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25 pages, 6330 KiB  
Article
FSDN-DETR: Enhancing Fuzzy Systems Adapter with DeNoising Anchor Boxes for Transfer Learning in Small Object Detection
by Zhijie Li, Jiahui Zhang, Yingjie Zhang, Dawei Yan, Xing Zhang, Marcin Woźniak and Wei Dong
Mathematics 2025, 13(2), 287; https://doi.org/10.3390/math13020287 - 17 Jan 2025
Viewed by 345
Abstract
The advancement of Transformer models in computer vision has rapidly spurred numerous Transformer-based object detection approaches, such as DEtection TRansformer. Although DETR’s self-attention mechanism effectively captures the global context, it struggles with fine-grained detail detection, limiting its efficacy in small object detection where [...] Read more.
The advancement of Transformer models in computer vision has rapidly spurred numerous Transformer-based object detection approaches, such as DEtection TRansformer. Although DETR’s self-attention mechanism effectively captures the global context, it struggles with fine-grained detail detection, limiting its efficacy in small object detection where noise can easily obscure or confuse small targets. To address these issues, we propose Fuzzy System DNN-DETR involving two key modules: Fuzzy Adapter Transformer Encoder and Fuzzy Denoising Transformer Decoder. The fuzzy Adapter Transformer Encoder utilizes adaptive fuzzy membership functions and rule-based smoothing to preserve critical details, such as edges and textures, while mitigating the loss of fine details in global feature processing. Meanwhile, the Fuzzy Denoising Transformer Decoder effectively reduces noise interference and enhances fine-grained feature capture, eliminating redundant computations in irrelevant regions. This approach achieves a balance between computational efficiency for medium-resolution images and the accuracy required for small object detection. Our architecture also employs adapter modules to reduce re-training costs, and a two-stage fine-tuning strategy adapts fuzzy modules to specific domains before harmonizing the model with task-specific adjustments. Experiments on the COCO and AI-TOD-V2 datasets show that FSDN-DETR achieves an approximately 20% improvement in average precision for very small objects, surpassing state-of-the-art models and demonstrating robustness and reliability for small object detection in complex environments. Full article
(This article belongs to the Special Issue Image Processing and Machine Learning with Applications)
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23 pages, 768 KiB  
Article
Robust Momentum-Enhanced Non-Negative Tensor Factorization for Accurate Reconstruction of Incomplete Power Consumption Data
by Dengyu Shi and Tangtang Xie
Electronics 2025, 14(2), 351; https://doi.org/10.3390/electronics14020351 - 17 Jan 2025
Viewed by 411
Abstract
Power consumption (PC) data are fundamental for optimizing energy use and managing industrial operations. However, with the widespread adoption of data-driven technologies in the energy sector, maintaining the integrity and quality of these data has become a significant challenge. Missing or incomplete data, [...] Read more.
Power consumption (PC) data are fundamental for optimizing energy use and managing industrial operations. However, with the widespread adoption of data-driven technologies in the energy sector, maintaining the integrity and quality of these data has become a significant challenge. Missing or incomplete data, often caused by equipment failures or communication disruptions, can severely affect the accuracy and reliability of data analyses, ultimately leading to poor decision-making and increased operational costs. To address this, we propose a Robust Momentum-Enhanced Non-Negative Tensor Factorization (RMNTF) model, which integrates three key innovations. First, the model utilizes adversarial loss and L2 regularization to enhance its robustness and improve its performance when dealing with incomplete data. Second, a sigmoid function is employed to ensure that the results remain non-negative, aligning with the inherent characteristics of PC data and improving the quality of the analysis. Finally, momentum optimization is applied to accelerate the convergence process, significantly reducing computational time. Experiments conducted on two publicly available PC datasets, with data densities of 6.65% and 4.80%, show that RMNTF outperforms state-of-the-art methods, achieving an average reduction of 16.20% in imputation errors and an average improvement of 68.36% in computational efficiency. These results highlight the model’s effectiveness in handling sparse and incomplete data, ensuring that the reconstructed data can support critical tasks like energy optimization, smart grid maintenance, and predictive analytics. Full article
(This article belongs to the Special Issue Intelligent Data Analysis and Learning)
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25 pages, 2278 KiB  
Article
CrowdBA: A Low-Cost Quality-Driven Crowdsourcing Architecture for Bounding Box Annotation Based on Blockchain
by Rongxin Guo, Shenglong Liao and Jianqing Zhu
Electronics 2025, 14(2), 345; https://doi.org/10.3390/electronics14020345 - 17 Jan 2025
Viewed by 356
Abstract
Many blockchain-based crowdsourcing frameworks currently struggle to address the high costs associated with on-chain storage and computation effectively, and they lack a quality-driven incentive mechanism tailored to bounding box annotation scenarios. To address these challenges, this paper proposes CrowdBA: A low-cost, quality-driven crowdsourcing [...] Read more.
Many blockchain-based crowdsourcing frameworks currently struggle to address the high costs associated with on-chain storage and computation effectively, and they lack a quality-driven incentive mechanism tailored to bounding box annotation scenarios. To address these challenges, this paper proposes CrowdBA: A low-cost, quality-driven crowdsourcing architecture. The CrowdBA utilizes the Ethereum public blockchain as the foundational architecture and develops corresponding smart contracts. First, by integrating Ethereum with the InterPlanetary File System (IPFS), storage and computation processes are shifted off-chain, effectively addressing the high costs associated with data storage and computation on public blockchains. Additionally, the CrowdBA introduces a Dynamic Intersection over the union-weighted bounding box fusion (DWBF) algorithm, which assigns dynamic weights based on IoU to infer true bounding boxes, thereby assessing each worker’s annotation quality. Annotation quality then serves as a key criterion for incentive distribution, ensuring fair and appropriate compensation for all contributors. Experimental results demonstrate that the operational costs of each smart contract function remain within reasonable limits; the off-chain storage and computation approach significantly reduces storage and computation expenses, and the DWBF algorithm shows marked improvements in accuracy and robustness over other bounding box fusion methods. Full article
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18 pages, 5955 KiB  
Article
G-UNETR++: A Gradient-Enhanced Network for Accurate and Robust Liver Segmentation from Computed Tomography Images
by Seungyoo Lee, Kyujin Han, Hangyeul Shin, Harin Park, Seunghyon Kim, Jeonghun Kim, Xiaopeng Yang, Jae Do Yang, Hee Chul Yu and Heecheon You
Appl. Sci. 2025, 15(2), 837; https://doi.org/10.3390/app15020837 - 16 Jan 2025
Viewed by 289
Abstract
Accurate liver segmentation from computed tomography (CT) scans is essential for liver cancer diagnosis and liver surgery planning. Convolutional neural network (CNN)-based models have limited segmentation performance due to their localized receptive fields. Hybrid models incorporating CNNs and transformers that can capture long-range [...] Read more.
Accurate liver segmentation from computed tomography (CT) scans is essential for liver cancer diagnosis and liver surgery planning. Convolutional neural network (CNN)-based models have limited segmentation performance due to their localized receptive fields. Hybrid models incorporating CNNs and transformers that can capture long-range dependencies have shown promising performance in liver segmentation with the cost of high model complexity. Therefore, a new network architecture named G-UNETR++ is proposed to improve accuracy in liver segmentation with moderate model complexity. Two gradient-based encoders that take the second-order partial derivatives (the first two elements from the last column of the Hessian matrix of a CT scan) as inputs are proposed to learn the 3D geometric features such as the boundaries between different organs and tissues. In addition, a hybrid loss function that combines dice loss, cross-entropy loss, and Hausdorff distance loss is designed to address class imbalance and improve segmentation performance in challenging cases. The proposed method was evaluated on three public datasets, the Liver Tumor Segmentation (LiTS) dataset, the 3D Image Reconstruction for Comparison of Algorithms Database (3D-IRCADb), and the Segmentation of the Liver Competition 2007 (Sliver07) dataset, and achieved 97.38%, 97.50%, and 97.32% in terms of the dice similarity coefficient for liver segmentation on the three datasets, respectively. The proposed method outperformed the other state-of-the-art models on the three datasets, which demonstrated the strong effectiveness, robustness, and generalizability of the proposed method in liver segmentation. Full article
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20 pages, 2004 KiB  
Article
A Dual-Stage Processing Architecture for Unmanned Aerial Vehicle Object Detection and Tracking Using Lightweight Onboard and Ground Server Computations
by Odysseas Ntousis, Evangelos Makris, Panayiotis Tsanakas and Christos Pavlatos
Technologies 2025, 13(1), 35; https://doi.org/10.3390/technologies13010035 - 16 Jan 2025
Viewed by 384
Abstract
UAVs are widely used for multiple tasks, which in many cases require autonomous processing and decision making. This autonomous function often requires significant computational capabilities that cannot be integrated into the UAV due to weight or cost limitations, making the distribution of the [...] Read more.
UAVs are widely used for multiple tasks, which in many cases require autonomous processing and decision making. This autonomous function often requires significant computational capabilities that cannot be integrated into the UAV due to weight or cost limitations, making the distribution of the workload and the combination of the results produced necessary. In this paper, a dual-stage processing architecture for object detection and tracking in Unmanned Aerial Vehicles (UAVs) is presented, focusing on efficient resource utilization and real-time performance. The proposed system delegates lightweight detection tasks to onboard hardware while offloading computationally intensive processes to a ground server. The UAV is equipped with a Raspberry Pi for onboard data processing, utilizing an Intel Neural Compute Stick 2 (NCS2) for accelerated object detection. Specifically, YOLOv5n is selected as the onboard model. The UAV transmits selected frames to the ground server, which handles advanced tracking, trajectory prediction, and target repositioning using state-of-the-art deep learning models. Communication between the UAV and the server is maintained through a high-speed Wi-Fi link, with a fallback to a 4G connection when needed. The ground server, equipped with an NVIDIA A40 GPU, employs YOLOv8x for object detection and DeepSORT for multi-object tracking. The proposed architecture ensures real-time tracking with minimal latency, making it suitable for mission-critical UAV applications such as surveillance and search and rescue. The results demonstrate the system’s robustness in various environments, highlighting its potential for effective object tracking under limited onboard computational resources. The system achieves recall and accuracy scores as high as 0.53 and 0.74, respectively, using the remote server, and is capable of re-identifying a significant portion of objects of interest lost by the onboard system, measured at approximately 70%. Full article
(This article belongs to the Section Information and Communication Technologies)
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22 pages, 9620 KiB  
Article
New Approach of Blind Adaptive Equalizer Based on Genetic Algorithms
by Caroline A. D. Silva and Marcelo A. C. Fernandes
Telecom 2025, 6(1), 6; https://doi.org/10.3390/telecom6010006 - 10 Jan 2025
Viewed by 338
Abstract
This paper introduces a novel approach to blind adaptive equalization for digital communication systems using genetic algorithms (GAs). Unlike traditional methods that rely on linear programming and suffer from local minima issues, this technique utilizes a stochastic linear programming cost function with GAs [...] Read more.
This paper introduces a novel approach to blind adaptive equalization for digital communication systems using genetic algorithms (GAs). Unlike traditional methods that rely on linear programming and suffer from local minima issues, this technique utilizes a stochastic linear programming cost function with GAs for robust optimization. The proposed method termed Blind Linear Equalizer based on genetic algorithm (BLE-GA) enhances performance by leveraging a GA’s ability to handle stochastic variables, offering rapid convergence and resilience against signal noise and inter-symbol interference. Extensive simulations demonstrate the effectiveness of BLE-GA across different QAM systems, outperforming conventional techniques like the Constant Modulus Algorithm in scenarios with high modulation levels. This study validates the potential of using GAs in adaptive blind equalization to achieve reliable and efficient communication, even in complex and noisy channel conditions. Full article
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23 pages, 696 KiB  
Review
The Rise of Fentanyl: Molecular Aspects and Forensic Investigations
by Cecilia Barletta, Virginia Di Natale, Massimiliano Esposito, Mario Chisari, Giuseppe Cocimano, Lucio Di Mauro, Monica Salerno and Francesco Sessa
Int. J. Mol. Sci. 2025, 26(2), 444; https://doi.org/10.3390/ijms26020444 - 7 Jan 2025
Viewed by 669
Abstract
Fentanyl is a synthetic opioid widely used for its potent analgesic effects in chronic pain management and intraoperative anesthesia. However, its high potency, low cost, and accessibility have also made it a significant drug of abuse, contributing to the global opioid epidemic. This [...] Read more.
Fentanyl is a synthetic opioid widely used for its potent analgesic effects in chronic pain management and intraoperative anesthesia. However, its high potency, low cost, and accessibility have also made it a significant drug of abuse, contributing to the global opioid epidemic. This review aims to provide an in-depth analysis of fentanyl’s medical applications, pharmacokinetics, metabolism, and pharmacogenetics while examining its adverse effects and forensic implications. Special attention is given to its misuse, polydrug interactions, and the challenges in determining the cause of death in fentanyl-related fatalities. Fentanyl misuse has escalated dramatically, driven by its substitution for heroin and its availability through online platforms, including the dark web. Polydrug use, where fentanyl is combined with substances like xylazine, alcohol, benzodiazepines, or cocaine, exacerbates its toxicity and increases the risk of fatal outcomes. Fentanyl undergoes rapid distribution, metabolism by CYP3A4 into inactive metabolites, and renal excretion. Genetic polymorphisms in CYP3A4, OPRM1, and ABCB1 significantly influence individual responses to fentanyl, affecting its efficacy and potential for toxicity. Fentanyl’s side effects include respiratory depression, cardiac arrhythmias, gastrointestinal dysfunction, and neurocognitive impairments. Chronic misuse disrupts brain function, contributes to mental health disorders, and poses risks for younger and older populations alike. Fentanyl-related deaths require comprehensive forensic investigations, including judicial inspections, autopsies, and toxicological analyses. Additionally, the co-administration of xylazine presents distinct challenges for the scientific community. Histological and immunohistochemical studies are essential for understanding organ-specific damage, while pharmacogenetic testing can identify individual susceptibilities. The growing prevalence of fentanyl abuse highlights the need for robust forensic protocols, advanced research into its pharmacogenetic variability, and strategies to mitigate its misuse. International collaboration, public education, and harm reduction measures are critical for addressing the fentanyl crisis effectively. Full article
(This article belongs to the Special Issue Pharmacogenetics and Pharmacogenomics)
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28 pages, 43934 KiB  
Article
A Cross-Stage Focused Small Object Detection Network for Unmanned Aerial Vehicle Assisted Maritime Applications
by Gege Ding, Jiayue Liu, Dongsheng Li, Xiaming Fu, Yucheng Zhou, Mingrui Zhang, Wantong Li, Yanjuan Wang, Chunxu Li and Xiongfei Geng
J. Mar. Sci. Eng. 2025, 13(1), 82; https://doi.org/10.3390/jmse13010082 - 5 Jan 2025
Viewed by 669
Abstract
The application potential of unmanned aerial vehicles (UAVs) in marine search and rescue is especially of concern for the ongoing advancement of visual recognition technology and image processing technology. Limited computing resources, insufficient pixel representation for small objects in high-altitude images, and challenging [...] Read more.
The application potential of unmanned aerial vehicles (UAVs) in marine search and rescue is especially of concern for the ongoing advancement of visual recognition technology and image processing technology. Limited computing resources, insufficient pixel representation for small objects in high-altitude images, and challenging visibility conditions hinder UAVs’ target recognition performance in maritime search and rescue operations, highlighting the need for further optimization and enhancement. This study introduces an innovative detection framework, CFSD-UAVNet, designed to boost the accuracy of detecting minor objects within imagery captured from elevated altitudes. To improve the performance of the feature pyramid network (FPN) and path aggregation network (PAN), a newly designed PHead structure was proposed, focusing on better leveraging shallow features. Then, structural pruning was applied to refine the model and enhance its capability in detecting small objects. Moreover, to conserve computational resources, a lightweight CED module was introduced to reduce parameters and conserve the computing resources of the UAV. At the same time, in each detection layer, a lightweight CRE module was integrated, leveraging attention mechanisms and detection heads to enhance precision for small object detection. Finally, to enhance the model’s robustness, WIoUv2 loss function was employed, ensuring a balanced treatment of positive and negative samples. The CFSD-UAVNet model was evaluated on the publicly available SeaDronesSee maritime dataset and compared with other cutting-edge algorithms. The experimental results showed that the CFSD-UAVNet model achieved an mAP@50 of 80.1% with only 1.7 M parameters and a computational cost of 10.2 G, marking a 12.1% improvement over YOLOv8 and a 4.6% increase compared to DETR. The novel CFSD-UAVNet model effectively balances the limitations of scenarios and detection accuracy, demonstrating application potential and value in the field of UAV-assisted maritime search and rescue. Full article
(This article belongs to the Section Ocean Engineering)
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30 pages, 2499 KiB  
Article
Machine Selection for Inventory Tracking with a Continuous Intuitionistic Fuzzy Approach
by Ufuk Cebeci, Ugur Simsir and Onur Dogan
Appl. Sci. 2025, 15(1), 425; https://doi.org/10.3390/app15010425 - 5 Jan 2025
Viewed by 461
Abstract
Today, businesses are adopting digital transformation strategies to make their production processes more agile, efficient, and sustainable. At the same time, lean manufacturing principles aim to create value by reducing waste in production processes. In this context, it is important that the machine [...] Read more.
Today, businesses are adopting digital transformation strategies to make their production processes more agile, efficient, and sustainable. At the same time, lean manufacturing principles aim to create value by reducing waste in production processes. In this context, it is important that the machine to be selected for inventory tracking can meet both the technological features suitable for digital transformation goals and the operational efficiency criteria required by lean manufacturing. In this study, multi-criteria decision-making methods were used to select the most suitable machine for inventory tracking based on digital transformation and lean manufacturing perspectives. This study applies a framework that integrates the Continuous Intuitionistic Fuzzy Analytic Hierarchy Process (CINFU AHP) and the Continuous Intuitionistic Fuzzy Combinative Distance-Based Assessment (CINFU CODAS) methods to select the most suitable machine for inventory tracking. The framework contributes to lean manufacturing by providing actionable insights and robust sensitivity analyses, ensuring decision-making reliability under fluctuating conditions. The CINFU AHP method determines the relative importance of each criterion by incorporating expert opinions. Six criteria, Speed (C1), Setup Time (C2), Ease to Operate and Move (C3), Ability to Handle Multiple Operations (C4), Maintenance and Energy Cost (C5), and Lifetime (C6), were considered in the study. The most important criteria were C1 and C4, with scores of 0.25 and 0.23, respectively. Following the criteria weighting, the CINFU CODAS method ranks the alternative machines based on their performance across the weighted criteria. Four alternative machines (High-Speed Automated Scanner (A1), Multi-Functional Robotic Arm (A2), Mobile Inventory Tracker (A3), and Cost-Efficient Fixed Inventory Counter (A4)) are evaluated based on the criteria selected. The results indicate that Alternative A1 ranked first because of its superior speed and operational efficiency, while Alternative A3 ranked last due to its high initial cost despite being cost-effective. Finally, a sensitivity analysis further examines the impact of varying criteria weights on the alternative rankings. Quantitative findings demonstrate how the applied CINFU AHP&CODAS methodology influenced the rankings of alternatives and their sensitivity to criteria weights. The results revealed that C1 and C4 were the most essential criteria, and Machine A2 outperformed others under varying weights. Sensitivity results indicate that the changes in criterion weights may affect the alternative ranking. Full article
(This article belongs to the Special Issue Soft Computing Methods and Applications for Decision Making)
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23 pages, 5801 KiB  
Article
An Improved Human Evolution Optimization Algorithm for Unmanned Aerial Vehicle 3D Trajectory Planning
by Xue Wang, Shiyuan Zhou, Zijia Wang, Xiaoyun Xia and Yaolong Duan
Biomimetics 2025, 10(1), 23; https://doi.org/10.3390/biomimetics10010023 - 3 Jan 2025
Viewed by 447
Abstract
To address the challenges of slow convergence speed, poor convergence precision, and getting stuck in local optima for unmanned aerial vehicle (UAV) three-dimensional path planning, this paper proposes a path planning method based on an Improved Human Evolution Optimization Algorithm (IHEOA). First, a [...] Read more.
To address the challenges of slow convergence speed, poor convergence precision, and getting stuck in local optima for unmanned aerial vehicle (UAV) three-dimensional path planning, this paper proposes a path planning method based on an Improved Human Evolution Optimization Algorithm (IHEOA). First, a mathematical model is used to construct a three-dimensional terrain environment, and a multi-constraint path cost model is established, framing path planning as a multidimensional function optimization problem. Second, recognizing the sensitivity of population diversity to Logistic Chaotic Mapping in a traditional Human Evolution Optimization Algorithm (HEOA), an opposition-based learning strategy is employed to uniformly initialize the population distribution, thereby enhancing the algorithm’s global optimization capability. Additionally, a guidance factor strategy is introduced into the leader role during the development stage, providing clear directionality for the search process, which increases the probability of selecting optimal paths and accelerates the convergence speed. Furthermore, in the loser update strategy, an adaptive t-distribution perturbation strategy is utilized for its small mutation amplitude, which enhances the local search capability and robustness of the algorithm. Evaluations using 12 standard test functions demonstrate that these improvement strategies effectively enhance convergence precision and algorithm stability, with the IHEOA, which integrates multiple strategies, performing particularly well. Experimental comparative research on three different terrain environments and five traditional algorithms shows that the IHEOA not only exhibits excellent performance in terms of convergence speed and precision but also generates superior paths while demonstrating exceptional global optimization capability and robustness in complex environments. These results validate the significant advantages of the proposed improved algorithm in effectively addressing UAV path planning challenges. Full article
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21 pages, 20668 KiB  
Article
Inverted Pyramid Nanostructures Coupled with a Sandwich Immunoassay for SERS Biomarker Detection
by Wen-Huei Chang, Shao-Quan Zhang, Zi-Yi Yang and Chun-Hung Lin
Nanomaterials 2025, 15(1), 64; https://doi.org/10.3390/nano15010064 - 2 Jan 2025
Viewed by 553
Abstract
Cancer diagnostics often faces challenges, such as invasiveness, high costs, and limited sensitivity for early detection, emphasizing the need for improved approaches. We present a surface-enhanced Raman scattering (SERS)-based platform leveraging inverted pyramid SU-8 nanostructured substrates fabricated via nanoimprint lithography. These substrates, characterized [...] Read more.
Cancer diagnostics often faces challenges, such as invasiveness, high costs, and limited sensitivity for early detection, emphasizing the need for improved approaches. We present a surface-enhanced Raman scattering (SERS)-based platform leveraging inverted pyramid SU-8 nanostructured substrates fabricated via nanoimprint lithography. These substrates, characterized by sharp apices and edges, are further functionalized with (3-aminopropyl)triethoxysilane (APTES), enabling the uniform self-assembly of AuNPs to create a highly favorable configuration for enhanced SERS analysis. Performance testing of the substrates using malachite green (MG) as a model analyte demonstrated excellent detection capabilities, achieving a limit of detection as low as 10−12 M. Building on these results, the SERS platform was adapted for the sensitive and specific detection of hyaluronic acid (HA), a key biomarker associated with inflammation and cancer progression. The system employs a sandwich immunoassay configuration, with substrates functionalized with antibodies to capture HA molecules and 4-MBA-labeled SERS tags for detection. This setup achieved an ultra-sensitive detection limit of 10−11 g/mL for HA. Comprehensive characterization confirmed the uniformity and reproducibility of the SERS substrates, while validation in complex biological matrices demonstrated their robustness and reliability, highlighting their potential in cancer diagnostics and biomarker detection. Full article
(This article belongs to the Special Issue Synthesis and Applications of Gold Nanoparticles: 2nd Edition)
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20 pages, 3614 KiB  
Article
Timing Optimization Method for Pumped Storage Plant Construction Considering Capital Expenditure Capacity Feedback
by Jie Jiao, Xiaoquan Lei, Puyu He, Qian Wang, Guangxiu Yu, Wenshi Ren and Shaokang Qi
Energies 2025, 18(1), 47; https://doi.org/10.3390/en18010047 - 27 Dec 2024
Viewed by 366
Abstract
With the extensive integration of renewable energy into the power grid, pumped storage power plants have become an essential component in the development of modern power systems due to their rapid response capabilities, advanced technology, and other beneficial features. However, high construction costs [...] Read more.
With the extensive integration of renewable energy into the power grid, pumped storage power plants have become an essential component in the development of modern power systems due to their rapid response capabilities, advanced technology, and other beneficial features. However, high construction costs and irrational capital expenditure and construction schedules have constrained the robust and sustainable growth of pumped storage plants. Therefore, this paper proposes a pumped storage plant construction timing optimization method considering capital expenditure capacity feedback. Initially, an analysis is conducted on the factors that influence the capital expenditure costs of pumped storage power plants throughout their lifecycle. Next, the value of investing in pumped storage plants is assessed across three different aspects: economics, environment, and reliability. Finally, according to the principle of dynamic planning combined with the actual needs and capital expenditure potential of pumped storage plants, the sum of the capital expenditure effectiveness values in each stage is used as the indicator function of each stage to construct the pumped storage plants project capital expenditure timing optimization model, and a simulation analysis is carried out with Province Z as an example to verify the validity and applicability of the proposed model. The findings indicate that the suggested model is effective in balancing the implementation time of individual projects to achieve the maximum cumulative capital expenditure performance over the entire planning period. Full article
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19 pages, 13577 KiB  
Article
A Lightweight YOLO Model for Rice Panicle Detection in Fields Based on UAV Aerial Images
by Zixuan Song, Songtao Ban, Dong Hu, Mengyuan Xu, Tao Yuan, Xiuguo Zheng, Huifeng Sun, Sheng Zhou, Minglu Tian and Linyi Li
Drones 2025, 9(1), 1; https://doi.org/10.3390/drones9010001 - 24 Dec 2024
Viewed by 429
Abstract
Accurate counting of the number of rice panicles per unit area is essential for rice yield estimation. However, intensive planting, complex growth environments, and the overlapping of rice panicles and leaves in paddy fields pose significant challenges for precise panicle detection. In this [...] Read more.
Accurate counting of the number of rice panicles per unit area is essential for rice yield estimation. However, intensive planting, complex growth environments, and the overlapping of rice panicles and leaves in paddy fields pose significant challenges for precise panicle detection. In this study, we propose YOLO-Rice, a rice panicle detection model based on the You Only Look Once version 8 nano (YOLOv8n). The model employs FasterNet, a lightweight backbone network, and incorporates a two-layer detection head to improve rice panicle detection performance while reducing the overall model size. Additionally, we integrate a Normalization-based Attention Module (NAM) and introduce a Minimum Point Distance-based IoU (MPDIoU) loss function to further improve the detection capability. The results demonstrate that the YOLO-Rice model achieved an object detection accuracy of 93.5% and a mean Average Precision (mAP) of 95.9%, with model parameters reduced to 32.6% of the original YOLOv8n model. When deployed on a Raspberry Pi 5, YOLO-Rice achieved 2.233 frames per second (FPS) on full-sized images, reducing the average detection time per image by 81.7% compared to YOLOv8n. By decreasing the input image size, the FPS increased to 11.36. Overall, the YOLO-Rice model demonstrates enhanced robustness and real-time detection capabilities, achieving higher accuracy and making it well-suited for deployment on low-cost portable devices. This model offers effective support for rice yield estimation, as well as for cultivation and breeding applications. Full article
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25 pages, 3319 KiB  
Article
Load Optimization for Connected Modern Buildings Using Deep Hybrid Machine Learning in Island Mode
by Seyed Morteza Moghimi, Thomas Aaron Gulliver, Ilamparithi Thirumarai Chelvan and Hossen Teimoorinia
Energies 2024, 17(24), 6475; https://doi.org/10.3390/en17246475 - 23 Dec 2024
Viewed by 437
Abstract
This paper examines Connected Smart Green Buildings (CSGBs) in Burnaby, BC, Canada, with a focus on townhouses with one to four bedrooms. The proposed model integrates sustainable materials and smart components such as recycled insulation, Photovoltaic (PV) solar panels, smart meters, and high-efficiency [...] Read more.
This paper examines Connected Smart Green Buildings (CSGBs) in Burnaby, BC, Canada, with a focus on townhouses with one to four bedrooms. The proposed model integrates sustainable materials and smart components such as recycled insulation, Photovoltaic (PV) solar panels, smart meters, and high-efficiency systems. These elements improve energy efficiency and promote sustainability. Operating in island mode, CSGBs can function independently of the grid, providing resilience during power outages and reducing reliance on external energy sources. Real data on electricity, gas, and water consumption are used to optimize load management under isolated conditions. Electric Vehicles (EVs) are also considered in the system. They serve as energy storage devices and, through Vehicle-to-Grid (V2G) technology, can supply power when needed. A hybrid Machine Learning (ML) model combining Long Short-Term Memory (LSTM) and a Convolutional Neural Network (CNN) is proposed to improve the performance. The metrics considered include accuracy, efficiency, emissions, and cost. The performance was compared with several well-known models including Linear Regression (LR), CNN, LSTM, Random Forest (RF), Gradient Boosting (GB), and hybrid LSTM–CNN, and the results show that the proposed model provides the best results. For a four-bedroom Connected Smart Green Townhouse (CSGT), the Mean Absolute Percentage Error (MAPE) is 4.43%, the Root Mean Square Error (RMSE) is 3.49 kWh, the Mean Absolute Error (MAE) is 3.06 kWh, and R2 is 0.81. These results indicate that the proposed model provides robust load optimization, particularly in island mode, and highlight the potential of CSGBs for sustainable urban living. Full article
(This article belongs to the Section A: Sustainable Energy)
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13 pages, 3330 KiB  
Article
Nickel-Catalyzed Reductive Cyanation of Aryl Halides and Epoxides with Cyanogen Bromide
by Yu-Juan Wu, Chen Ma, Muhammad Bilal and Yu-Feng Liang
Molecules 2024, 29(24), 6016; https://doi.org/10.3390/molecules29246016 - 20 Dec 2024
Viewed by 470
Abstract
Nitriles are valuable compounds because they have widespread applications in organic chemistry. This report details the nickel-catalyzed reductive cyanation of aryl halides and epoxides with cyanogen bromide for the synthesis of nitriles. This robust protocol underscores the practicality of using a commercially available [...] Read more.
Nitriles are valuable compounds because they have widespread applications in organic chemistry. This report details the nickel-catalyzed reductive cyanation of aryl halides and epoxides with cyanogen bromide for the synthesis of nitriles. This robust protocol underscores the practicality of using a commercially available and cost-effective cyanation reagent. A variety of aryl halides and epoxides featuring diverse functional groups, such as -TMS, -Bpin, -OH, -NH2, -CN, and -CHO, were successfully converted into nitriles in moderate-to-good yields. Moreover, the syntheses at gram-scale and application in late-stage cyanation of natural products and drugs reinforces its potentiality. Full article
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