Search Results (28,739)

MicroRNAs are closely associated with various physiological and pathological processes, making their in situ fluorescence imaging crucial for functional studies and disease diagnosis. Current methods for the in situ fluorescence imaging of microRNA predominantly rely on linear signal amplification, resulting in relatively weak imaging signals. This study introduces a Y-shaped cascade assembly (YCA) method for high-brightness microRNA imaging in cells. Triggered by target microRNA, catalytic hairpin assembly forms double-stranded DNA (H). Through annealing and hybridization, a Y-shaped structure (P) is created. These components assemble into DNA nanofluorescent particles with multiple FAM fluorophores, significantly amplifying fluorescence signals. Optimization experiments revealed that a 1:1 ratio of P to H and an assembly time of 60 min yielded the best results. Under these optimal conditions, the resulting fluorescent nanoparticles exhibited diameters of 664.133 nm, as observed by DLS. In Huh7 liver cancer cells, YCA generated DNA nanoparticles with a fluorescence intensity increase of 117.77%, triggered by target microRNA-21, producing high-intensity fluorescence images and enabling qualitative detection of microRNA-21. The YCA in situ imaging method offers excellent imaging quality and high efficiency, providing a robust and reliable analytical tool for the diagnosis and monitoring of microRNA-related diseases. Full article

Background/Objectives: Multi-scenario calculational methods have been used to evaluate proton teletherapy plan robustness but few studies have been performed to determine the accuracy of these calculational methods. This study evaluates a multi-scenario method by comparing calculations to measurements made in phantoms that simulate the effects of possible uncertainties. Methods: Plans were made using four phantoms in which the delivered dose was highly sensitive to positional and penetration uncertainties. The effects of alignment and penetration uncertainties on the dose distributions of each of those phantoms were simulated by performing calculations using nine different uncertainty scenarios and comparing the calculations to measurements with induced physical alignment displacements. Measured dose distributions were obtained by exposing films placed inside the phantoms and extracting multiple linear profiles. The maximum and minimum doses obtained for each of the calculational scenarios were compared with the measured dose profiles. In addition, comparisons of DVHs for nominal and uncertainty scenarios were performed. Results: The results showed that, under the influence of uncertainties, the minimum dose for the four phantoms decreased by more than 20 Gy, the V_95% coverage fluctuated by more than 10%, but the maximum dose parameter changed by less than 5 Gy. This was expected, as no margins for uncertainties were applied around the targets. The envelope bounded by the maximum and minimum possible calculated doses contained most of the measurements, although the shapes of the dose profiles displayed some mismatches for wedge and head phantoms. There were a few points where the measured maximum dose for bone and lung slab phantom cases was slightly higher than the maximum dose calculated from the nine scenarios. Conclusions: This study demonstrates that a nine-scenario method can adequately evaluate the robustness of simple mono-directional plans containing heterogeneities. Full article

Detecting small moving objects under challenging lighting conditions, such as overexposure and underexposure, remains a critical challenge in computer vision applications including surveillance, autonomous driving, and anti-UAV systems. Traditional RGB-based detectors often suffer from degraded object visibility and highly dynamic illumination, leading to suboptimal performance. To address these limitations, we propose a novel RGB-Event fusion framework that leverages the complementary strengths of RGB and event modalities for enhanced small object detection. Specifically, we introduce a Temporal Multi-Scale Attention Fusion (TMAF) module to encode motion cues from event streams at multiple temporal scales, thereby enhancing the saliency of small object features. Furthermore, we design a Sparse Noisy Gated Attention Fusion (SNGAF) module, inspired by the mixture-of-experts paradigm, which employs a sparse gating mechanism to adaptively combine multiple fusion experts based on input characteristics, enabling flexible and robust RGB-Event feature integration. Additionally, we present RGBE-UAV, which is a new RGB-Event dataset tailored for small moving object detection under diverse exposure conditions. Extensive experiments on our RGBE-UAV and public DSEC-MOD datasets demonstrate that our method outperforms existing state-of-the-art RGB-Event fusion approaches, validating its effectiveness and generalization under complex lighting conditions. Full article

With the widespread deployment of 5G technology, high-precision positioning in global navigation satellite system (GNSS)-denied environments is a critical yet challenging task for emerging 5G applications, enabling enhanced spatial resolution, real-time data acquisition, and more accurate geolocation services. Traditional methods relying on single-source measurements like received signal strength information (RSSI) or time of arrival (TOA) often fail in complex multipath conditions. To address this, the positional encoding multi-scale residual network (PE-MSRN) is proposed, a novel deep learning framework that enhances positioning accuracy by deeply mining spatial information from 5G channel state information (CSI). By designing spatial sampling with multigranular data and utilizing multi-source information in 5G CSI, a dataset covering a variety of positioning scenarios is proposed. The core of PE-MSRN is a multi-scale residual network (MSRN) augmented by a positional encoding (PE) mechanism. The positional encoding transforms raw angle of arrival (AOA) data into rich spatial features, which are then mapped into a 2D image, allowing the MSRN to effectively capture both fine-grained local patterns and large-scale spatial dependencies. Subsequently, the PE-MSRN algorithm that integrates ResNet residual networks and multi-scale feature extraction mechanisms is designed and compared with the baseline convolutional neural network (CNN) and other comparison methods. Extensive evaluations across various simulated scenarios, including indoor autonomous driving and smart factory tool tracking, demonstrate the superiority of our approach. Notably, PE-MSRN achieves a positioning accuracy of up to 20 cm, significantly outperforming baseline CNNs and other neural network algorithms in both accuracy and convergence speed, particularly under real measurement conditions with higher SNR and fine-grained grid division. Our work provides a robust and effective solution for developing high-fidelity 5G positioning systems. Full article

This study analyzed the use of a toolbox to evaluate the impact of agricultural activity on the water quality/status classification of a hydro-agricultural reservoir (Lage reservoir, Southern Portugal). The framework integrated the quantification of a group of 51 pesticides and ecotoxicological endpoints with organisms from different trophic categories (the bacterium Aliivibrio fischeri, the microalga Pseudokirchneriella subcapitata, and the crustaceans Daphnia magna and Thamnocephalus platyurus) at two sampling points in the reservoir (Lage (L) and Lage S (LS)) between 2018 and 2020. Over the three-year study, we quantified 36 of the 51 pesticides analyzed in the Lage reservoir. Total concentrations increased successively from 0.95 µg L⁻¹ to 1.99 and 2.66 µg L⁻¹. Among these, the pesticides most frequently detected were terbuthylazine (100% of detection) and metolachlor (83% of detection), with maximum concentrations of 115.6 and 85.5 µg L⁻¹, respectively. Samples from the LS site showed higher toxicity, where A. fischeri presented 30 min EC₅₀ values of 39–51%. Microalgae growth was consistently inhibited, correlating with agricultural activity, mainly the application of herbicides and insecticides, while D. magna feeding rates revealed no inhibitory effects in the Lage samples. The results highlight that although the detected pesticide levels were below regulatory limits, they still induced toxic effects in the tested organisms. The potential ecological status of the reservoir was classified as moderate, and the integration of the proposal toolbox allowed refinement of the classification of water status. The results demonstrated that this integrated approach, combining multiple assessment methods, establishes a more robust water quality evaluation methodology, allowing it to be used as a tool complementary to the WFD methodology. This proposal not only identified existing pollution impacts but also enabled (1) early detection of the toxic effects of emerging contaminants to prevent ecological damage; (2) proactive management through specific actions to restore water status; and (3) improved sustainable water use. Full article

The detection of damage in wind turbine blades is critical for ensuring their operational efficiency and longevity. This study presents a novel method for wind turbine blade damage detection, utilizing Gramian Angular Field (GAF) transformations of vibration signals in combination with Convolutional Neural Networks (CNNs). The GAF method enables the transformation of vibration signals, which are captured using a triaxial accelerometer, into angular representations that preserve temporal dependencies and reveal distinctive texture patterns that can be associated with structural damage. This transformation facilitates the capability of CNNs to identify complex features correlated with crack severity in wind turbine blades, thereby enhancing the precision and effectiveness of turbine fault diagnosis. The GAF-CNN model achieved a notable classification accuracy over 99.9%, demonstrating its robustness and potential for automated damage detection. Unlike traditional methods, which rely on expert interpretation and are sensitive to noise, the proposed system offers a more efficient and precise tool for damage monitoring. The findings suggest that this method can significantly enhance wind turbine condition monitoring systems, offering reduced dependency on manual inspections and improving early detection capabilities. Full article

Background: Differentiating pediatric pneumonia from acute bronchitis remains a persistent clinical challenge due to overlapping symptoms, often leading to diagnostic uncertainty and inappropriate antibiotic use. Methods: This study introduces DAPLEX, a structured ensemble learning framework designed to enhance diagnostic accuracy and reliability. A retrospective cohort of 868 pediatric patients was analyzed. DAPLEX was developed in three phases: (i) deployment of diverse base learners from multiple learning paradigms; (ii) multi-criteria evaluation and pruning based on generalization stability to retain a subset of well-generalized and stable learners; and (iii) complementarity-driven knowledge fusion. In the final phase, out-of-fold predicted probabilities from the retained base learners were combined with a consensus-based feature importance profile to construct a hybrid meta-input for a Multilayer Perceptron (MLP) meta-learner. Results: DAPLEX achieved a balanced accuracy of 95.3%, an F1-score of ~0.96, and a ROC-AUC of ~0.99 on an independent holdout test. Compared to the range of performance from the weakest to the strongest base learner, DAPLEX improved balanced accuracy by 3.5–5.2%, enhanced the F1-score by 4.4–5.6%, and increased sensitivity by a substantial 8.2–13.6%. Crucially, DAPLEX’s performance remained robust and consistent across all evaluated demographic subgroups, confirming its fairness and potential for broad clinical. Conclusions: The DAPLEX framework offers a robust and transparent pipeline for diagnostic decision support. By systematically integrating diverse predictive models and synthesizing both outcome predictions and key feature insights, DAPLEX substantially reduces diagnostic uncertainty in differentiating pediatric pneumonia and acute bronchitis and demonstrates strong potential for clinical application. Full article

In urban underground construction, Earth Pressure Balance (EPB) tunneling faces complex geological uncertainties and dynamic operational risks. Traditional safety management approaches often struggle under such conditions. This paper proposes an integrated safety resilience assessment framework for EPB tunneling that combines an entropy-weighted TOPSIS method, the Analytic Network Process (ANP), and an extension cloud model to capture interdependencies and uncertainties. A hierarchical indicator system with four primary dimensions (stability, redundancy, efficiency, and fitness) is constructed. The entropy-TOPSIS algorithm provides objective initial weights and scenario ranking, while ANP models the feedback relationships among criteria. The extension cloud model quantifies fuzziness in expert judgments and converts qualitative assessments into probabilistic resilience ratings. The methodology is applied to a case study of the EPB shield tunnel section of Jinan Metro Line 6 (China). The section’s resilience is classified as a medium level, which agrees with expert evaluation. The results demonstrate that the proposed approach yields accurate and robust safety resilience evaluations, supporting data-driven decision-making. This framework offers a quantitative tool for resilience-based safety management of shield tunneling projects, providing guidance for shifting from traditional risk control toward a resilience-enhancement strategy. Full article

Background/Objectives: Sleep plays a key role in female fertility. Sleep disturbances (SDis) during pregnancy are common and may negatively affect maternal health, contributing to an increased risk of perinatal depression and anxiety. Aim: The present prospective study aimed to examine the interplay of sleep, anxiety, and depression during the pregnancy and postpartum stages, comparing women who conceived naturally (NC) with those who conceived through assisted reproductive treatment (ART). Methods: The study included five timepoints: pre-pregnancy (t0), the end of each trimester (t1–t3), and the postpartum period (t4). SDis were assessed using the Pittsburgh Sleep Quality Index (PSQI), the Athens Insomnia Scale (AIS), the Epworth Sleepiness Scale (ESS), the Fatigue Severity Scale (FFS); perinatal depressive and anxiety symptoms were assessed using the Edinburgh Postnatal Depression Scale (EPDS). Demographic and clinical characteristics were also collected. Given the imbalance in group size and the dispersion of values, a negative binomial regression model with robust variances and Satterthwaite approximation for the degrees of freedom was applied. Results: Compared to women with NC (N = 37), those undergoing ART (N = 57) were more likely to be older (p < 0.001), married (p < 0.001), unemployed (p < 0.001), and have a history of thyroid disease (p = 0.008). Significant differences between different time points were observed in both NC (N = 37) and successfully conceived ART groups (N = 9) in all sleep, fatigue, and well-being parameters. Notably, at the end of the first trimester (t1), the ART group reported more severe insomnia symptoms (p = 0.02). Conclusions: SDis are common in pregnancy, but more pronounced during the first trimester among women on ART. These findings highlight the need for early screening and targeted psychological support during perinatal care. Full article

Objective: The Mediterranean diet (MedDiet) is characterized by its emphasis on plant-based foods, olive oil, and fish products, and has been associated with providing relevant fatty acids (FAs) for adolescent physiology. This study aims to investigate the relationship between adherence to the MedDiet and the FA composition of red blood cell (RBC) membranes in an adolescent population. Methods: The current research examines the relationship between MedDiet adherence, assessed using the KIDMED questionnaire, and the composition of RBC membranes, specifically measuring 22 FAs in a cross-sectional analysis of adolescents from two cohorts (mean age = 14.55). Baseline data from 552 participants with complete dietary adherence and FA information were analyzed using multivariable regression models and principal component analysis (PCA) as confirmatory analysis. All regression models were adjusted by age, sex, body mass index, physical activity, maternal education and cohort enrollment. Results: Main results shown that “Good adherence” to the MedDiet was positively associated with omega-3 FAs, including eicosapentaenoic acid (β = 0.34; 95% CI: 0.17, 0.52; p-value < 0.001) and docosahexaenoic acid (β = 0.29; 95% CI: 0.11, 0.46; p-value = 0.001), and inversely associated with specific omega-6 FAs, such as arachidonic acid (β = −0.28; 95% CI: −0.46, −0.11; p-value = 0.002) and adrenic acid (β = −0.19; 95% CI: −0.30, −0.08; p-value < 0.001). PCA identified distinct FA patterns, with “Good adherence” to the MedDiet being associated with an increase in the omega-3 FAs pattern (β = 0.32; 95% CI: 0.14, 0.49; p-value < 0.001). These findings remained robust after multiple test comparisons. Conclusions: This study underscores the potential of the MedDiet to promote optimal RBC FA composition in healthy adolescents, characterized by high levels of omega-3 FAs and reduced levels of arachidonic acid and adrenic acid in RBC membranes. Full article

Wet cracks caused by leakage often exhibit visual and structural distortions due to surface contamination, salt crystallization, and corrosion byproducts. These factors significantly degrade the performance of sensor- and vision-based crack detection systems. In moist environments, the initiation and propagation of cracks tend to be highly nonlinear and irregular, making it challenging to distinguish crack regions from the background—especially under visual noise such as reflections, stains, and low contrast. To address these challenges, this study proposes a segmentation framework that integrates a dedicated preprocessing pipeline aimed at suppressing noise and enhancing feature clarity, all without altering the underlying segmentation architecture. The pipeline begins with adaptive thresholding to perform initial binarization under varying lighting conditions. This is followed by morphological operations and connected component analysis to eliminate micro-level noise and restore structural continuity of crack patterns. Subsequently, both local and global contrast are enhanced using histogram stretching and contrast limited adaptive histogram equalization. Finally, a background fusion step is applied to emphasize crack features while preserving the original surface texture. Experimental results demonstrate that the proposed method significantly improves segmentation performance under adverse conditions. Notably, it achieves a precision of 97.5% and exhibits strong robustness against noise introduced by moisture, reflections, and surface irregularities. These findings confirm that targeted preprocessing can substantially enhance the accuracy and reliability of crack detection systems deployed in real-world infrastructure inspection scenarios. Full article

Unmanned Aerial Vehicles (UAVs) play a crucial role in the continuous monitoring and coordination of disaster relief operations, providing real-time information that aids in decision-making and resource allocation. However, optimizing the deployment of multi-UAV systems in disaster-stricken areas presents a significant challenge. This challenge arises due to conflicting objectives, such as maximizing coverage while minimizing energy consumption, critical to ensuring prolonged operational capability in dynamic and unpredictable environments. To address these challenges, this paper proposes a novel successive deployment method specifically designed for optimizing UAV placements in complex disaster relief scenarios. The overall optimization problem is decomposed into two NP-hard subproblems: the coverage problem and the Energy Consumption (EC) problem. To achieve maximum coverage of the affected area, we employ the Strong Weiszfeld (SW) algorithm to determine optimal UAV placement. Simultaneously, to minimize energy consumption while maintaining optimal coverage performance, we utilize the Steepest Descent with Goldstein (SDG) algorithm. This dual-algorithmic approach is tailored to balance the trade-offs between wide-area coverage and energy efficiency. We validate the effectiveness of the proposed SW + SDG method by comparing its performance against traditional deployment strategies across multiple scenarios. Experimental results demonstrate that our approach significantly reduces energy consumption while maintaining extensive coverage, and outperforms conventional algorithms. This not only ensures a more sustainable and long-lasting operational network but also enhances deployment efficiency and stability. These findings suggest that the SW + SDG algorithm is a robust and versatile solution for optimizing multi-UAV deployments in dynamic, resource-constrained environments, providing a balanced approach to coverage and energy efficiency. Full article

This paper presents a novel support vector machine (SVM) classification approach that simultaneously accounts for both overall and extreme misclassification errors via a dual-norm regularization strategy. Traditional SVMs minimize the $L_1$-norm of slack variables to control global misclassification, while least squares SVM (LSSVM) minimizes the sum of squared errors. In contrast, our method preserves the classical $L_1$-norm penalty to maintain overall classification fidelity and incorporates an additional $L_{\infty }$-norm term to penalize the largest slack variable, thereby constraining the worst-case margin violation. This composite objective yields a more robust and generalizable classifier, particularly effective when occasional large deviations disproportionately affect decision boundaries. The resulting optimization problem minimizes a regularized objective combining the model norm, the sum of slack variables, and the maximum slack variable, with two hyperparameters, $C_1$ and $C_2$, balancing global error against extremal robustness. By formulating the problem under convex constraints, the optimization remains tractable and guarantees a globally optimal solution. Experimental evaluations on benchmark datasets demonstrate that the proposed method achieves comparable or superior classification accuracy while reducing the impact of outliers and maintaining a sparse model structure. These results underscore the advantage of jointly enforcing $L_1$ and $L_{\infty }$ penalties, providing an effective mechanism to balance average performance with worst-case error sensitivity in support vector classification. Full article

To address the challenges of complex task matching, limited semantic representation, and low recommendation efficiency in remote sensing data acquisition for natural disasters, this study proposes a semantic path-guided recommendation method based on a knowledge graph framework. A disaster-oriented remote sensing knowledge graph is constructed by integrating entities such as disaster types, remote sensing tasks, observation requirements, sensors, and satellite platforms. High-order meta-paths with semantic closure are designed to model task–resource relationships structurally. A Meta-Path2Vec embedding mechanism is employed to learn vector representations of nodes through path-constrained random walks and Skip-Gram training, capturing implicit semantic correlations between tasks and sensors. Cosine similarity and a Top-K ranking strategy are then applied to perform intelligent task-driven sensor recommendation. Experiments on multiple disaster scenarios—such as floods, landslides, and wildfires—demonstrate the model’s high accuracy and robust stability. An interactive recommendation system is also developed, integrating data querying, model inference, and visual feedback, validating the method’s practicality and effectiveness in real-world applications. This work provides a theoretical foundation and practical solution for intelligent remote sensing data matching in disaster contexts. Full article

The digital economy is a key driver of industrial upgrading and regional growth. Focusing on Gansu Province—an under-represented, less-developed region in northwest China—this study constructs a multidimensional digital economy index (DEI) for 2009–2023 under a unified normalisation and weighting scheme. Two complementary MCDA approaches—entropy-weighted TOPSIS and SESP-SPOTIS—are implemented on the same 0–1 normalised indicators. Robustness is assessed using COMSAM sensitivity analysis and is benchmarked against a PCA reference. The empirical analysis then estimates log-elasticity models linking modern logistics production (MLP) and the DEI to the provincial GDP and sectoral value added, with inferences based on White heteroskedasticity–robust standard errors and bootstrap confidence intervals. Results show a steady rise in the DEI with a temporary dip in 2021 and recovery thereafter. MLP is positively and significantly associated with GDP and value added in the primary, secondary, and tertiary sectors. The DEI is positively and significantly associated with GDP, the primary sector, and the tertiary sector, but its effect is not statistically significant for the secondary sector, indicating a manufacturing digitalisation gap relative to services. Cross-method agreement and narrow sensitivity bands support the stability of these findings. Policy implications include continued investment in digital infrastructure and accessibility, targeted acceleration of manufacturing digitalisation, and the development of a “digital agriculture–smart logistics–green development” pathway to foster high-quality, sustainable regional growth. Full article