Search Results (158)

In a variety of UAV applications, visual–inertial navigation systems (VINSs) play a crucial role in providing accurate positioning and navigation solutions. However, traditional VINS struggle to adapt flexibly to varying environmental conditions due to fixed covariance matrix settings. This limitation becomes especially acute during high-speed drone operations, where motion blur and fluctuating image clarity can significantly compromise navigation accuracy and system robustness. To address these issues, we propose an innovative adaptive covariance matrix estimation method for UAV-based VINS using Gaussian formulas. Our approach enhances the accuracy and robustness of the navigation system by dynamically adjusting the covariance matrix according to the quality of the images. Leveraging the advanced Laplacian operator, detailed assessments of image blur are performed, thereby achieving precise perception of image quality. Based on these assessments, a novel mechanism is introduced for dynamically adjusting the visual covariance matrix using a Gaussian model according to the clarity of images in the current environment. Extensive simulation experiments across the EuRoC and TUM VI datasets, as well as the field tests, have validated our method, demonstrating significant improvements in navigation accuracy of drones in scenarios with motion blur. Our algorithm has shown significantly higher accuracy compared to the famous VINS-Mono framework, outperforming it by 18.18% on average, as well as the optimization rate of RMS, which reaches 65.66% for the F1 dataset and 41.74% for F2 in the field tests outdoors. Full article

Biostimulants, particularly single amino acids, can increase plant growth and crop quality, gaining significant attention. This study investigates the effects of 10 amino acids via root/foliar application on the growth, quality, taste, and volatile flavor of mini-watermelons and compares the differences between the application methods. Here, we employed electronic noses, electronic tongues, and gas chromatography–ion mobility spectrometry to investigate these effects. Root application excels in fruit growth and pectin accumulation, while foliar application boosts soluble protein and specific nutrients. Specifically, root application (except for Val) significantly increases fruit weight, with Gly being most effective for longitudinal diameter, while most amino acids (except Val/Lys) promote transverse diameter. Pectin content shows bidirectional regulation: root application of Glu/Gly/Lys/Pro/Trp/Val enhances pectin, whereas foliar application inhibits it. For taste indices, most treatments improve soluble solids (except Glu root/Arg-Leu foliar), and Ala/Asp/Glu/Gly reduce titratable acids, optimizing the sugar–acid ratio. Foliar application is more efficient for soluble protein accumulation (Ala/Glu/Gly/Pro/Leu). For nutritional quality, except for Lys, all treatments increase vitamin C and widely promote total phenolics and lycopene, with only minor exceptions, and only Arg foliar application enhances ORAC. Additionally, the results revealed that root-applied lysine and valine greatly raised the levels of hexanal and 2-nonenal, whereas foliar-applied valine significantly increased n-nonanal and (Z)-6-nonenal. Overall, we found that amino acids can considerably improve mini-watermelon production, quality, taste, and antioxidant capacity, providing theoretical and practical references for their widespread use in agriculture. Full article

As a fundamental technology in environmental perception, sound source localization (SSL) plays a critical role in public safety, marine exploration, and smart home systems. However, traditional methods such as beamforming and time-delay estimation rely on manually designed physical models and idealized assumptions, which struggle to meet practical demands in dynamic and complex scenarios. Recent advancements in deep learning have revolutionized SSL by leveraging its end-to-end feature adaptability, cross-scenario generalization capabilities, and data-driven modeling, significantly enhancing localization robustness and accuracy in challenging environments. This review systematically examines the progress of deep learning-based SSL across three critical domains: marine environments, indoor reverberant spaces, and unmanned aerial vehicle (UAV) monitoring. In marine scenarios, complex-valued convolutional networks combined with adversarial transfer learning mitigate environmental mismatch and multipath interference through phase information fusion and domain adaptation strategies. For indoor high-reverberation conditions, attention mechanisms and multimodal fusion architectures achieve precise localization under low signal-to-noise ratios by adaptively weighting critical acoustic features. In UAV surveillance, lightweight models integrated with spatiotemporal Transformers address dynamic modeling of non-stationary noise spectra and edge computing efficiency constraints. Despite these advancements, current approaches face three core challenges: the insufficient integration of physical principles, prohibitive data annotation costs, and the trade-off between real-time performance and accuracy. Future research should prioritize physics-informed modeling to embed acoustic propagation mechanisms, unsupervised domain adaptation to reduce reliance on labeled data, and sensor-algorithm co-design to optimize hardware-software synergy. These directions aim to propel SSL toward intelligent systems characterized by high precision, strong robustness, and low power consumption. This work provides both theoretical foundations and technical references for algorithm selection and practical implementation in complex real-world scenarios. Full article

Objectives: This study aimed to determine the factors affecting the diagnostic efficacy of frozen sections for assessing tumor spread through air spaces (STAS) and to provide suggestions for the sampling of these frozen sections. Methods: Cases of invasive adenocarcinoma with a pathological diagnosis of stage IA-IIIB were screened, and frozen and paraffin sections were reviewed. Using paraffin sections as the gold standard, the consistency of frozen pathological diagnosis of STAS was calculated. Factors that may affect STAS diagnosis in frozen sections were screened, and a nomogram was drawn. Results: The sensitivity of frozen sections in STAS diagnosis was 55.4% (108/195), the specificity was 74.5% (254/321), and the kappa value was 0.35. In the subsequent logistic regression, the ratio of the tissue diameters of the frozen and paraffin sections, number of frozen section sheets, clarity of the tumor boundary, and number of alveoli from the peritumoral area to tissue edge were all statistically significantly significant (p < 0.05) factors affecting the frozen STAS diagnostic efficacy. Conclusions: The diagnostic efficacy of frozen sections for STAS is poor. In our study, the tissue diameter ratio of the frozen to paraffin sections, the number of frozen sections, the clarity of the tumor boundary, and the number of alveoli from the peritumoral tissue to the tissue edge were considered independent factors affecting diagnostic consistency. The accuracy of the frozen section analysis in STAS diagnosis can be improved by our reasonable suggestions on frozen sampling, making it a reliable indicator of the surgical method. Full article

Sargassum thunbergii is a dominant seaweed species in the intertidal zone along the coast of China. It provides various ecological services, such as primary productivity, marine carbon sequestration, and water purification. To investigate the population structure characteristics of Sargassum thunbergii, the Hegyi competition model was employed to quantify intraspecific competition within populations in the intertidal zone of Luhua Island, China. The results showed that the competition intensity decreased as a power function (y = 1.93x^−0.89, R² = 0.28) with increasing seaweed height. Intraspecific competition had minimal effects on seaweeds taller than 50 cm. Seaweeds at lower population levels exhibited more stable competition indices. Therefore, the model can reliably predict intraspecific competition intensity in Sargassum thunbergii. The sample circle method was applied to identify an optimal intraspecific competitive range of 50 cm for intertidal populations of Sargassum thunbergii. This study provides scientific guidance for seaweed spacing and rational harvesting during ecological restoration. Moreover, it offers valuable insight for conserving other macroalgae, such as Sargassum fusiforme, and restoring seaweed beds ecologically. Full article

With the rapid advancement of artificial intelligence, particularly in fields such as autonomous driving, drone delivery, and logistics automation, the demand for high-precision and robust navigation services has become critical. In complex and dynamic urban environments, the navigation capabilities of single-sensor systems struggle to meet the practical requirements of autonomous driving technology. To address this issue, we propose a multi-source fusion navigation algorithm tailored for dynamic urban canyon scenarios, aiming to achieve reliable and continuous state estimation in complex environments. In our proposed method, we utilize independent threads on a graphics processing unit (GPU) to perform real-time detection and removal of dynamic objects in visual images, thereby enhancing the visual accuracy of multi-source fusion navigation in dynamic scenes. To tackle the challenges of significant Global Navigation Satellite System (GNSS) positioning errors and limited satellite availability in urban canyon environments, we introduce a specialized GNSS Real-Time Kinematic (RTK) stochastic model for such settings. The navigation performance of the proposed algorithm was evaluated using public datasets. The results demonstrate that our RTK/INS/Vision integrated navigation algorithm effectively improves both accuracy and availability in dynamic urban canyon environments. Full article

Adipose-derived mesenchymal stem cells (ADSCs) have exhibited promising therapeutic potential in Alzheimer’s disease (AD), although the underlying mechanisms remain poorly understood. Previously established Alzheimer’s disease neuron models derived from Ts21-induced pluripotent stem cells (Ts21-iPSCs) have been shown to exhibit progressive amyloid beta accumulation during neuronal differentiation. In this study, we employed a Transwell co-culture system to investigate the interaction between neurons derived from Ts21-iPSCs and ADSCs. Our findings revealed that co-culture with ADSCs significantly enhanced the survival rate of AD neurons. Proteomics analysis identified significant upregulation of left–right determination factor 2 (LEFTY2) protein in the co-culture medium. Supplementation with 2 nM LEFTY2 markedly improved the survival and growth of AD neurons. Furthermore, LEFTY2 effectively downregulates the expression of apolipoprotein E4 and amyloid beta 1–42, along with attenuating phosphorylated tau231 levels in AD neurons. These results suggest the potential of LEFTY2 as a promising therapeutic candidate for Alzheimer’s disease. Full article

Accurate acquisition of two-dimensional digital maps of disaster sites is crucial for rapid and effective emergency response. The construction of two-dimensional digital maps using unmanned aerial vehicle (UAV) aerial images is not affected by factors such as signal interference, terrain, or complex building structures, which are common issues with methods like single-soldier image transmission or satellite imagery. Therefore, this paper investigates a method for modeling two-dimensional digital maps based on UAV aerial images. The proposed Canny edge-enhanced Speeded-Up Robust Features (C-SURF) algorithm in this method is designed to enhance the number of feature extractions and the accuracy of image registration. Compared to the SIFT and SURF algorithms, the number of feature points increased by approximately 44%, and the registration accuracy improved by about 16%, laying a solid foundation for feature-based image stitching. Additionally, a novel image stitching method based on the novel energy function is introduced, effectively addressing issues such as color discrepancies, ghosting, and misalignment in the fused image sequences. Experimental results demonstrate that the signal-to-noise ratio (SNR) of the fused images based on the novel energy function can reach an average of 36 dB. Full article

With the deployment of fifth-generation (5G) mobile communication technology and rapid advancements in artificial intelligence and edge computing, smart construction sites have emerged as a critical direction for the construction industry’s transformation and upgrading. However, existing intelligent Internet of Things (IoT) access algorithms often struggle to simultaneously meet practical requirements for high-efficiency data transmission rates, low latency, and secure privacy-aware access in the dynamic and complex environments of smart construction sites. To address this, this paper proposes a federated learning-based Multi-Objective Dueling Double Deep Q-Network (FL-MD3QN)-based IoT access algorithm for multi-site, multi-modal, multi-user IoT systems under the same Base Station (BS). First, a three-objective optimization mathematical model was established. The optimization goals include maximizing data transmission rates, minimizing transmission delays, and maximizing reliability. Constraints such as bandwidth, rate, bit error rate (BER), and security/privacy are defined. Second, the FL-MD3QN algorithm is developed to solve this optimization problem. This algorithm can adaptively adjust the access strategy to cope with the complex and ever-changing communication needs of smart construction sites and, by introducing a federated learning mechanism, it achieves collaborative optimization of multiple construction site IoT systems while ensuring user privacy. Simulation results demonstrated significant advantages of the FL-MD3QN algorithm. For latency, it achieved markedly lower delays across multi-modal services compared to benchmark algorithms, with the shortest training time. In transmission rates, FL-MD3QN delivered the highest average rates, particularly excelling in video services. Under high signal-to-noise ratio conditions, FL-MD3QN achieved exceptionally low BER values. Additionally, it attained high levels in average access success rate and average reward value, confirming its robust adaptability and optimization performance in complex smart construction environments. Full article

Streak cameras, known for their ultra-high spatiotemporal resolution, rely heavily on the spatial resolution capabilities of their core component, the streak tube, to ensure engineering stability. However, factors such as assembly inaccuracies and external magnetic fields, including geomagnetic interference, often cause deformation and shifts in the imaging plane. To enhance equipment stability and accelerate engineering advancements, a dual approach involving hardware improvements and computational imaging-based software corrections is essential. Future image reconstruction efforts in software require robust benchmarks; however, existing benchmarks are predominantly validated under idealized conditions, neglecting real-world interference factors. This study, grounded in electron optical imaging principles, experimentally confirms that the Lagrange–Helmholtz relationship remains valid within streak tube systems under geomagnetic field influences. These findings affirm that the imaging plane retains spatial resolution consistency despite such environmental disturbances. Consequently, the need for specific image orientations during reconstruction can be eliminated, enabling the development of more robust and efficient image reconstruction algorithms. Full article

Buffered chemical polishing (BCP) is an important and widely used polishing technique for superconducting radio-frequency (SRF) cavities made of niobium. A common problem encountered during BCP is the formation of bubbles and W-shaped pits on the cavity surface, which may severely limit the RF performance. We report a method to address the problem of W-shaped pits through optimizing the BCP acid ratio. We systematically investigate the effect of the BCP acid ratio through sample and cavity BCP experiments and determine an optimal ratio for the three acids. The new BCP recipe with the optimal acid ratio is verified through the development of niobium cavities with several different shapes, which are shown to be free of pits and demonstrate excellent RF performance; notably, several 3.9 GHz nine-cell cavities present unprecedented accelerating gradients. Furthermore, the findings suggest a simple pit-free BCP recipe that does not require ${\mathrm{H}}_3{\mathrm{PO}}_4$, using only HF and ${\mathrm{HNO}}_3$. The method proposed in this study is also appropriate for suppressing pit formation with other acid mixtures or when polishing other metal objects. Full article

Given that the preparation method of polysaccharides affects the functional properties, four types of acidic polysaccharides (BCP30-1a, BCP50-1a, BCP70-1a, and BCP90-1a) were prepared using the gradient ethanol precipitation method. Then, a series of chemical and instrumental analysis techniques were used to compare structural characteristics and morphology. Neuroprotective effects were explored using OGD/R-induced PC12 cells. The results showed that BCP30-1a, BCP50-1a, BCP70-1a, and BCP90-1a had similar characteristic groups and contained both β-glycosidic and α-glycosidic bonds. Their molecular weights, in descending order, were 198.398 kDa, 184.690 kDa, 184.556 kDa, and 184.217 kDa, respectively. In addition, the four polysaccharides contained different proportions of glycosidic bonds, namely, Manp-(1→, →5)-Araf-(1→, →3)-Galp (or GalAp)-(1→, →4)-Glcp-(1→ and →3,6)-Galp-(1→. BCP30-1a also contained a certain proportion of Galp-(1→, and each polysaccharide had different microscopic characteristics and good thermal stability. Finally, BCP50-1a, BCP70-1a, and BCP90-1a exhibited good cytoprotective effects on PC12 cells based on the OGD/R model. These findings provide a novel regulatory strategy for the functional activity of BCPs and offer scientific evidence supporting application in the research field of ischemic stroke. Full article

Melittin, a naturally occurring antimicrobial peptide, demonstrates broad-spectrum activity, effectively suppressing and eliminating both Gram-positive and Gram-negative bacteria, including specific drug-resistant strains. In this study, molecular simulation software was employed to investigate and modify the structure of melittin with the aim of synthesizing a modified peptide exhibiting enhanced antibacterial potency and assessing its bacteriostatic and antibacterial properties. The primary research objectives were as follows: 1. Preparation and characterization of melittin-modified peptide—Using molecular simulation software, the structure of the melittin-modified peptide was adjusted to predict its activity and select the most appropriate amino acid sequence. The peptide was synthesized through solid-phase peptide synthesis employing the Fmoc strategy and subsequently purified using liquid chromatography. The yield of the purified modified melittin was determined to be 30.97%, and the identity of the product was confirmed by LC-MS and MALDI-TOF-MS. 2. Evaluation of the antimicrobial activity of the melittin-modified peptide—The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of melittin and its modified peptide were measured using gradient dilution and plate counting techniques. The results revealed that both melittin and its modified peptide exhibited strong antibacterial efficacy against Gram-positive and Gram-negative bacteria, as well as certain drug-resistant strains. This showed that melittin and its modified peptide have the same antibacterial (killing) effect. A scanning electron microscope analysis indicated that both melittin and its modified peptide were capable of disrupting bacterial cell structures, leading to bacterial cell death. Full article

Seven new (1–7) and six known (8–13) statin derivatives were obtained from the deep-sea-derived fungus Penicillium viridicatum MCCC 3A00265. The structures assigned to the new compounds were based on a comprehensive analysis of the spectroscopic data, with absolute configurations established by Mosher analysis and biogenetic consideration. Most of the new compounds (1–5 and 7) share an octohydronaphthalene backbone, except that viridecalin F (6) possesses an uncommon naphthalene core. Viridecalins C (3) and F (6) and the two known compounds 9 and 11 exhibit considerable ability in reactivating mutant p53 protein at 10 μM, while viridecalin C showcases the most potent reactivation activity, indicating the potential of application in cancer therapy. Full article