Search Results (133)

Accurate performance prediction for large offshore wind turbines requires a principled treatment of uncertainty in both the wind resource and the rotor design parameters. In the present work, we develop a surrogate-based, multi-level uncertainty quantification (UQ) framework coupling a physics-based Blade Element Momentum (BEM) solver with a spectral Polynomial Chaos Expansion (PCE) surrogate that replaces the expensive Monte Carlo loop and apply it to the IEA 15 MW offshore reference wind turbine. The framework is completed by Sobol variance-based global sensitivity analysis. The contribution is methodological rather than algorithmic: although each individual ingredient (PCE, Sobol, BEM, and Jensen) is well established, their joint deployment in a single, internally consistent, end-to-end probabilistic workflow that simultaneously delivers (i) aerodynamic–structural UQ with analytical Sobol ranking, (ii) a like-for-like cross-comparison of three reference turbines, (iii) a quantitative leading-edge icing degradation study, and (iv) a farm-level wake-steering optimization on the same IEA 15 MW reference rotor yields a unified probabilistic envelope from which manufacturing tolerances, cold-climate investment thresholds, and farm-layout/control trade-offs can be read off consistently. Five input parameters are treated as random variables: hub-height wind speed (Weibull, k = 2.2, c = 9.8 m/s), air density, blade chord length, twist angle, and rotor speed. A degree-4 sparse PCE is built by non-intrusive spectral projection using N = 5000 Sobol quasi-random realizations, which allows the Sobol indices to be recovered analytically from the expansion coefficients at essentially no extra cost. Three parallel engineering studies complement the core UQ analysis: (A) a head-to-head comparison of the NREL 5 MW, DTU 10 MW, and IEA 15 MW reference turbines; (B) a quantitative assessment of leading-edge ice accretion at four severity levels; and (C) a Jensen-based wake optimization for a 25-turbine offshore array with static wake steering. The main results are as follows: the turbine reaches C_p,max = 0.480 at λ_opt = 8.51, and an annual energy production (AEP) of 71,261 MWh/year (PCE: 70,840 ± 2,140 MWh/year, 95% CI). Wind speed emerges as the dominant driver of C_p variance (S₁ = 0.412), followed by blade twist (0.198) and chord (0.143). Severe icing (30 kg/m) reduces C_p by 18.2% and increases the blade-root Damage Equivalent Load (DEL) by 18.5%. For the array, the optimal spacing (s_x = 8D, s_y = 6D) gives a farm efficiency of 89.6% and 1296 GWh/year, and a 15° wake-steering offset adds a further +3.2% to farm AEP. Compared with plain Monte Carlo, the sparse PCE delivers the same statistics with about 36% fewer model evaluations and a relative error below 0.8%. Full article

As floating offshore wind turbines (FOWTs) scale towards 10 MW+ capacities, suppressing wave-induced rotational resonance becomes critical for system survivability. This study introduces an optimized, highly symmetrical four-float semi-submersible platform, explicitly tailored to support the DTU 10 MW wind turbine and paired with an orthogonal four-point mooring system. Using three-dimensional linear potential flow theory via ANSYS AQWA, comprehensive frequency- and time-domain hydrodynamic evaluations were conducted. To address the inherent limitations of inviscid potential flow assumptions, an empirical added-damping method was implemented. Quantitative results demonstrate a drastic reduction in motion responses: the peak Response Amplitude Operator (RAO) for heave decreased by 68.6% (from 1.945 m/m to 0.610 m/m). Most notably, the peak RAOs for the critical rotational degrees of freedom—pitch and roll—were reduced by over 92% (from 2.080 °/m and 2.216 °/m to ~0.168 °/m, respectively). Ultimately, compared to traditional asymmetric three-float concepts, this novel symmetric omnidirectional layout provides a more uniform restoring stiffness. The resulting suppression of pitch and roll resonance results in a profound reduction in tower-base bending moments and gyroscopic loads, thereby significantly enhancing the dynamic stability, safety margins, and fatigue life of the 10 MW FOWT under extreme survival sea states. Full article

In the field of autonomous driving, camera-based perception models are mostly trained on clear weather data. Models designed to handle specific weather conditions often lack generalization to dynamically changing environments and primarily focus on weather removal rather than robust perception. This paper proposes a semantic-enabled network for object detection under diverse weather conditions. Semantic information enables the model to generate plausible content in missing regions and accurately delineate object boundaries. It also preserves visual coherence and realism across both restored and original image areas, thereby facilitating image transformation and object recognition. Specifically, our architecture consists of a Preprocessing Unit (PPU) and a Detection Unit (DTU), where the PPU utilizes a U-shaped network enriched with semantics to refine degraded images, and the DTU integrates this semantic information for object detection using a modified YOLO network. Extensive experiments demonstrate that the proposed method achieves mAP improvements ranging from 1.49% to 8.78% compared with existing approaches across multiple benchmark datasets under various weather conditions. These results demonstrate the effectiveness of semantic guidance in image enhancement and object detection, providing a comprehensive framework for improving detection performance. The source code will be made publicly available. Full article

Leading-edge erosion of wind turbine blades caused by repeated raindrop impingement can significantly reduce power output and increase maintenance costs. This study develops a rain erosion atlas for Japan over 11 years from 2006 to 2016 based on the CRIEPI-RCM-Era2 dataset. The NREL 5 MW, DTU 10 MW, and IEA 15 MW wind turbines were employed to evaluate the incubation time (erosion onset time) of commercial polyurethane-based coating at the blade tip. Erosion progression was simulated using an empirical damage model that relates raindrop impingement and impact velocity to the incubation time. The rain erosion atlas reveals a clear correlation between wind turbine size and erosion risk: the NREL 5MW turbine shows an incubation time of 3–12 years, the DTU 10MW turbine 1–4 years, and the IEA 15MW turbine 0.5–2 years. Shorter incubation times are observed on the Pacific Ocean side, where annual precipitation is higher than on the Sea of Japan side. Additionally, the influence of coating degradation due to ultraviolet radiation was assessed using solar radiation data, revealing a further reduction in incubation time on the Pacific Ocean side. Finally, the potential of erosion-safe mode operation was examined, demonstrating its effectiveness in alleviating erosion progression. Full article

Background/Objectives: Long-term exposure to polychlorinated biphenyls (PCBs), including the mixture of PCBs in Aroclor1260 (Ar1260), results in metabolic dysfunction-associated steatotic liver disease (MASLD) in mice and humans. While the effects of PCBs on gene expression are well-documented using short-read RNA sequencing, the regulatory roles of alternative splicing (AS) and differential transcript usage (DTU) are uncharacterized. AS has been implicated in MASLD. Previously, we reported that chronic (34 wks.) exposure of normal, low-fat-diet (LFD)-fed male mice to Ar1260 resulted in 12 hepatic RNA modifications. Proteomic analysis of these same liver samples identified Ar1260 exposure-associated changes in selenoproteins: GPX4 and SELENBP2 were increased and SELENOS and SELENOF were reduced. Methods: Here we used long-read isoform sequencing (IsoSeq) to identify DTU in four genes in the Ar1260-exposed livers: Adpgk, Blvra, Mup2, and Ndufaf6. Results: Network analysis of the corresponding proteins revealed a strong association with pathways relevant to MASLD including lipid metabolism, glycolysis, and oxidative stress. Conclusions: These findings suggest that PCB exposure alters the transcript isoform landscape of key metabolic genes involved in MASLD. Full article

Chagas disease (CD), caused by Trypanosoma cruzi, is a neglected tropical illness affecting 6–8 million people in Latin America. Reaching scholarly consensus on the host response to T. cruzi infection remains a significant challenge, primarily due to substantial heterogeneity in outcomes driven by both the choice of animal model and the infecting parasite’s discrete typing unit (DTU). This variability complicates the evaluation and comparison of new therapeutic compounds against existing drugs, namely benznidazole and nifurtimox. This study provides a comprehensive, kinetic, multifaceted characterization of the acute infection using the highly virulent T. cruzi Y strain (TcII) in outbred Swiss mice. Here, crucial infection parameters are presented, including the optimal infective dose, the parasitemia dynamics, tissue damage markers, hematological profiles, cytokine production (Th1/Th2/Th17/Th22), and molecular parasite identification in target organs (heart, colon, esophagus, spleen, and liver) across the span of the infection. The novelty of this study lies in the kinetic integration of these parameters within a defined model; rather than presenting isolated data points, we demonstrate how the biochemical, physiological, and clinical signs and immunological responses, with the resulting organ involvement, evolve and interact over time. To complete the report, a necropsy evaluation was performed at the end of the acute, fatal infection, and it is presented here. This study fulfills a long-standing recommendation from diverse drug discovery groups for the creation of a definitive reference model to standardize preclinical testing for anti-Chagasic agents. Full article

Traditional fusion methods for integrating multi-source gravity data rely on predefined mathematical models that inadequately capture complex nonlinear relationships, particularly at wavelengths shorter than 10 km. We developed a convolutional neural network incorporating differential marine geodetic data (DMGD-CNN) to enhance marine gravity anomaly recovery from HY-2A satellite altimetry. The DMGD-CNN framework encodes spatial gradient information by computing differences between target points and their surrounding neighborhoods, enabling the model to explicitly capture local gravity field variations. This approach transforms absolute parameter values into spatial gradient representations, functioning as a spatial high-pass filter that enhances local gradient information critical for short-wavelength gravity signal recovery while reducing the influence of long-wavelength components. Through systematic ablation studies with eight parameter configurations, we demonstrate that incorporating first- and second-order seabed topography derivatives significantly enhances model performance, reducing the root mean square error (RMSE) from 2.26 mGal to 0.93 mGal, with further reduction to 0.85 mGal achieved by the differential learning strategy. Comprehensive benchmarking against international gravity models (SIO V32.1, DTU17, and SDUST2022) demonstrates that DMGD-CNN achieves 2–10% accuracy improvement over direct CNN predictions in complex topographic regions. Power spectral density analysis reveals enhanced predictive capabilities at wavelengths below 10 km for the direct CNN approach, with DMGD-CNN achieving further precision enhancement at wavelengths below 5 km. Cross-validation with independent shipborne surveys confirms the method’s robustness, showing 47–63% RMSE reduction in shallow water regions (<2000 m depth) compared to HY-2A altimeter-derived results. These findings demonstrate that deep learning with differential marine geodetic features substantially improves marine gravity field modeling accuracy, particularly for capturing fine-scale gravitational features in challenging environments. Full article

Distribution terminal units (DTUs) play critical roles in smart grid for supporting data acquisition, remote monitoring, and fault management. A single DTU generates continuous data streams, imposing new challenges on data processing. To tackle these issues, a cloud-edge collaboration-based data processing method is introduced for DTU edge clusters. First, considering the load imbalance degree of DTU data queues, a cloud-edge integrated data processing architecture is designed. It optimizes edge server selection, the offloading splitting ratio, and edge-cloud computing resource allocation in a collaboration mechanism. Second, an optimization problem is formulated to maximize the weighted difference between the total data processing volume and the load imbalance degree. Next, a cloud-edge collaboration-based data processing method is proposed. In the first stage, cloud-edge collaborative data offloading based on the load imbalance degree, and a data volume-aware deep Q-network (DQN) is developed. A penalty function based on load fluctuations and the data volume deficit is incorporated. It drives the DQN to evolve toward suppressing the fluctuation of load imbalance degree while ensuring differentiated long-term data volume constraints. In the second stage, cloud-edge computing resource allocation based on adaptive differential evolution is designed. An adaptive mutation scaling factor is introduced to overcome the gene overlapping issues of traditional heuristic approaches, enabling deeper exploration of the solution space and accelerating global optimum identification. Finally, the simulation results demonstrate that the proposed method effectively improves the data processing efficiency of DTUs while reducing the load imbalance degree. Full article

Alternative splicing (AS) is a fundamental mechanism governing transcriptomic diversity and cellular identity. Although 293T (human embryonic kidney) and A549 (human lung adenocarcinoma) cell lines are widely used, cell-type-specific splicing dynamics—including responses to receptor overexpression—remain incompletely characterized. To address this, we integrated Oxford Nanopore long-read sequencing with BGI short-read data to profile transcriptomes under both basal and GPCR-overexpressing conditions (ADORA3 in 293T; P2RY12 in A549). Full-length isoform analysis using FLAIR and SQANTI3 revealed extensive transcriptomic complexity, including 18.02% novel isoforms in 293T and 19.52% in A549 cells. We found that 293T cells exhibited a stable transcriptome architecture enriched in splicing-related pathways, whereas A549 cells underwent broader transcriptional remodeling linked to tumorigenic processes. These findings suggest that 293T cells may be a suitable model for investigating splicing regulation, while A549 cells could serve as a relevant system for exploring tumor-related transcriptome dynamics. Our work elucidates context-dependent AS regulation and underscores the value of integrating long-read sequencing with FLAIR/SQANTI3 for dissecting cell-state-specific transcriptome dynamics. Full article

Electric vehicle (EV) charging presents both a challenge and an opportunity for modern power systems, particularly in workplace environments with grid constraints and dynamic energy pricing. This study presents a real-life implementation and experimental validation of a hierarchical distributed control system for smart EV charging. The proposed architecture combines upper-level receding horizon optimization with lower-level priority-based dispatch, enabling cost-efficient energy allocation and fair distribution among EVs. The system was deployed at the Risø campus of the Technical University of Denmark (DTU) and tested over two days under realistic operational conditions, including heterogeneous EV behavior and limited grid capacity. The control system demonstrated autonomous operation, responsiveness to price signals, and effective coordination between control layers. High energy delivery rates were achieved, nearly 100% on the first test day and close to 90% on the second, despite operating under a constrained energy budget. The study also documents practical challenges encountered during deployment, such as charger communication faults and EV-side issues, and proposes adaptation strategies. These results confirm the feasibility of distributed smart charging in real-world conditions and provide actionable insights for future implementations. Full article

The use of digital technologies can break down information barriers in rural areas, thereby creating crucial conditions for the widespread adoption of green agricultural technologies (GATs) among farmers. To explore the relationship between digital technology use (DTU) and farmers’ adoption of GATs, this study draws on 18 in-depth interviews and 608 survey responses collected from rice farmers in Sichuan Province, China. By adopting a mixed-methods design, it offers a comprehensive examination of the mechanisms through which digital technology use (DTU) promotes the adoption of green agricultural technologies (GATs) among farmers. Grounded theory analysis reveals that the DTU–GATs adoption pathway can be conceptualized within a “condition–process–outcome” framework. Specifically, digital infrastructure, farmers’ capital endowment, and practical needs constitute the foundational conditions, while technology perception and the regional soft environment act as key mediating processes. The ultimate outcomes include improvements in economic performance, social well-being, and ecological sustainability. Empirical evidence confirms that DTU significantly promotes the adoption of GATs, primarily by enhancing farmers’ perceptions of technology and improving the agricultural soft environment at the regional level. Moreover, the effects of DTU display substantial heterogeneity across different types of green technologies and among various farmer groups. These findings highlight the importance of strengthening digital infrastructure in rural areas, enhancing farmers’ digital literacy and capacity, and leveraging digital tools to tailor the dissemination and guidance of GATs. Such efforts are essential to raise farmers’ awareness, foster a supportive soft environment for sustainable agriculture, and ultimately advance the adoption of GATs. Full article

The size and flexibility of offshore turbine blades manufactured from composite materials have continuously increased in recent years. In this context, accurate and efficient aeroelastic analyses are important for designing and safely assessing long, flexible blades. Existing linear beam models need to be revised to offer accurate estimates of the geometric nonlinear effects triggered by large displacements. Nonlinear, geometrically exact beam models that have already been extensively used for the above purpose are generally difficult to converge and inefficient. We propose a novel co-rotational beam model for the nonlinear analysis of wind turbine blades. The method adopts vector complement to resolve rotation vector singularity problems. A complete anisotropic cross-sectional stiffness matrix and Timoshenko beam elements are introduced to capture full coupling effects. The method also considers the anisotropy and taper effects caused by the non-uniformity of chord length and material distributions. We established the nonlinear aeroelastic model of the DTU 10 MW turbine, and the results showed that the taper effect dramatically reduced the blade torsion angle by up to 31.44% under rated wind speed. Meanwhile, static beam experiments demonstrate that the accuracy error of the current method is only 1.78%, which is significantly lower than the 17.8% error of the conventional finite element beam method. Full article

The 3D point cloud generated by MVSNet has good scene integrity but lacks sensitivity to details, causing holes and non-dense areas in flat and weak-texture regions. To address this problem and enhance the point cloud information of weak-texture areas, the BCA-MVSNet network is proposed in this paper. The network integrates the Bidirectional Feature Pyramid Network (BIFPN) into the feature processing of the MVSNet backbone network to accurately extract the features of weak-texture regions. In the feature map fusion stage, the Coordinate Attention (CA) mechanism is introduced into 3DU-Net to obtain the position information on the channel dimension related to the direction, improve the detail feature extraction, optimize the depth map and improve the depth accuracy. The experimental results show that BCA-MVSNet not only improves the accuracy of detail texture reconstruction, but also effectively controls the computational overhead. In the DTU dataset, the Overall and Comp metrics of BCA-MVSNet are reduced by 10.2% and 2.6%, respectively; in the Tanksand Temples dataset, the Mean metrics of the eight scenarios are improved by 6.51%. Three scenes are shot by binocular camera, and the reconstruction quality is excellent in the weak-texture area by combining the camera parameters and the BCA-MVSNet model. Full article

Three-Dimensional Gaussian Splatting (3DGS) has revolutionized novel-view synthesis, enabling real-time rendering of high-quality scenes. Two-Dimensional Gaussian Splatting (2DGS) improves geometric accuracy by replacing 3D Gaussians with flat 2D Gaussians. However, the flat nature of 2D Gaussians reduces mesh quality on volumetric surfaces and results in over-smoothed reconstruction. To address this, we propose Dual-Dimensional Gaussian Splatting (DDGS), which integrates both 2D and 3D Gaussians. First, we generalize the homogeneous transformation matrix based on 2DGS to initialize all Gaussians in 3D. Subsequently, during training, we selectively convert Gaussians into 2D representations based on their scale. This approach leverages the complementary strengths of 2D and 3D Gaussians, resulting in more accurate surface reconstruction across both flat and volumetric regions. Additionally, to mitigate over-smoothing, we introduce gradient-based regularization terms. Quantitative evaluations on the DTU and TnT datasets demonstrate that DDGS consistently outperforms prior methods, including 3DGS, SuGaR, and 2DGS, achieving the best Chamfer Distance and F1 score across a wide range of scenes. Full article

With the rapid advancement of smart forestry, 3D reconstruction and the extraction of structural parameters have emerged as indispensable tools in modern forest monitoring. Although traditional methods involving LiDAR and manual surveys remain effective, they often entail considerable operational complexity and fluctuating costs. To provide a cost-effective and scalable alternative, this study introduces FS-MVSNet—a multi-view image-based 3D reconstruction framework incorporating feature pyramid structures and attention mechanisms. Field experiments were performed in three representative forest parks in Beijing, characterized by open canopies and minimal understory, creating the optimal conditions for photogrammetric reconstruction. The proposed workflow encompasses near-ground image acquisition, image preprocessing, 3D reconstruction, and parameter estimation. FS-MVSNet resulted in an average increase in point cloud density of 149.8% and 22.6% over baseline methods, and facilitated robust diameter at breast height (DBH) estimation through an iterative circle-fitting strategy. Across four sample plots, the DBH estimation accuracy surpassed 91%, with mean improvements of 3.14% in AE, 1.005 cm in RMSE, and 3.64% in rRMSE. Further evaluations on the DTU dataset validated the reconstruction quality, yielding scores of 0.317 mm for accuracy, 0.392 mm for completeness, and 0.372 mm for overall performance. The proposed method demonstrates strong potential for low-cost and scalable forest surveying applications. Future research will investigate its applicability in more structurally complex and heterogeneous forest environments, and benchmark its performance against state-of-the-art LiDAR-based workflows. Full article