Search Results (2,923)

Apples are cultivated over a large global area with high yields, and efficient robotic harvesting requires accurate detection and localization, particularly in complex orchard environments where occlusion by leaves and fruits poses substantial challenges. To address this, we proposed a YOLO-CSB model-based method for apple detection and localization, designed to overcome occlusion and enhance the efficiency and accuracy of mechanized harvesting. Firstly, a comprehensive apple dataset was constructed, encompassing various lighting conditions and leaf obstructions, to train the model. Subsequently, the YOLO-CSB model, built upon YOLO11s, was developed with improvements including the integration of a lightweight CSFC Block to reconstruct the backbone, making the model more lightweight; the SEAM component is introduced to improve feature restoration in areas with occlusions, complemented by the efficient BiFPN approach to boost detection precision. Additionally, a 3D positioning technique integrating YOLO-CSB with an RGB-D camera is presented. Validation was conducted via ablation analyses, comparative tests, and 3D localization accuracy assessments in controlled laboratory and structured orchard settings, The YOLO-CSB model demonstrated effectiveness in apple target recognition and localization, with notable advantages under leaf and fruit occlusion conditions. Compared to the baseline YOLO11s model, YOLO-CSB improved mAP by 3.02% and reduced the parameter count by 3.19%. Against mainstream object detection models, YOLO-CSB exhibited significant advantages in detection accuracy and model size, achieving a mAP of 93.69%, precision of 88.82%, recall of 87.58%, and a parameter count of only 9.11 M. The detection accuracy in laboratory settings reached 100%, with average localization errors of 4.15 mm, 3.96 mm, and 4.02 mm in the X, Y, and Z directions, respectively. This method effectively addresses complex occlusion environments, enabling efficient detection and precise localization of apples, providing reliable technical support for mechanized harvesting. Full article

Efficient retrieval of mathematical and structural similarities in System Dynamics models remains a significant challenge for traditional lexical systems, which often fail to capture the contextual dependencies of simulation processes. This paper presents an architectural approach and implementation of a semantic search module integrated into an existing cloud-based modeling and simulation system. The proposed method employs a strategy for serializing graph structures into textual descriptions, followed by the generation of vector embeddings via local ONNX inference and indexing within a vector database (Qdrant). Experimental validation performed on a diverse corpus of complex dynamic models, compares the proposed approach against traditional information retrieval methods (Full-Text Search, Keyword Search in PostgreSQL, and Apache Lucene with Standard and BM25 scoring). The results demonstrate the distinct advantage of semantic search, achieving high precision (over 90%) within the scope of the evaluated corpus and effectively eliminating information noise. In comparison, keyword search exhibited only 24.8% precision with a significant rate of false positives, while standard full-text analysis failed to identify relevant models for complex conceptual queries (0 results). Despite a recorded increase in latency (~2 s), the study proves that the vector-based approach is a significantly more robust solution for detecting hidden semantic connections in mathematical model databases, providing a foundation for future developments toward multi-vector indexing strategies. Full article

This work presents integrated flux and velocity channel maps of the planetary nebula Abell 30 (A30) inner knot system. The observations were taken with the INTEGRAL spectrograph at the William Herschel Telescope (WHT), La Palma, Spain. Our IFU data cube has a field of view (FoV) of 12.3${}^{\prime \prime }$× 16${}^{\prime \prime }$ that partially covers knots J1 and J2, and completely covers knots J3 and J4 in the system. Optical Recombination Lines (ORLs) of C II, He I, He II, N III, O II and Collisionally Excited Lines (CELs) of [Ar IV], [Ar V], [N II], [Ne III], [Ne IV], and [O III] were detected. Our integrated flux maps visualise the ionisation structure and the chemical inhomogeneity in the system previously reported by other groups. We find that ORLs are concentrated in the polar region (J1, J3), whereas the equatorial knots (J2, J4) are dominated by CELs. The flux ratio map of the diagnostic [O III $\lambda$ 5007/4363 Å] lines reveals the electron temperature distribution, which shows cold cores of 15,000 K in knots J3 and J4 surrounded by a hot outer layer of above 20,000 K. Our channel maps show positive and negative velocity excursions from the systemic value among the ions. Several ions show variation in their velocity structures from their lower-energy-level counterparts, including [Ar IV] and [Ar V], [Ne III] and [Ne IV], and He I and He II. New recurrent velocity structures are identified in the low-density regions where the ions move much faster compared to their surrounding environments. The velocity dispersion measurements highlight extreme turbulence in some of the ions (${\sigma }_{{\mathrm{v}}_{\mathrm{rad}}}\approx 140$ km/s), consistent with supersonic/hypersonic motion driven by shocks. The forbidden line species [N II] exhibits lower turbulence (${\sigma }_{{\mathrm{v}}_{\mathrm{rad}}}\approx$ 50–60 km/s), tracing denser, less-turbulent gases. Based on our data, we conclude that both the ionisation and kinematic studies hint at shock heating and multiple ejection history in the evolutionary pathway of A30. Full article

This study developed an integrated vision–actuation system for precision weeding in indoor soil bin environments, with cabbage as a case example. The system integrates lightweight object detection, 3D co-ordinate mapping, path planning, and a three-axis synchronized conveyor-type actuator to enable precise weed identification and automated removal. By integrating ECA and CBAM attention mechanisms into YOLO11, we developed the YOLO11-WeedNet model. This integration significantly enhanced the detection performance for small-scale weeds under complex lighting and cluttered backgrounds. Based on the optimal model performance achieved during experimental evaluation, the model achieved 96.25% precision, 86.49% recall, 91.10% F1-score, and a mean Average Precision (mAP@0.5) of 91.50% calculated across two categories (crop and weed). An RGB-D fusion localization method combined with a protected-area constraint enabled accurate mapping of weed spatial positions. Furthermore, an enhanced Artificial Hummingbird Algorithm (AHA⁺) was proposed to optimize the execution path and reduce the operating trajectory while maintaining real-time performance. Indoor soil bin tests showed positioning errors of less than 8 mm on the X/Y axes, depth control within ±1 mm on the Z-axis, and an average weeding rate of 88.14%. The system achieved zero contact with cabbage seedlings, with a processing time of 6.88 s per weed. These results demonstrate the feasibility of the proposed system for precise and automated weeding at the cabbage seedling stage. Full article

Circulating tumor DNA (ctDNA) has gained increasing attention as a non-invasive biomarker with potential utility across multiple stages of melanoma. ctDNA reflects tumor-derived genetic alterations in real time and has shown value in detecting minimal residual disease, identifying early recurrence, estimating tumor burden, and monitoring response to systemic therapies. In early-stage melanoma, postoperative ctDNA positivity is strongly associated with higher recurrence risk and often precedes radiologic detection. In advanced disease, ctDNA correlates with tumor volume and can distinguish responders from non-responders during targeted therapy and immunotherapy, while also identifying emerging resistance mechanisms. Despite these advantages, clinical implementation remains limited by low shedding in early-stage disease, variation among detection platforms, and the absence of standardized clinical thresholds. Recent advances, including fragmentomics, methylation assays, and multi-target sequencing strategies, aim to improve sensitivity, particularly in low-tumor-burden settings. Integration of ctDNA with radiomics, artificial intelligence, and digital pathology represents an additional opportunity to enhance precision in risk stratification and treatment adaptation. This review summarizes current evidence on ctDNA biology, detection methods, and clinical applications in melanoma and outlines ongoing challenges and future directions required for translation into routine practice. Full article

Endothelial integrity is essential for cardiovascular health, and circulating endothelial progenitor cells, particularly endothelial colony-forming cells (ECFCs), are key contributors to vascular repair and maintenance. Long non-coding RNAs (lncRNAs) have emerged as novel epigenetic regulators of endothelial physiology and pathology. Building on our previous work identifying the lncRNA KLRK1-AS1 as a positive modulator of ECFC wound healing, we aimed to elucidate its role in endothelial biology. Cord blood-derived ECFCs were subjected to siRNA-mediated silencing of KLRK1-AS1, followed by blinded evaluations of monolayer morphology, barrier stability using ECIS impedance measurements, assessments of proliferation, and spheroid-based angiogenic activity. SiRNA-mediated silencing of KLRK1-AS1 induced detectable alterations in ECFC monolayer morphology (p = 0.047), while proliferation remained unaffected. Notably, KLRK1-AS1 knockdown significantly compromised endothelial barrier integrity, resulting in a 44% reduction in impedance after 48 h (p < 0.001), suggesting weakened intercellular contacts. In contrast, loss of KLRK1-AS1 enhanced angiogenic behaviour, demonstrated by an increased number of sprouts (+62%, p = 0.031). Together, these findings indicate that KLRK1-AS1 supports a quiescent, stable endothelial phenotype, with intact barrier function, while its depletion shifts ECFCs toward a more angiogenic, activated state. Our results identify KLRK1-AS1 as a previously unrecognised regulator of endothelial function. Full article

Despite the persistent global threat of seasonal influenza viruses such as A(H1N1)pdm09 and A/H3N2, their epidemiological and genetic characteristics in China following the implementation of COVID-19 non-pharmaceutical interventions (NPIs) remain poorly characterized. Between September 2020 and December 2023, we conducted an integrated epidemiological and genomic analysis of influenza A viruses in children in Wuhan. The overall positivity rate for influenza A virus was markedly low at 3.43% (109/3171), reflecting a profound suppression of circulation during the pandemic. Among genotyped positives, H1N1pdm09 was predominant (52.3%), followed by H3N2 (16.5%) and untypeable strains (32.1%). Preschool children showed the highest susceptibility. Phylogenetic analysis revealed that the circulating H1N1 strains (90%) belonged to clade 6B.1A.5a.2, clustering with viruses from Hong Kong and Pakistan. In contrast, H3N2 strains (76.92%) primarily fell into clade 3C.2a1b.2a.2b, closely related to contemporary strains from Europe and North America. Notably, we identified key hemagglutinin mutations associated with antigenic drift (e.g., R240Q in H1N1; E78G, R158G in H3N2) and neuraminidase mutations potentially conferring antiviral resistance (e.g., S247N in H1N1; S245N, a putative novel glycosylation site, in H3N2). Evidence of reassortment events was also detected, underscoring the continued genomic evolution of these viruses despite their low prevalence. Our findings demonstrate that genetically diverse and antigenically drifted influenza A viruses continued to circulate and evolve in Wuhan during the COVID-19 pandemic, albeit at dramatically reduced levels. This highlights the critical need for sustained genomic surveillance and timely updates of vaccine compositions to pre-empt the resurgence of influenza in the post-pandemic era. Full article

Structural health monitoring (SHM) has evolved into an indispensable component for ensuring the safety, durability, and life-cycle efficiency of civil infrastructure. Over the past five years, significant technological advancements have been made in innovative sensing systems, facilitating real-time assessment of structural performance and the early detection of deterioration. This comprehensive review presents recent developments in smart sensor-based SHM, with particular emphasis on the convergence of the Internet of Things (IoT), artificial intelligence (AI), and digital twin (DT) frameworks. Our review critically examines advances in fiber-optic, piezoelectric, MEMS-based, vision-based, acoustic, and environmental sensors, as well as emerging multi-sensor fusion architectures. In addition, bibliometric insights highlight the significant rise in global research activity and influential thematic clusters in SHM between 2020 and 2025. The discussion underscores how AI-integrated data analytics, IoT-enabled wireless networks, and DT-driven virtual replicas enable intelligent, autonomous, and predictive monitoring of bridges, buildings, tunnels, and other large-scale civil infrastructure. Field deployments and case studies are analyzed to bridge the gap between laboratory-scale demonstrations and real-world implementation. Finally, key scientific and practical challenges—including the durability of embedded sensors, the interoperability of heterogeneous data, cybersecurity in connected systems, and the explainability of AI models—are outlined to guide future research. Overall, this review positions contemporary SHM as a transition from traditional damage detection to comprehensive life-cycle management of infrastructure through self-diagnosing, data-centric, and sustainability-driven monitoring ecosystems. Full article

Cerium oxide (CeO₂) nanozymes, also known as nanoceria have emerged as a versatile class of catalytic nanomaterials capable of mimicking key redox enzymes, including oxidases and peroxidases. Their tunable Ce³⁺/Ce⁴⁺ redox cycling, high density of oxygen vacancies, and exceptional resistance to thermal, pH, and storage stress distinguish CeO₂ from conventional enzyme labels, such as horseradish peroxidase (HRP). In immunoassays, CeO₂ enables H₂O₂-free TMB (3,3’,5,5’-tetramethylbenzidine) oxidation, generating strong chromogenic signals with minimal background. Although CeO₂ nanozymes have been explored in colorimetric, chemiluminescent, and photoactive immunoassays, their integration into lateral flow immunoassays (LFIAs) remains limited, with only a few hybrid CeO₂-containing systems reported to date. This mini-review highlights the limitations of conventional peroxidase-based formats and explains how CeO₂’s redox cycling (Ce³⁺/Ce⁴⁺) and oxygen-vacancy-driven catalysis deliver stable, reagent-free signal amplification. Emphasis is placed on the synthetic control of CeO₂, conjugation chemistry with antibodies, and integration into LFIA architectures. CeO₂ enables hydrogen-peroxide-free colorimetric detection with improved robustness and sensitivity, positioning it as a promising catalytic label for point-of-care testing. However, it may aggregate in high-ionic-strength buffers, and its synthesis cost increases for highly uniform, vacancy-engineered materials. Surface functionalization with polymers or dopants and optimized dispersion strategies can mitigate these issues, guiding future practical implementations. Full article

Background/Objectives: Hepatocellular carcinoma (HCC) shows marked molecular and histopathological heterogeneity. Among the alterations most strongly associated with clinical outcome are mutations in TP53 and CTNNB1, as well as the presence of microvascular invasion (MVI). Although these factors are well established as prognostic indicators, how their molecular effects relate to tumor morphology remains unclear. In this work, we studied transcriptomic changes linked to TP53 and CTNNB1 mutational status and to MVI, and examined whether these changes are reflected in routine histology. Methods: RNA sequencing data from HCC samples annotated for mutations and vascular invasion were analyzed using differential expression analysis combined with machine learning-based feature selection to characterize the underlying transcriptional programs. In parallel, we trained a weakly supervised multitask deep learning model on hematoxylin and eosin-stained whole-slide images using slide-level labels only, without spatial annotations, to assess whether these features could be inferred from global histological patterns. Results: Distinct gene expression profiles were observed for TP53-mutated, CTNNB1-mutated, and MVI-positive tumors, involving pathways related to proliferation, metabolism, and invasion. Image-based models were able to capture morphological patterns associated with these states, achieving above-random discrimination with variable performance across tasks. Conclusions: Taken together, these results support the existence of coherent biological programs underlying key risk determinants in HCC and indicate that their phenotypic effects are, at least in part, detectable in routine histopathology. This provides a rationale for integrative morpho-molecular approaches to risk assessment in HCC. Full article

Understanding interactions among sympatric mesocarnivore populations is essential for making sound management decisions. The golden jackal has rapidly expanded in Europe, raising questions about its potential intraguild effects. Using long-term hunting bag data (1997–2024) from Hungary, we investigated spatiotemporal population trends of the European badger, red fox, and golden jackal. We examined pairwise associations in their annual growth rates. Generalised additive models and Pearson correlation analyses revealed strong species-specific temporal and spatial trends and weak to moderate positive relationships among the species’ population growth rates at the national scale and within regions of high jackal population density. We found no evidence of jackal suppression of foxes or badgers. Additionally, badgers showed the strongest positive association with fox populations. Our large-scale analyses suggest that these mesocarnivores coexist without substantial competitive interference, likely due to local spatial heterogeneity and fine-scale temporal partitioning that are not detectable in annual, broad-scale (national) data. These findings highlight the importance of integrating broad-scale population data with finer-scale behavioural studies to better understand coexistence mechanisms in expanding mesocarnivore assemblages. Full article

This paper presents an integrative methodology combining advanced network packet vectorization techniques, parallel processing with Dask, GPU-optimized machine learning models, and the Qdrant vector database. Our approach achieves a 99.9% detection rate for malicious traffic with only a 1% false-positive rate, setting new performance benchmarks for cybersecurity systems. The methodology establishes an average detection time limit not exceeding 10% of the total response time, maintaining high precision even for sophisticated attacks. The system processes 56 GB of PCAP files from Malware-Traffic-Analysis.net (2020–2024) through a five-stage pipeline: distributed packet processing, feature extraction, vectorization, vector database storage, and GPU-accelerated classification using XGBoost, Random Forest, and K-Nearest Neighbors models. Full article

Image-Based Overlay (IBO) equipment leverages optical reflection imaging principles, combined with focusing and alignment strategies to measure overlay marks. Among all measurement steps, the focal plane measurement of marks exerts the most critical impact on overlay accuracy, while the time consumed by focal plane detection directly determines the overall measurement throughput. To address the trade-off between accuracy and efficiency in advanced process nodes, this paper proposes an integrated optimization strategy encompassing optical hardware design and software algorithms. The hardware solution adopts a dual-wavelength, dual-detector architecture: optimal imaging wavelengths are selected independently for the previous-layer and current-layer marks, ensuring each layer achieves ideal imaging conditions without mutual interference. The software strategy employs a deep learning framework to simultaneously predict and adjust the horizontal position (alignment) and vertical defocus number of measured marks in real time with high precision, thereby securing the optimal imaging posture. By synergizing hardware-based optimal imaging conditions and software-based posture adjustment, this method effectively mitigates the impact of background noise and system aberrations, ultimately improving both the accuracy and efficiency of overlay measurement. Full article

Urbanization and climate change have intensified the need for advanced methods to simulate building energy performance within realistic urban environmental contexts. This study presents a microclimate-informed framework for developing representative building energy prototypes that enable the estimation of energy use for buildings sharing similar microclimatic conditions and building-level characteristics. The framework is demonstrated using Des Moines, Iowa, as a case study. The framework combines high-resolution microclimate modeling with geospatial analysis to quantify the influence of urban form and vegetation on building energy use. Localized weather files were generated using the Weather Research and Forecasting (WRF) model to capture spatial variations in microclimate across the city. Detailed three-dimensional models of buildings and trees were developed from Light Detection and Ranging (LiDAR) point cloud data and integrated with building attributes, including construction materials and heating and cooling systems, to generate representative building typologies use them to build a similarity-based lookup table. Urban energy simulations were conducted using the Urban Modeling Interface (UMI). To demonstrate the effectiveness of the framework, simulations were conducted for two building prototypes according to the framework. Results show that monthly energy use intensity (EUI) of a representative cluster compared to randomly selected buildings differs by 10% to 19%, with both positive and negative deviations observed depending on building template and month. Thus, the proposed framework shows great promise to capture comparable energy performance trends across buildings with similar construction characteristics and urban context and minimize computational demands for doing so. While evapotranspiration effects are not explicitly modeled in the current framework, they are recognized as an important microclimatic process and will be incorporated in future work. This study demonstrates that the proposed framework provides a scalable and computationally efficient approach for urban-scale energy analysis and can support data driven decision making for climate-responsive urban planning. Full article

Autonomous ships require reliable and economical navigation; however, their performance is hindered when satellite-based positioning signals become unavailable. In such global navigation satellite system (GNSS)-denied conditions, a backup navigation system integrating a strapdown inertial navigation system (SINS), Doppler velocity logger (DVL), and a compass (SINS/DVL/COMPASS) can provide essential state information, but the accuracy and fault tolerance of such systems are constrained by weak observability of position/heading errors and strong dependence on DVL measurements. This study proposes a fault-tolerant optimization method based on perceptual information compensation. First, radar imagery and electronic chart data are fused at the feature level using a weighted wavelet strategy to enhance the environmental feature saliency for shoreline extraction. Second, characteristic coastline inflection points are detected and tracked using a dual-curvature and distance-constrained procedure, generating external position observations via radar–chart matching. These observations are incorporated into the SINS/DVL/COMPASS framework to improve its state observability and robustness. Simulation results show that under nominal conditions, perceptual compensation mitigates error divergence and promotes the convergence of position errors, improving the positioning stability. In terms of robustness, the proposed method delivered more stable state-error behavior than the baseline under DVL speed faults of +2 m/s, −2 m/s, and +2 m/s injected at 301–330, 701–730, and 1101–1130 s, respectively. Quantitatively, the 3σ bounds of velocity and position-related errors are reduced under fault conditions, indicating improved fault tolerance and suitability for short-term nearshore autonomous navigation during GNSS outages. Full article