Search Results (907)

In this paper, a novel online detection system is designed to enhance accuracy and operational efficiency in the outbound logistics of automotive components after production. The system consists of a scanning portal system and an improved YOLOv12-based detection algorithm which captures images of automotive parts passing through the scanning portal in real time. By integrating deep learning, the system enables real-time monitoring and identification, thereby preventing misdetections and missed detections of automotive parts, in this way promoting intelligent automotive part recognition and detection. Our system introduces the A2C2f-SA module, which achieves an efficient feature attention mechanism while maintaining a lightweight design. Additionally, Dynamic Space-to-Depth (Dynamic S2D) is employed to improve convolution and replace the stride convolution and pooling layers in the baseline network, helping to mitigate the loss of fine-grained information and enhancing the network’s feature extraction capability. To improve real-time performance, a GFL-MBConv lightweight detection head is proposed. Furthermore, adaptive frequency-aware feature fusion (Adpfreqfusion) is hybridized at the end of the neck network to effectively enhance high-frequency information lost during downsampling, thereby improving the model’s detection accuracy for target objects in complex backgrounds. On-site tests demonstrate that the system achieves a comprehensive accuracy of 97.3% and an average vehicle detection time of 7.59 s, exhibiting not only high precision but also high detection efficiency. These results can make the proposed system highly valuable for applications in the automotive industry. Full article

Low Earth orbit (LEO) signal of opportunity (SOP) positioning relies on the accumulation of epochs obtained through prolonged observation periods. The contribution of an LEO satellite single epoch to positioning accuracy is influenced by multi-level characteristics that are challenging for traditional models. To address this limitation, we propose an Agent-Weighted Recursive Least Squares (RLS) Positioning Framework (AWR-PF). This framework employs an agent to comprehensively analyze individual epoch characteristics, assess their credibility, and convert them into adaptive weights for RLS iterations. We developed a novel Markov Decision Process (MDP) model to assist the agent in addressing the epoch weighting problem and trained the agent utilizing the Double Deep Q-Network (DDQN) algorithm on 107 h of Iridium signal data. Experimental validation on a separate 28 h Iridium signal test set through 97 positioning trials demonstrated that AWR-PF achieves superior average positioning accuracy compared to both standard RLS and randomly weighted RLS throughout nearly the entire iterative process. In a single positioning trial, AWR-PF improves positioning accuracy by up to 45.15% over standard RLS. To the best of our knowledge, this work represents the first instance where an AI algorithm is used as the core decision-maker in LEO SOP positioning, establishing a groundbreaking paradigm for future research. Full article

The countries and regions along the Belt and Road are rich in wetland carbon sink resources, crucial for mitigating greenhouse gas emissions and achieving global emission reduction. This paper uses policy analysis and desk research to analyze the overview of wetland carbon sinks in these countries. It explores the necessity of legal system construction for their carbon sink trading. This study finds that smooth trading requires clear property rights definition rules, efficient market trading entities, definite carbon sink trading price rules, financial support aligned with the Equator Principles, and support from biodiversity-compatible environmental regulatory principles. Currently, there are still obstacles in wetland carbon sink trading in the Belt and Road, such as property rights confirmation, an accounting system, an imperfect market trading mechanism, and the coexistence of multiple trading risks. Therefore, this paper first proposes to clarify the goal of the legal guarantee mechanism. Efforts should focus on promoting a consensus on wetland carbon sink ownership and establishing a unified accounting standard system; simultaneously, the relevant departments should conduct field investigations and monitoring, standardize the market order, and strengthen government financial support and funding guarantees. Full article

The parameter identification of Autonomous Underwater Vehicles (AUVs) serves as a fundamental basis for achieving high-precision motion control, state monitoring, and system development. Currently, AUV parameter identification typically relies on the complete motion information obtained from onboard sensors. However, in practical applications, it is often challenging to accurately measure key state variables such as velocity and angular velocity, resulting in incomplete measurement data that compromises identification accuracy and model reliability. This issue is particularly pronounced in vertical motion tasks involving low-speed, large pitch angles, and highly maneuverable conditions, where the strong coupling and nonlinear characteristics of underwater vehicles become more significant. Traditional hydrodynamic models based on full-state measurements often suffer from limited descriptive capability and difficulties in parameter estimation under such conditions. To address these challenges, this study investigates a parameter identification method for AUVs operating under vertical, large-amplitude maneuvers with constrained measurement information. A control autoregressive (CAR) model-based identification approach is derived, which requires only pitch angle, vertical velocity, and vertical position data, thereby reducing the dependence on complete state observations. To overcome the limitations of the conventional Recursive Least Squares (RLS) algorithm—namely, its slow convergence and low accuracy under rapidly changing conditions—a Multi-Innovation Least Squares (MILS) algorithm is proposed to enable the efficient estimation of nonlinear hydrodynamic characteristics in complex dynamic environments. The simulation and experimental results validate the effectiveness of the proposed method, demonstrating high identification accuracy and robustness in scenarios involving large pitch angles and rapid maneuvering. The results confirm that the combined use of the CAR model and MILS algorithm significantly enhances model adaptability and accuracy, providing a solid data foundation and theoretical support for the design of AUV control systems in complex operational environments. Full article

Space-Air-Ground Integrated Network (SAGIN), which is considered a network architecture with great development potential, exhibits significant cross-domain collaboration characteristics at present. However, most of the existing works ignore the matching and adaptability of differential tasks and heterogeneous resources, resulting in significantly inefficient task execution and undesirable network performance. As a consequence, we formulate a category attribute-oriented resource allocation and task offloading optimization problem with the aim of minimizing the overall scheduling cost. We first introduce a task–resource matching matrix to facilitate optimal task offloading policies with computation resources. In addition, virtual queues are constructed to take the impacts of randomized task arrival into account. To solve the optimization objective which jointly considers bandwidth allocation, transmission power control and task offloading decision effectively, we proposed a deep reinforcement learning (DRL) algorithm framework considering type matching. Simulation experiments demonstrate the effectiveness of our proposed algorithm as well as superior performance compared to others. Full article

The estimation of the state of health (SOH) of lithium-ion batteries is a critical technology for enhancing battery lifespan and safety. When estimating SOH, it is essential to select representative features, commonly referred to as health indicators (HIs). Most existing studies primarily focus on HIs related to capacity degradation and internal resistance increase. However, due to the complexity of lithium-ion battery degradation mechanisms, the relationships between these mechanisms and health indicators remain insufficiently explored. This paper provides a comprehensive review of core methodologies for SOH estimation, with a particular emphasis on the classification and extraction of health indicators, direct measurement techniques, model-based and data-driven SOH estimation approaches, and emerging trends in battery management system applications. The findings indicate that capacity, internal resistance, and temperature-related indicators significantly impact SOH estimation accuracy, while machine learning models demonstrate advantages in multi-source data fusion. Future research should further explore composite health indicators and aging mechanisms of novel battery materials, and improve the interpretability of predictive models. This study offers theoretical support for the intelligent management and lifespan optimization of lithium-ion batteries. Full article

The existing deep learning-based Airborne Laser Scanning (ALS) point cloud semantic segmentation methods require a large amount of labeled data for training, which is not always feasible in practice. Insufficient training data may lead to over-fitting. To address this issue, we propose a novel Multi-branch Feature Extractor (MFE) and a three-stage transfer learning strategy that conducts pre-training on multi-source ALS data and transfers the model to another dataset with few samples, thereby improving the model’s generalization ability and reducing the need for manual annotation. The proposed MFE is based on a novel multi-branch architecture integrating Neighborhood Embedding Block (NEB) and Point Transformer Block (PTB); it aims to extract heterogeneous features (e.g., geometric features, reflectance features, and internal structural features) by leveraging the parameters contained in ALS point clouds. To address model transfer, a three-stage strategy was developed: (1) A pre-training subtask was employed to pre-train the proposed MFE if the source domain consisted of multi-source ALS data, overcoming parameter differences. (2) A domain adaptation subtask was employed to align cross-domain feature distributions between source and target domains. (3) An incremental learning subtask was proposed for continuous learning of novel categories in the target domain, avoiding catastrophic forgetting. Experiments conducted on the source domain consisted of DALES and Dublin datasets and the target domain consists of ISPRS benchmark dataset. The experimental results show that the proposed method achieved the highest OA of 85.5% and an average F1 score of 74.0% using only 10% training samples, which means the proposed framework can reduce manual annotation by 90% while keeping competitive classification accuracy. Full article

Accurate Doppler frequency estimation for Low Earth Orbit (LEO)-based Signals of Opportunity (SoOP) positioning faces significant challenges from extreme dynamics (±40 kHz Doppler shift, 0.4 Hz/ms fluctuation) and severe SNR fluctuations induced by multi-beam switching. Empirical analysis reveals that phased-array beamforming generates three-tiered SNR fluctuation patterns during unpredictable beam handovers, rendering conventional single-algorithm solutions fundamentally inadequate. To address this limitation, we propose KDFE (KNN-Driven Fusion Estimator)—an adaptive framework integrating the Rife–Vincent algorithm and MLE via intelligent switching. Global FFT processing extracts real-time Doppler-SNR parameter pairs, while a KNN-based arbiter dynamically selects the optimal estimator by: (1) Projecting parameter pairs into historical performance space, (2) Identifying the accuracy-optimal algorithm for current beam conditions, and (3) Executing real-time switching to balance accuracy and robustness. This decision model overcomes the accuracy-robustness trade-off by matching algorithmic strengths to beam-specific dynamics, ensuring optimal performance during abrupt SNR transitions and high Doppler rates. Both simulations and field tests demonstrate KDFE’s dual superiority: Doppler estimation errors were reduced by 26.3% (vs. Rife–Vincent) and 67.9% (vs. MLE), and 3D positioning accuracy improved by 13.6% (vs. Rife–Vincent) and 49.7% (vs. MLE). The study establishes a pioneering framework for adaptive LEO-SoOP positioning, delivering a methodological breakthrough for LEO navigation. Full article

Ultraviolet B (UVB) irradiation drives skin photodamage, prompting exploration of natural therapeutics. This study investigated the reparative effects and mechanisms of crude Gastrodia elata polysaccharides (GP) on UVB-induced acute skin damage. GP was extracted from fresh G. elata via water extraction and alcohol precipitation. It is a homogeneous polysaccharide with a weight-average molecular weight of 808.863 kDa, comprising Ara, Glc, Fru, and GalA. Histopathological analysis revealed that topical application of GP on the dorsal skin of mice effectively restored normal physiological structure, suppressing epidermal hyperplasia and collagen degradation. Biochemical assays showed that GP significantly reduced the activities of MPO and MDA following UVB exposure while restoring the enzymatic activities of SOD and GSH, thereby mitigating oxidative stress. Moreover, GP treatment markedly upregulated the anti-inflammatory cytokines TGF-β and IL-10 and downregulated the pro-inflammatory mediators IL-6, IL-1β, and TNF-α, suggesting robust anti-inflammatory effects. Transcriptomics revealed dual-phase mechanisms: Early repair (day 5) involved GP-mediated suppression of hyper inflammation and accelerated necrotic tissue clearance via pathway network modulation. Late phase (day 18) featured enhanced anti-inflammatory, antioxidant, and tissue regeneration processes through energy-sufficient, low-inflammatory pathway networks. Through a synergistic response involving antioxidation, anti-inflammation, promotion of collagen synthesis, and acceleration of skin barrier repair, GP achieves comprehensive repair of UVB-induced acute skin damage. Our findings not only establish GP as a potent natural alternative to synthetic photoprotective agents but also reveal novel pathway network interactions governing polysaccharide-mediated skin regeneration. Full article

A sudden increase in ambient oxygen concentration after birth forces the metabolic switch from anaerobic glycolysis to oxidative phosphorylation, which contributes to the rapid decline of cardiomyocyte proliferation. Lactate dehydrogenase A (LDHA), a metabolic enzyme normally localized in the cytoplasm, has been reported to regulate cardiomyocyte proliferation via inducing metabolic reprogramming. Nuclear LDHA has been observed in multiple proliferative cells, whereas the role of LDHA nuclear translocation in cardiomyocyte proliferation remains unresolved. Here we found that the expression of nuclear LDHA was induced both in the infarct area of myocardial infarction (MI) in mice and hypoxic cardiomyocytes in vitro. Mechanically, mild hypoxia prompted metabolic reprogramming which motivated cardiomyocyte proliferation by alleviating reactive oxygen species (ROS), while severe hypoxia coincided with oxidative stress that induced cardiomyocyte cell cycle arrest. Interestingly, LDHA nuclear translocation in cardiomyocytes occurred in response to oxidative stress, and blocking of nuclear LDHA resulted in elevated ROS generation. Collectively, our findings uncover a non-canonical role of nuclear LDHA in maintaining redox balance and resisting cardiomyocyte cell cycle arrest. Full article

Urban systems are the spatial carriers of social and economic relations at the regional level, and their relational and structural resilience are key to regional coordination and sustainable development, attracting widespread attention from scholars. In order to analyze the internal relationships of urban agglomerations in underdeveloped mountainous regions and optimize their spatial resource allocation and resilience, this study takes the urban agglomeration of Qiandongnan in China as an example and researches their internal relationships, development potential, and influencing factors based on quantitative methods such as social network analysis. The results show that the urban cluster in Qiandongnan presents “large dispersion and small aggregation” distribution characteristics, with the karst landscape as the main influencing factor; the spatial network exhibits a scale-free morphology with an obvious core–periphery structure, demonstrating moderate stability but poor completeness, weak equilibrium, and low overall resilience; only 15.61% of nodes demonstrate high competitiveness; urban units with functional roles serve as critical network nodes; urban units’ development potential is divided into three tiers (with 47.31% being medium-high), although overall levels remain low; and the development potential, overall network, individual network, and network resilience of urban units are all positively correlated, with economic and transportation development conditions being the main influencing factors. Based on the abovementioned findings, this study proposes a “multi-level resilience promotion path for network structure optimization”, which provides a theoretical basis and optimization control methods for the reconstruction and synergistic development of urban agglomerations. It also serves as a reference for the development planning of urban systems in other underdeveloped mountainous regions. Full article

Insects’ body coloration may be indirectly influenced by their host plants. Perina nuda (Lepidoptera: Lymantriidae), commonly known as the Banyan Tussock Moth and a serious pest of banyan trees (Ficus spp.) in southern China, exhibits light body coloration during its first- to third-instar stages, with its coloration progressively darkening as it matures, but little is known of the relationship between larval body coloration and host plants. To address this gap, we examined the R (red), G (green), B (blue), and L (lightness) values of the head, dorsal thorax and abdomen, stripe, dorsal mid-line, and tail of larvae fed on different hosts and host endogenous substance by using quantitative image analysis and chemical determination. Our results revealed that larval body coloration exhibited conserved ontogenetic patterns but varied significantly with host species, developmental age, and anatomical region. Redundancy analysis identified chlorophyll-b as the dominant driver, strongly associating with dorsal thorax–abdomen pigmentation. Flavonoids exhibited subthreshold significance, correlating with darker dorsal mid-line coloration, while nutrients (sugars, proteins) showed negligible effects. Linear regression revealed weak but significant links between leaf and larval body coloration in specific body regions. These findings demonstrate that host plant endogenous substances play a critical role in shaping larval body coloration. This study provides a foundation for understanding the ecological and biochemical mechanisms underlying insect pigmentation, with implications for adaptive evolution and pest management strategies. Full article

Underwater explosion ice-breaking technology is critical for Arctic development and ice disaster prevention due to its high efficiency, yet it faces challenges in understanding the coupled dynamics of shock waves, pulsating bubbles, and heterogeneous ice fracture. This review synthesizes theoretical models, experimental studies, and numerical simulations investigating damage mechanisms. Key findings establish that shock waves initiate brittle fracture via stress superposition while bubble pulsation drives crack propagation through pressure oscillation; optimal ice fragmentation depends critically on charge weight, standoff distance, and ice thickness. However, significant limitations persist in modeling sea ice heterogeneity, experimental replication of polar conditions, and computational efficiency. Future advancements require multiscale fluid–structure interaction models integrating brine migration effects, enhanced experimental diagnostics for transient processes, and optimized numerical algorithms to enable reliable predictions for engineering applications. Full article

Globally, the proportion of the consumption of fruits in the human diet shows an increasing trend. However, fruits may incur significant losses during the post-harvest storage and transportation process due to metabolic activities and mechanical damage. Post-harvest coating technology has been proven to be an effective means of reducing quality loss, and it offers the advantages of being environmentally friendly, energy-efficient, and free of chemical residues. This article begins with an introduction to the three main mechanisms of coating preservation, including physical barrier effects, physiological metabolism regulation, and antibacterial and antioxidant effects. Secondly, this paper comprehensively reviews the latest progress of coating application technology in the field of fruit preservation, and summarizes the development of coating application technology in recent years, which is divided into two categories: traditional technology and fiber coating formation technology. Among these, the spraying method in traditional technology and microfluidic spinning technology in fiber coating formation technology are emphasized. This information will help to further develop coating application techniques to improve post-harvest fruit preservation. Full article

In the context of the intensifying global climate crisis, the power industry, as a significant carbon emitter, urgently needs to promote low-carbon transformation using market mechanisms. In this paper, a multi-objective stochastic optimization scheduling framework for regional power grids integrating carbon trading (CET) and green certificate trading (GCT) is proposed to coordinate the conflict between economic benefits and environmental objectives. By building a deterministic optimization model, the goal of maximizing power generation profit and minimizing carbon emissions is combined in a weighted form, and the power balance, carbon quota constraint, and the proportion of renewable energy are introduced. To deal with the uncertainty of power demand, carbon baseline, and the green certificate ratio, Monte Carlo simulation was further used to generate random parameter scenarios, and the CPLEX solver was used to optimize scheduling schemes iteratively. The simulation results show that when the proportion of green certificates increases from 0.35 to 0.45, the proportion of renewable energy generation increases by 4%, the output of coal power decreases by 12–15%, and the carbon emission decreases by 3–4.5%. At the same time, the tightening of carbon quotas (coefficient increased from 0.78 to 0.84) promoted the output of gas units to increase by 70 MWh, verifying the synergistic emission reduction effect of the “total control + market incentive” policy. Economic–environmental tradeoff analysis shows that high-cost inputs are positively correlated with the proportion of renewable energy, and carbon emissions are significantly negatively correlated with the proportion of green certificates (correlation coefficient −0.79). This study emphasizes that dynamic adjustments of carbon quota and green certificate targets can avoid diminishing marginal emission reduction efficiency, while the independent carbon price mechanism needs to enhance its linkage with economic targets through policy design. This framework provides theoretical support and a practical path for decision-makers to design a flexible market mechanism and build a multi-energy complementary system of “coal power base load protection, gas peak regulation, and renewable energy supplement”. Full article