Background Conduction blocks (CBs) play an important role in the initiation and perpetuation of atrial fibrillation and may be masked because of its direction- or rate dependency. Objective We aim to investigate how the highest amount and most severe CB at the right atrium (RA) can be unmasked by delivering programmed electrical stimulation (PES) from various directions and at different frequencies. Methods High-resolution epicardial mapping was performed at the middle of the RA on 40 patients during sinus rhythm (SR) and PES from the 4 sides of the mapping array at the average SR cycle length minus 50 ms (SR ₅₀) and 3 different S1S2 trains (S1 ₄₀₀, S2 ₃₀₀, S2 ₂₅₀ or S2 ₂₀₀). CB area percentage (CBA%) was defined as the proportion of electrodes with a local conduction time ≥12 ms. CB severity was defined as the 95th percentile of the conduction times over the lines of CB. Results CBA% increased from 0.6 [0-7.0]% during SR to 15.4 [12.3-19.2]% during S2 ₂₀₀ (P <.001). CB severity increased from 18 [14-29] ms during SR to 46 [29-53] ms during S2 ₂₀₀ (P <.001). PES increased CBA% over SR from 58% of patients during SR ₅₀ to 100% during S2 ₂₀₀. The largest increase in CBA% occurred during S2 ₂₅₀ during pacing from perpendicular (+7.3 [0.5-10.8]%) and opposite (+7.4 [3.5-15.5]%) to the direction of SR. Conclusion Perpendicular pacing opposite to the direction of SR using premature stimuli is optimal for unmasking CB. PES may also reduce CB in patients who already exhibit complex activation patterns during SR.

Clustered vehicle routing problems (CluVRPs) represent a complex class of combinatorial optimization problems with significant real-world relevance. They extend classic VRPs by introducing pre-specified customer clusters and requiring effective routing both between clusters and within each cluster. While numerous deep learning approaches have been developed to address the standard VRP, research on CluVRPs remains relatively limited, presenting opportunities and challenges for advancing solutions to more practical VRPs with cluster-related constraints. This paper offers a deep reinforcement learning (DRL) approach to solving CluVRPs. We propose a cluster-aware attention module in the encoder, along with inter-cluster and intra-cluster decoders to specialize the constructive policies within and between clusters. Symmetrical data augmentation is adopted in the training to improve the performance. Empirical results in different CluVRP variants manifest that the DRL method outperforms existing approaches, consistently offering advantages for various instances.

Constructed wetlands (CWs) have been proven to effectively immobilize plastic particles. However, little is known about the differences in the impact of varying sized plastic particles on nitrous oxide (N₂O) release, as well as the intervention mechanisms in CWs. Here, we built a lab-scale wetland model and introduced plastic particles of macro-, micro-, and nano-size at 100 μg/L for 370 days. The results showed that plastic particles of all sizes reduced N₂O release in CWs, with the degrees being the strongest for the Nano group, followed by Micro and Macro groups. Meanwhile, ¹⁵N- and ¹⁸O-tracing experiment revealed that the ammoxidation process contributed the most N₂O production, followed by denitrification. While for every N₂O-releasing process, the contributing proportion of N₂O in nitrification-coupled denitrification were most significantly cut down under exposing to macro-sized plastics and had an obvious increase in nitrifier denitrification in all groups, respectively. Finally, we revealed the three mechanism pathways of N₂O release reduction with macro-, micro-, and nano-sized plastics by impacting carbon assimilation (RubisCO activity), ammonia oxidation (gene amo abundance and HAO activity), and N-ion transmembrane and reductase activities, respectively. Our findings thus provided novel insights into the potential effects of plastic particles in CWs as an eco-technology.

(1) Background: Structural remodeling plays an important role in the pathophysiology of atrial fibrillation (AF). It is likely that structural remodeling occurs transmurally, giving rise to electrical endo-epicardial asynchrony (EEA). Recent studies have suggested that areas of EEA may be suitable targets for ablation therapy of AF. We hypothesized that the degree of EEA is more pronounced in areas of transmural conduction block (T-CB) than single-sided CB (SS-CB). This study examined the degree to which SS-CB and T-CB enhance EEA and which specific unipolar potential morphology parameters are predictive for SS-CB or T-CB. (2) Methods: Simultaneous endo-epicardial mapping in the human right atrium was performed in 86 patients. Potential morphology parameters included unipolar potential voltages, low-voltage areas, potential complexity (long double and fractionated potentials: LDPs and FPs), and the duration of fractionation. (3) Results: EEA was mostly affected by the presence of T-CB areas. Lower potential voltages and more LDPs and FPs were observed in T-CB areas compared to SS-CB areas. (4) Conclusion: Areas of T-CB could be most accurately predicted by combining epicardial unipolar potential morphology parameters, including voltages, fractionation, and fractionation duration (AUC = 0.91). If transmural areas of CB indeed play a pivotal role in the pathophysiology of AF, they could theoretically be used as target sites for ablation.

Surface melt plays a vital role in impacting the polar mass balance and global sea level rise. Over the past decades, synthetic aperture radar (SAR) imagery has garnered considerable attention due to its capacity to provide high-precision and long-term information. However, the traditional SAR-based large-scale surface melt detection methods utilizing co-orbit normalization predominantly depend on reference images and the precise spatial registration to mitigate geometric distortions arising from diverse incidence angles. Consequently, both the absence of reference imagery and the movement of ice sheets and shelves present challenges to the method. In this study, we address this issue by developing a reference-free deep learning network integrating the Convolutional Block Attention Module (CBAM) into DeepLabv3+ to automatically detect surface melt and establishing the surface melt dataset based on multi-temporal Sentinel-1 SAR imagery, encompassing diverse surface conditions of the Antarctic. Our model achieves an accuracy of 95.67%, surpassing the reference-based method and an advanced deep learning-based approach by 4.23% and 4.67%, respectively. Moreover, compared to 500 m resolution UMelt product and the kilometer-level results obtained from Advanced Scatterometer (ASCAT) and Special Sensor Microwave Imager Sounder (SSMIS), our model demonstrates the capability to accurately capture the small-scale melting patterns of ice shelves with a higher spatial resolution of 40 m. Notably, our findings underscore the dispensability of reference imagery in traditional methods through the formidable information extraction capabilities of deep learning. We finally applied the proposed method to extract and analyze the spatiotemporal characteristics of surface melt on the Larsen C Ice Shelf during the 2019/2020 period. The corresponding code of this study is at https://github.com/Tangyu35/Surface-melt-detection.

Objective: Patients with persistent atrial fibrillation (AF) have more electrical endo-epicardial asynchrony (EEA) during sinus rhythm (SR) than patients without AF. Prior mapping studies indicated that particularly unipolar, endo- and/or epicardial electrogram (EGM) morphology may be indicators of EEA. This study aim to develop a novel method for estimating the degree of EEA by using unipolar EGM characteristics recorded from either the endo- and/or epicardium. Methods: Simultaneous endo-epicardial mapping during sinus rhythm was performed in 86 patients. EGM characteristics, including unipolar voltages, low-voltage areas (LVAs), potential types (single, short/long double and fractionated potentials: SP, SDP, LDP and FP) and fractionation duration (FD) of double potentials (DP) and FP were compared between EEA and non-EEA areas. Asynchrony Fingerprinting Scores (AFS) containing quantified EGM characteristics were constructed to estimate the degree of EEA. Results: Endo- and epicardial sites of EEA areas are characterized by lower unipolar voltages, a higher number of LDPs and FPs and longer DP and FP durations. Patients with AF have lower potential voltages in EEA areas, along with alterations in the potential types. The EE-AFS, containing the proportion of endocardial LVAs and FD of epicardial DPs, had the highest predictive value for determining the degree of EEA (AUC: 0.913). Endo- and epi-AFS separately also showed good predictive values (AUC: 0.901 and 0.830 respectively). Conclusions: EGM characteristics can be used to identify EEA areas. AFS can be utilized as a novel diagnostic tool for accurately estimating the degree of EEA. These characteristics potentially indicate AF related arrhythmogenic substrates.

Temperature variation plays a significant role in the long-term structural behaviour of civil infrastructures, but very few quantitative studies have measured and analysed the infrastructures' global thermal dilation because of their large sizes and geometric complexities. The modern Differential Synthetic Aperture Radar Interferometry (DInSAR) technique has great potential in applications of their thermal dilation mapping and characterization due to the techniques' unique capabilities for use in large areas, with high-resolution, and at low-costs for deformation measurements. However, the practical application of DInSAR in thermal dilation estimation is limited by difficulty in the precise separation from the residual topographic phase and the trend deformation phase. Moreover, due to a lack of thermal dilation characteristics analyses in previous studies, the thermal dilation mechanisms are still unclear to users, which restricts the accurate understanding of the thermal dilation evolution process. Given the above challenges, an advanced multi-temporal DInSAR approach is proposed in this study, and the effectiveness of this approach was presented using three cases studies concerning different infrastructure types. In this method, the coherent, incoherent, and semantic information of structures were combined in order to refine the detection of point-like targets (PTs). Interferometric subsets with small temporal baselines and temperature differences were used for high-resolution topography estimation. A pre-analysis was adopted to determine the transmission direction, spatial pattern, and temporal variation of the thermal dilation. Then, both the traditional least squares estimation and our robust coherence-weighted least squares regression analysis were performed between the time series displacements and the corresponding temperatures to quantitatively estimate the thermal dilation model. The results were verified in terms of the estimated linear thermal dilation coefficient, which indicates the greater reliability of our method. Furthermore, the thermal dilation characteristics of different civil infrastructure types were analysed, which facilitates a greater understanding of the thermal dilation evolution process of civil infrastructures.

Arch bridges are important transportation infrastructures widely distributed in China, but they are prone to structural defects due to aging without routine inspection and maintenance. Therefore, Structural Health Monitoring (SHM) of these bridges is urgently needed by civil engineers to effectively reduce the risk of bridge damage or collapse on public safety. An essential method for SHM, the modern Differential Synthetic Aperture Radar Interferometry (DInSAR) technique, can detect subtle deformation of bridges at relatively low costs. Nevertheless, identifying dense point-like targets (PTs) on such partially coherent arch bridges in SAR image is more difficult than that for other man-made objects, owing to their complex structures and backscattering behaviors. Furthermore, the complex mechanical properties of arch bridges, due to the varying arch-beam interactions, make it hard to separate the surface deformation and thermal dilation accurately, and the lack of specific structural knowledge, that can help to understand the deformation evolution process, limits the global structural risk assessment. Aiming at these problems, we developed a structure-driven multi-temporal DInSAR approach for arch bridge-specific SHM. By introducing three structure-driven steps, i.e. backscattering geometrical interpretation, linear thermal dilation estimation and validation, and Deformation Feature Points (DFPs) based risk assessment, into the traditional DInSAR method, the reliability of PTs identification, thermal dilation separation, and structural risk assessment for arch bridges are significantly improved. The effectiveness of our approach was fairly presented by two case studies of the Rainbow and Lupu bridges, and the experimental results were verified by leveling benchmark validation, cross-sensor comparison, as well as structural-reliability assessment. Our results revealed that arch bridges exhibit a similar pattern of PTs distribution that is dense around piers and sparse on the spans, as well as a symmetrical progressive pattern of surface deformation with the subsidence increasing from piers and reaching a peak at the central spans. In contrast, magnitudes and mechanisms of thermal dilation are different, and highly dependent on the materials and structural characteristics of specific bridges.

Morphing wings have a large potential to improve the overall aircraft performances, in a way like natural flyers do. By adapting or optimising dynamically the shape to various flight conditions, there are yet many unexplored opportunities beyond current proof-of-concept demonstrations. This review discusses the most prominent examples of morphing concepts with applications to two and three-dimensional wing models. Methods and tools commonly deployed for the design and analysis of these concepts are discussed, ranging from structural to aerodynamic analyses, and from control to optimisation aspects. Throughout the review process, it became apparent that the adoption of morphing concepts for routine use on aerial vehicles is still scarce, and some reasons holding back their integration for industrial use are given. Finally, promising concepts for future use are identified.