Search Results (551)

The two-dimensional (2D) Ruddlesden–Popper perovskite exhibits superior chemical stability but suffers from compromised photoelectric properties due to the van der Waals gap. This study presents a novel investigation of surface passivation effects on quasi-2D perovskite X₂Cs_n−1Pb_nI_3n+1 (n = 1–6; X = MA, FA, PEA) using DFT methods, revealing three key advances: First, we demonstrate that organic cation passivation (MA⁺, FA⁺, PEA⁺) enables exceptional stability improvements, with FA-passivated structures showing optimal stability—a crucial finding for materials design. Second, we identify a critical thickness effect (n > 3) where bandgaps converge to <1.6 eV (approaching bulk values) while maintaining strong absorption, establishing the minimum layer requirement for optimal performance. Third, we reveal that effective masses balance and absorption strengthens significantly when n > 3. These fundamental insights provide a transformative strategy to simultaneously enhance both stability and optoelectronic properties in quasi-2D perovskites. Full article

Due to its extensive applications, aerial image object detection has long been a hot topic in computer vision. In recent years, advancements in unmanned aerial vehicle (UAV) technology have further propelled this field to new heights, giving rise to a broader range of application requirements. However, traditional UAV aerial object detection methods primarily focus on detecting predefined categories, which significantly limits their applicability. The advent of cross-modal text–image alignment (e.g., CLIP) has overcome this limitation, enabling open-vocabulary object detection (OVOD), which can identify previously unseen objects through natural language descriptions. This breakthrough significantly enhances the intelligence and autonomy of UAVs in aerial scene understanding. This paper presents a comprehensive survey of OVOD in the context of UAV aerial scenes. We begin by aligning the core principles of OVOD with the unique characteristics of UAV vision, setting the stage for a specialized discussion. Building on this foundation, we construct a systematic taxonomy that categorizes existing OVOD methods for aerial imagery and provides a comprehensive overview of the relevant datasets. This structured review enables us to critically dissect the key challenges and open problems at the intersection of these fields. Finally, based on this analysis, we outline promising future research directions and application prospects. This survey aims to provide a clear road map and a valuable reference for both newcomers and seasoned researchers, fostering innovation in this rapidly evolving domain. We keep track of related works in a public GitHub repository. Full article

Illegal mining operations induce cascading ecosystem degradation by causing extensive ground subsidence, necessitating accurate underground goaf localization for effectively induced-hazard mitigation. The conventional locating method applied the synthetic aperture radar interferometry (InSAR) technique to obtain ground deformation to estimate underground goaf parameters, and the locating accuracy was crucially contingent upon the appropriateness of nonlinear deformation function models selection and the precision of geological parameters acquisition. However, conventional model-driven underground goaf locating frameworks often fail to sufficiently integrate prior geological information during the model selection process, potentially leading to increased positioning errors. In order to enhance the operational efficiency and locating accuracy of underground goaf, deformation model selection must be aligned with site-specific geological conditions under varying cases of prior information. To address these challenges, this study categorizes prior geological information into three different hierarchical levels (detailed, moderate, and limited) to systematically investigate the correlations between model selection and prior information. Subsequently, field validation was carried out by applying two different non-linear deformation function models, Probability Integral Model (PIM) and Okada Dislocation Model (ODM), with three different prior geological information conditions. The quantitative performance results indicate that, (1) under a detailed prior information condition, PIM achieves enhanced dimensional parameter estimation accuracy with 6.9% reduction in maximum relative error; (2) in a moderate prior information condition, both models demonstrate comparable estimation performance; and (3) for a limited prior information condition, ODM exhibits superior parameter estimation capability showing 3.4% decrease in maximum relative error. Furthermore, this investigation discusses the influence of deformation spatial resolution, the impacts of azimuth determination methodologies, and performance comparisons between non-hybrid and hybrid optimization algorithms. This study demonstrates that aligning the selection of deformation models with different types of prior geological information significantly improves the accuracy of underground goaf detection. The findings offer practical guidelines for selecting optimal models based on varying information scenarios, thereby enhancing the reliability of disaster evaluation and mitigation strategies related to illegal mining. Full article

Timely detection of pathogen spores is fundamental to ensuring early intervention and reducing the spread of corn diseases, like northern corn leaf blight, corn head smut, and corn rust. Traditional spore detection methods struggle to identify spore-level targets within complex backgrounds. To improve the recognition accuracy of various maize disease spores, this study introduced the YOLOv8s-SPM model by incorporating the space-to-depth and convolution (SPD-Conv) layers, the Partial Self-Attention (PSA) mechanism, and Minimum Point Distance Intersection over Union (MPDIoU) loss function. First, we combined SPD-Conv layers into the Backbone of the YOLOv8s to enhance recognition performance on small targets and low-resolution images. To improve computational efficiency, the PSA mechanism was incorporated within the Neck layer of the network. Finally, MPDIoU loss function was applied to refine the localization performance of bounding boxes. The results revealed that the YOLOv8s-SPM model achieved 98.9% accuracy on the mixed spore dataset. Relative to the baseline YOLOv8s, the YOLOv8s-SPM model yielded a 1.4% gain in accuracy. The improved model significantly improved spore detection accuracy and demonstrated superior performance in recognizing diverse spore types under complex background conditions. It met the demands for high-precision spore detection and filled a gap in intelligent spore recognition for maize, offering an effective starting point and practical path for future research in this field. Full article

Accurate short-term weather forecasting plays a vital role in disaster response, agriculture, and energy management, where timely and reliable predictions are essential for decision-making. Graph neural networks (GNNs), known for their ability to model complex spatial structures and relational data, have achieved remarkable success in meteorological forecasting by effectively capturing spatial dependencies among distributed weather stations. However, most existing GNN-based approaches rely on pairwise station connections, limiting their capacity to represent higher-order spatial interactions. Moreover, their dependence on supervised learning makes them vulnerable to spatial heterogeneity and temporal non-stationarity. This paper introduces a novel spatial–temporal pretraining framework, Hypergraph-enhanced Meteorological Pretraining (HyMePre), which combines hypergraph neural networks with self-supervised learning to model high-order spatial dependencies and improve generalization across diverse climate regimes. HyMePre employs a two-stage masking strategy, applying spatial and temporal masking separately, to learn disentangled representations from unlabeled meteorological time series. During forecasting, dynamic hypergraphs group stations based on meteorological similarity, explicitly capturing high-order dependencies. Extensive experiments on large-scale reanalysis datasets show that HyMePre outperforms conventional GNN models in predicting temperature, humidity, and wind speed. The integration of pretraining and hypergraph modeling enhances robustness to noisy data and improves generalization to unseen climate patterns, offering a scalable and effective solution for operational weather forecasting. Full article

This paper introduces a 0.5–5.8 GHz low-noise amplifier (LNA) incorporating a gyrator-C-based active inductor (AI) and an enhanced deep trench isolation (DTI) electrostatic discharge (ESD) diode. Results suggest that AIs exhibit excellent consistency under various process voltage temperatures (PVTs) as well as input powers and the improved DTI diodes reduce parasitic capacitance by an average of 8.5% compared to conventional ones. In terms of circuit design, comprehensive analyses of gain flatness and noise are conducted. Fabricated using a 0.18 μm SiGe BiCMOS technology, the LNA delivers a high S21 of 18.3 ± 0.3 dB, a minimum noise figure of 2.6 dB, and an S11 and S22 of less than −10 dB over the entire frequency band. Operating from a 3.3 V supply voltage with a core area of 0.049 mm², it consumes 10 mA of current. Full article

With the increasing complexity of ecological and environmental problems, eco-anxiety is increasingly recognized as an essential problem in China. Despite its prevalence, there is a lack of valid measurements in China. The purpose of the present study was to expand the Hogg Eco-anxiety Scale (HEAS) under the Chinese context and evaluate the psychometric attributes of the expanded scale. Specifically, a qualitative study was conducted in Study 1 (n = 17) to expand the HEAS in the Chinese context. Exploratory factor analysis in Study 2 (n = 297) and confirmatory factor analysis in Study 3 (n = 374) were conducted to validate the scale. The climate change anxiety scale and pro-environmental behavior scale were used to assess criterion-related validity in Study 4 (n = 305). Results indicated that a new eco-anxiety scale (i.e., EAS-20) including 20 items attributed to four dimension (somatic symptoms, affective symptoms, rumination, and behavioral symptoms) was developed. It showed satisfactory psychometric properties, including high internal consistency (α = 0.97) and a four-factor structure explaining 84.36% of the variance. The criterion-related validity was acceptable (0.25 ≤ r ≤ 0.37). The article concludes that the 20-item Eco-Anxiety Scale (EAS-20) has good psychometric properties and can be applied to measure eco-anxiety in the Chinese adult population. Full article

Soluble solids content (SSC) in fruits, as one of the key indicators of fruit quality, plays a critical role in postharvest quality assessment and grading. While traditional destructive methods can provide precise measurements of sugar content, they have limitations such as damaging the fruit’s integrity and the inability to perform rapid detection. In contrast, non-destructive detection technologies offer the advantage of preserving the fruit’s integrity while enabling fast and efficient sugar content measurements, making them highly promising for applications in fruit quality detection. This review summarizes recent advances in non-destructive detection technologies for fruit sugar content measurement. It focuses on elucidating the principles, advantages, and limitations of mainstream technologies, including near-infrared spectroscopy (NIR), X-ray technology, computer vision (CV), electronic nose (EN) technology and so on. Critically, our analysis identifies key challenges hindering the broader implementation of these technologies, namely: the integration and optimization of multi-technology approaches, the development of robust intelligent and automated detection systems, and issues related to high equipment costs and barriers to widespread adoption. Based on this assessment, we conclude by proposing targeted future research directions. These focus on overcoming the identified challenges to advance the development and practical application of non-destructive SSC detection technologies, ultimately contributing to the modernization and intelligentization of the fruit industry. Full article

Liver, the center of substance metabolism, plays a vital role in the hibernation of mammals, a topic arousing increasing interest from researchers around the world. However, it remains unclear how the liver regulates energy metabolism during the hibernation of mammals. Metabolic disorders in the liver are closely associated with numerous diseases. In this research on chipmunks (Tamias sibiricus), we compared histological changes in the liver and energy source between the conditions for hibernation and room temperature, and subsequently conducted transcriptome sequencing analysis. The results demonstrate that lipid metabolism becomes a significant energy source during hibernation via the retinol signaling pathway and PPAR signaling pathway, thereby suggesting the importance of the liver in maintaining homeostasis when facing hypothermia. Furthermore, the result provides us with a novel perspective to obtain an insight into liver metabolic reprogramming and potential therapeutic strategies for metabolic disease in the liver. Full article

This study investigated the metabolite dynamic changes and transformation pathways in Diqing Tibetan pig (DTP) hams during fermentation (0, 30, 90, 180, 360, 540 d) by widely targeted metabolomics. A total of 873 metabolites in 17 subclasses were detected, with significant changes in 448 metabolites. Additionally, 65 key metabolites were found to be involved in the top 10 pathways, with the top pathways for metabolite markers in mature hams including protein metabolism (2-oxocarboxylic acid metabolism, tryptophan metabolism and amino acid biosynthesis) and lipid metabolism (unsaturated fatty acid biosynthesis and linoleic acid metabolism). Overall, the unique DTP ham taste, flavor, and nutritional value may be contributed to by the significant accumulation of essential amino acids, MSG-like amino acids, free fatty acids (arachidonic acid, docosahexaenoic acid, and eicosapentaenoic acid), citric acid, oxaloacetic acid, succinic acid, and vitamin B. This study facilitates a comprehensive understanding of metabolic profiling and the transformation pathways of DTP hams during fermentation, providing novel insights into the biochemical mechanisms underlying traditional Tibetan pig hams, bridging traditional knowledge with modern omics technologies. Full article

The increasing deployment of photovoltaic and energy storage systems (ESSs) in modern power grids has highlighted the critical challenge of component degradation, which significantly impacts system efficiency, operational costs, and long-term reliability. Conventional energy dispatch and optimization approaches fail to adequately mitigate the progressive efficiency loss in PV modules and battery storage, leading to suboptimal performance and reduced system longevity. To address these challenges, this paper proposes a quantum-enhanced degradation pathway optimization framework that dynamically adjusts operational strategies to extend the lifespan of PV storage systems while maintaining high efficiency. By leveraging quantum-assisted Monte Carlo simulations and hybrid quantum–classical optimization, the proposed model evaluates degradation pathways in real time and proactively optimizes energy dispatch to minimize efficiency losses due to aging effects. The framework integrates a quantum-inspired predictive maintenance algorithm, which utilizes probabilistic modeling to forecast degradation states and dynamically adjust charge–discharge cycles in storage systems. Unlike conventional optimization methods, which struggle with the complexity and stochastic nature of degradation mechanisms, the proposed approach capitalizes on quantum parallelism to assess multiple degradation scenarios simultaneously, significantly enhancing computational efficiency. A three-layer hierarchical optimization structure is introduced, ensuring real-time degradation risk assessment, periodic dispatch optimization, and long-term predictive adjustments based on PV and battery aging trends. The framework is tested on a 5 MW PV array coupled with a 2.5 MWh lithium-ion battery system, with real-world degradation models applied to reflect light-induced PV degradation (0.7% annual efficiency loss) and battery state-of-health deterioration (1.2% per 100 cycles). A hybrid quantum–classical computing environment, utilizing D-Wave’s Advantage quantum annealer alongside a classical reinforcement learning-based optimization engine, enables large-scale scenario evaluation and real-time operational adjustments. The simulation results demonstrate that the quantum-enhanced degradation optimization framework significantly reduces efficiency losses, extending the PV module’s lifespan by approximately 2.5 years and reducing battery-degradation-induced wear by 25% compared to conventional methods. The quantum-assisted predictive maintenance model ensures optimal dispatch strategies that balance energy demand with system longevity, preventing excessive degradation while maintaining grid reliability. The findings establish a novel paradigm in degradation-aware energy optimization, showcasing the potential of quantum computing in enhancing the sustainability and resilience of PV storage systems. This research paves the way for the broader integration of quantum-based decision-making in renewable energy infrastructure, enabling scalable, high-performance optimization for future energy systems. Full article

To ensure the on-orbit safety of crewed spacecraft and avoid the threat of constellations such as Starlink to manned spacecraft, the industry has started to research equipping phased array radars for situational awareness of collision threat. In order to enhance the resource allocation capability of the space station’s protection radar system, this paper proposes a task scheduling method based on time shifting constraints and pulse interleaving. The time shifting constraint is designed to minimize the deviation between the actual execution and the desired execution time of the task, and it is negatively correlated with the threat degree of the target. Pulse interleaving is intended to utilize the idle time between the transmitted pulse and the received pulse of a task to perform other tasks, thereby improving the utilization of radar resources. Through computer simulation under typical parameters, our proposed method reduces the average time shifting ratio by about 60% compared to traditional task scheduling methods, and the scheduling success ratio is also higher than that of traditional scheduling methods. This demonstrates the effectiveness of the proposed method in enhancing scheduling efficiency and overall system performance. Full article

Ship seismic wave signals represent one of the most critical physical field characteristics of vessels. To achieve the high-precision detection of ship seismic wave field signals in marine environments, an underwater seismic wave signal detection system was designed. The system adopts a three-stage architecture consisting of watertight instrument housing, a communication circuit, and a buoy to realize high-capacity real-time data transmissions. The host computer performs the collaborative optimization of multi-modal hardware architecture and adaptive signal processing algorithms, enabling the detection of ship targets in oceanic environments. Through verification in a water tank and sea trials, the system successfully measured seismic wave signals. An improved ALE-LOFAR (Adaptive Line Enhancer–Low-Frequency Analysis) joint framework, combined with DEMON (Demodulation of Envelope Modulation) demodulation technology, was proposed to conduct the spectral feature analysis of ship seismic wave signals, yielding the low-frequency signal characteristics of vessels. This scheme provides an important method for the covert monitoring of shallow-sea targets, providing early warnings of illegal fishing and ensuring underwater security. Full article

Bamboo forests are among China’s key strategic forest resources, characterized by rapid growth and high carbon sequestration efficiency. Traditional management practices primarily aim to maximize economic benefits by regulating stand density to enhance yields of bamboo culms and shoots. However, the influence of density regulation on the growth and carbon accumulation of spring bamboo shoots remains insufficiently understood. Therefore, this study focuses on moso bamboo (Phyllostachys edulis (Carrière) J. Houzeau) stands and investigates shoot emergence during the shooting period across four stand density levels: D1 (1400 stems/ha), D2 (2000 stems/ha), D3 (2600 stems/ha), and D4 (3200 stems/ha). The study analyzes the dynamics of shoot emergence, height development, and morphological traits under varying stand densities, and explores patterns of carbon accumulation during the shooting period, thereby clarifying the effects of stand density on shoot quantity, growth quality, and carbon sequestration. The main findings are as follows: the number of emerging shoots decreased with increasing stand density, ranging from 2592 to 4634 shoots per hectare. The peak shoot emergence period in the D1 stand was extended by 3 days compared to D2 and D3, while the D4 stand entered the peak emergence period 6 days later than D2 and D3. The rapid height growth phase in D1 occurred 3 days earlier than in D2 and D3, and 6 days earlier than in D4. Results from the variable exponent taper equation indicated that spring shoots in the D2 and D4 stands had larger basal diameters, following the order D4 > D2 > D3 > D1. Shoots in the D2 stand exhibited the smallest taper, with the order being D2 < D3 < D1 < D4. During the late stage of shoot emergence (3 May to 9 May), all stands entered a period of rapid carbon accumulation per individual shoot. In the early stage, carbon accumulation followed the order D1 > D2 > D4 > D3; in the middle stage, the order shifted to D4 > D3 > D2 > D1; and in the final stage, the trend was D1 > D4 > D3 > D2. Within the 30-day investigation period, the carbon storage in spring shoots reached up to one-quarter or even one-third of the total accumulation during the growth period. The D1 stand exhibited the highest rate of increase in the proportion of individual shoot carbon storage. Full article