and global context in a unified form. We evaluate the proposed method on multiple vision tasks. The results show that TOSD performs competitively compared to baseline methods, while maintaining robustness in challenging cases such as occlusion and viewpoint changes. The framework is applicable to visual odometry, SLAM, object tracking, global localization, scene clustering, and image retrieval. In addition, this work extends our previous research on the Semantic Modeling Framework, which represents environments using layered structures of places, objects, and their ontological relations. Full article
(This article belongs to the Special Issue Event-Driven Vision Sensor Architectures and Application Scenarios)
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35 pages, 4256 KiB  
Article
Automated Segmentation and Morphometric Analysis of Thioflavin-S-Stained Amyloid Deposits in Alzheimer’s Disease Brains and Age-Matched Controls Using Weakly Supervised Deep Learning
by Gábor Barczánfalvi, Tibor Nyári, József Tolnai, László Tiszlavicz, Balázs Gulyás and Karoly Gulya
Int. J. Mol. Sci. 2025, 26(15), 7134; https://doi.org/10.3390/ijms26157134 - 24 Jul 2025
Viewed by 340
Abstract
Alzheimer’s disease (AD) involves the accumulation of amyloid-β (Aβ) plaques, whose quantification plays a central role in understanding disease progression. Automated segmentation of Aβ deposits in histopathological micrographs enables large-scale analyses but is hindered by the high cost of detailed pixel-level annotations. Weakly [...] Read more.
Alzheimer’s disease (AD) involves the accumulation of amyloid-β (Aβ) plaques, whose quantification plays a central role in understanding disease progression. Automated segmentation of Aβ deposits in histopathological micrographs enables large-scale analyses but is hindered by the high cost of detailed pixel-level annotations. Weakly supervised learning offers a promising alternative by leveraging coarse or indirect labels to reduce the annotation burden. We evaluated a weakly supervised approach to segment and analyze thioflavin-S-positive parenchymal amyloid pathology in AD and age-matched brains. Our pipeline integrates three key components, each designed to operate under weak supervision. First, robust preprocessing (including retrospective multi-image illumination correction and gradient-based background estimation) was applied to enhance image fidelity and support training, as models rely more on image features. Second, class activation maps (CAMs), generated by a compact deep classifier SqueezeNet, were used to identify, and coarsely localize amyloid-rich parenchymal regions from patch-wise image labels, serving as spatial priors for subsequent refinement without requiring dense pixel-level annotations. Third, a patch-based convolutional neural network, U-Net, was trained on synthetic data generated from micrographs based on CAM-derived pseudo-labels via an extensive object-level augmentation strategy, enabling refined whole-image semantic segmentation and generalization across diverse spatial configurations. To ensure robustness and unbiased evaluation, we assessed the segmentation performance of the entire framework using patient-wise group k-fold cross-validation, explicitly modeling generalization across unseen individuals, critical in clinical scenarios. Despite relying on weak labels, the integrated pipeline achieved strong segmentation performance with an average Dice similarity coefficient (≈0.763) and Jaccard index (≈0.639), widely accepted metrics for assessing segmentation quality in medical image analysis. The resulting segmentations were also visually coherent, demonstrating that weakly supervised segmentation is a viable alternative in histopathology, where acquiring dense annotations is prohibitively labor-intensive and time-consuming. Subsequent morphometric analyses on automatically segmented Aβ deposits revealed size-, structural complexity-, and global geometry-related differences across brain regions and cognitive status. These findings confirm that deposit architecture exhibits region-specific patterns and reflects underlying neurodegenerative processes, thereby highlighting the biological relevance and practical applicability of the proposed image-processing pipeline for morphometric analysis. Full article
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18 pages, 1178 KiB  
Article
Prevalence and Antimicrobial Resistance of Gram-Negative ESKAPE Pathogens Isolated from Tertiary Care Hospital in Eastern India
by Paramjyoti Rana, Sweta Padma Routray, Surajit De Mandal, Rajashree Panigrahy, Anjan Kumar Sahoo and Enketeswara Subudhi
Appl. Sci. 2025, 15(15), 8171; https://doi.org/10.3390/app15158171 - 23 Jul 2025
Viewed by 234
Abstract
Gram-negative ESKAPE pathogens pose major challenges to global public health due to their multidrug resistance and virulence. The present study aimed to study the prevalence and resistance of Gram-negative ESKAPE pathogens at a tertiary care hospital in Eastern India. A retrospective analysis was [...] Read more.
Gram-negative ESKAPE pathogens pose major challenges to global public health due to their multidrug resistance and virulence. The present study aimed to study the prevalence and resistance of Gram-negative ESKAPE pathogens at a tertiary care hospital in Eastern India. A retrospective analysis was conducted on 7343 non-duplicate isolates collected between January 2023 and December 2024. The bacterial isolates and their antibiotic susceptibility testing were identified using Kirby–Bauer disk diffusion techniques and the VITEK 2 Compact system, adhering to CLSI 2025 and EUCAST 2024 guidelines. Our findings indicate that Klebsiella pneumoniae was the most common isolate, followed by Pseudomonas aeruginosa, Acinetobacter baumannii complex, and Enterobacter cloacae complex, predominantly affecting male patients aged 18–64 years. Importantly, most of these isolates exhibit increased multidrug resistance (MDR) to several key antibiotics, including β-lactams and carbapenems, which further complicates the treatment process. The analysis of seasonal dynamics revealed an increased abundance of infections in monsoon and post-monsoon periods. These findings will be useful in understanding AMR in hospital environments and in developing strategies to prevent the occurrence and spread of antimicrobial resistance among pathogens. Full article
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34 pages, 2669 KiB  
Article
A Novel Quantum Epigenetic Algorithm for Adaptive Cybersecurity Threat Detection
by Salam Al-E’mari, Yousef Sanjalawe and Salam Fraihat
AI 2025, 6(8), 165; https://doi.org/10.3390/ai6080165 - 22 Jul 2025
Viewed by 321
Abstract
The escalating sophistication of cyber threats underscores the critical need for intelligent and adaptive intrusion detection systems (IDSs) to identify known and novel attack vectors in real time. Feature selection is a key enabler of performance in machine learning-based IDSs, as it reduces [...] Read more.
The escalating sophistication of cyber threats underscores the critical need for intelligent and adaptive intrusion detection systems (IDSs) to identify known and novel attack vectors in real time. Feature selection is a key enabler of performance in machine learning-based IDSs, as it reduces the input dimensionality, enhances the detection accuracy, and lowers the computational latency. This paper introduces a novel optimization framework called Quantum Epigenetic Algorithm (QEA), which synergistically combines quantum-inspired probabilistic representation with biologically motivated epigenetic gene regulation to perform efficient and adaptive feature selection. The algorithm balances global exploration and local exploitation by leveraging quantum superposition for diverse candidate generation while dynamically adjusting gene expression through an epigenetic activation mechanism. A multi-objective fitness function guides the search process by optimizing the detection accuracy, false positive rate, inference latency, and model compactness. The QEA was evaluated across four benchmark datasets—UNSW-NB15, CIC-IDS2017, CSE-CIC-IDS2018, and TON_IoT—and consistently outperformed baseline methods, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Quantum Genetic Algorithm (QGA). Notably, QEA achieved the highest classification accuracy (up to 97.12%), the lowest false positive rates (as low as 1.68%), and selected significantly fewer features (e.g., 18 on TON_IoT) while maintaining near real-time latency. These results demonstrate the robustness, efficiency, and scalability of QEA for real-time intrusion detection in dynamic and resource-constrained cybersecurity environments. Full article
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44 pages, 15871 KiB  
Article
Space Gene Quantification and Mapping of Traditional Settlements in Jiangnan Water Town: Evidence from Yubei Village in the Nanxi River Basin
by Yuhao Huang, Zibin Ye, Qian Zhang, Yile Chen and Wenkun Wu
Buildings 2025, 15(14), 2571; https://doi.org/10.3390/buildings15142571 - 21 Jul 2025
Viewed by 290
Abstract
The spatial genes of rural settlements show a lot of different traditional settlement traits, which makes them a great starting point for studying rural spatial morphology. However, qualitative and macro-regional statistical indicators are usually used to find and extract rural settlement spatial genes. [...] Read more.
The spatial genes of rural settlements show a lot of different traditional settlement traits, which makes them a great starting point for studying rural spatial morphology. However, qualitative and macro-regional statistical indicators are usually used to find and extract rural settlement spatial genes. Taking Yubei Village in the Nanxi River Basin as an example, this study combined remote sensing images, real-time drone mapping, GIS (geographic information system), and space syntax, extracted 12 key indicators from five dimensions (landform and water features (environment), boundary morphology, spatial structure, street scale, and building scale), and quantitatively “decoded” the spatial genes of the settlement. The results showed that (1) the settlement is a “three mountains and one water” pattern, with cultivated land accounting for 37.4% and forest land accounting for 34.3% of the area within the 500 m buffer zone, while the landscape spatial diversity index (LSDI) is 0.708. (2) The boundary morphology is compact and agglomerated, and locally complex but overall orderly, with an aspect ratio of 1.04, a comprehensive morphological index of 1.53, and a comprehensive fractal dimension of 1.31. (3) The settlement is a “clan core–radial lane” network: the global integration degree of the axis to the holy hall is the highest (0.707), and the local integration degree R3 peak of the six-room ancestral hall reaches 2.255. Most lane widths are concentrated between 1.2 and 2.8 m, and the eaves are mostly higher than 4 m, forming a typical “narrow lanes and high houses” water town streetscape. (4) The architectural style is a combination of black bricks and gray tiles, gable roofs and horsehead walls, and “I”-shaped planes (63.95%). This study ultimately constructed a settlement space gene map and digital library, providing a replicable quantitative process for the diagnosis of Jiangnan water town settlements and heritage protection planning. Full article
(This article belongs to the Section Architectural Design, Urban Science, and Real Estate)
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14 pages, 1393 KiB  
Article
Mitigating Water Stress and Enhancing Aesthetic Quality in Off-Season Potted Curcuma cv. ‘Jasmine Pink’ via Potassium Silicate Under Deficit Irrigation
by Vannak Sour, Anoma Dongsansuk, Supat Isarangkool Na Ayutthaya, Soraya Ruamrungsri and Panupon Hongpakdee
Horticulturae 2025, 11(7), 856; https://doi.org/10.3390/horticulturae11070856 - 20 Jul 2025
Viewed by 357
Abstract
Curcuma spp. is a popular ornamental crop valued for its vibrant appearance and suitability for both regular and off-season production. As global emphasis on freshwater conservation increases and with a demand for compact potted plants, reducing water use while maintaining high aesthetic quality [...] Read more.
Curcuma spp. is a popular ornamental crop valued for its vibrant appearance and suitability for both regular and off-season production. As global emphasis on freshwater conservation increases and with a demand for compact potted plants, reducing water use while maintaining high aesthetic quality presents a key challenge for horticulturists. Potassium silicate (PS) has been proposed as a foliar spray to alleviate plant water stress. This study aimed to evaluate the effects of PS on growth, ornamental traits, and photosynthetic parameters of off-season potted Curcuma cv. ‘Jasmine Pink’ under deficit irrigation (DI). Plants were subjected to three treatments in a completely randomized design: 100% crop evapotranspiration (ETc), 50% ETc, and 50% ETc with 1000 ppm PS (weekly sprayed on leaves for 11 weeks). Both DI treatments (50% ETc and 50% ETc + PS) reduced plant height by 7.39% and 9.17%, leaf number by 16.99% and 7.03%, and total biomass by 21.13% and 20.58%, respectively, compared to 100% ETc. Notably, under DI, PS-treated plants maintained several parameters equivalent to the 100% ETc treatment, including flower bud emergence, blooming period, green bract number, effective quantum yield of PSII (ΔF/Fm′), and electron transport rate (ETR). In addition, PS application increased leaf area by 8.11% and compactness index by 9.80% relative to untreated plants. Photosynthetic rate, ΔF/Fm′, and ETR increased by 31.52%, 13.63%, and 9.93%, while non-photochemical quenching decreased by 16.51% under water-limited conditions. These findings demonstrate that integrating deficit irrigation with PS foliar application can enhance water use efficiency and maintain ornamental quality in off-season potted Curcuma, promoting sustainable water management in horticulture. Full article
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18 pages, 2028 KiB  
Article
Research on Single-Tree Segmentation Method for Forest 3D Reconstruction Point Cloud Based on Attention Mechanism
by Lishuo Huo, Zhao Chen, Lingnan Dai, Dianchang Wang and Xinrong Zhao
Forests 2025, 16(7), 1192; https://doi.org/10.3390/f16071192 - 19 Jul 2025
Viewed by 228
Abstract
The segmentation of individual trees holds considerable significance in the investigation and management of forest resources. Utilizing smartphone-captured imagery combined with image-based 3D reconstruction techniques to generate corresponding point cloud data can serve as a more accessible and potentially cost-efficient alternative for data [...] Read more.
The segmentation of individual trees holds considerable significance in the investigation and management of forest resources. Utilizing smartphone-captured imagery combined with image-based 3D reconstruction techniques to generate corresponding point cloud data can serve as a more accessible and potentially cost-efficient alternative for data acquisition compared to conventional LiDAR methods. In this study, we present a Sparse 3D U-Net framework for single-tree segmentation which is predicated on a multi-head attention mechanism. The mechanism functions by projecting the input data into multiple subspaces—referred to as “heads”—followed by independent attention computation within each subspace. Subsequently, the outputs are aggregated to form a comprehensive representation. As a result, multi-head attention facilitates the model’s ability to capture diverse contextual information, thereby enhancing performance across a wide range of applications. This framework enables efficient, intelligent, and end-to-end instance segmentation of forest point cloud data through the integration of multi-scale features and global contextual information. The introduction of an iterative mechanism at the attention layer allows the model to learn more compact feature representations, thereby significantly enhancing its convergence speed. In this study, Dongsheng Bajia Country Park and Jiufeng National Forest Park, situated in Haidian District, Beijing, China, were selected as the designated test sites. Eight representative sample plots within these areas were systematically sampled. Forest stand sequential photographs were captured using an iPhone, and these images were processed to generate corresponding point cloud data for the respective sample plots. This methodology was employed to comprehensively assess the model’s capability for single-tree segmentation. Furthermore, the generalization performance of the proposed model was validated using the publicly available dataset TreeLearn. The model’s advantages were demonstrated across multiple aspects, including data processing efficiency, training robustness, and single-tree segmentation speed. The proposed method achieved an F1 score of 91.58% on the customized dataset. On the TreeLearn dataset, the method attained an F1 score of 97.12%. Full article
(This article belongs to the Section Forest Inventory, Modeling and Remote Sensing)
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