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With global climate warming intensifying, the Land Surface Temperature (LST) variation on the Tibetan Plateau (TP) has become a hot topic in environmental studies. This letter employs spatial analysis to identify LST distribution characteristics, the Pettitt test to detect abrupt changes, and Fast Fourier Transform (FFT) to analyse periodic variations. Analysing mean annual and seasonal LST from 2001 to 2020, the results indicate the following: (1) Over the past 20 years, the TP's LST increased slowly at 0.08°C per decade, with significant seasonal variations. Warming trends dominate in spring, summer, and autumn, shifting from the northern to the southwestern plateau. Autumn shows the most significant warming at 0.31°C per decade, while winter shows a cooling trend at −0.46°C per decade. (2) The Pettitt test reveals no statistically significant abrupt changes in mean annual and seasonal LST, though potential change points were detected in 2013 for mean annual LST, and in 2005, 2006, 2014, and 2017 for summer, spring, autumn, and winter, respectively. This indicates relative stability in interannual temperature changes, with some asynchronous seasonal variations. FFT analysis indicates dominant short-term variability with a period of 3.33 years and secondary cycles of 10 and 5 years. [ABSTRACT FROM AUTHOR]

With advancements in unmanned aerial vehicle (UAV) remote sensing technology, remote sensing images have emerged as a critical source of research data across various domains, including agriculture, forestry, and environmental research. UAVs fitted with diverse spectral sensors are capable of capturing diverse image modalities, presenting both challenges and opportunities for image semantic segmentation technology. Most existing semantic segmentation networks excel in processing images captured by visible light cameras and often fail to segment images captured by unmanned aerial vehicles under low-light conditions due to insufficient lighting, reduced visual clarity, high noise levels, and uneven illumination. Thermal infrared imaging sensors can capture thermal radiation information, which has the potential to improve segmentation accuracy when integrated with visible images. In this study, we introduce a novel semantic segmentation processing framework, which evaluates different fusing methods and fuses visible and thermal infrared images. The framework employs a lightweight deep learning model and is designed for accurate semantic segmentation on the fused images. Experiments are conducted on images collected in our unmanned aerial vehicle flight experiments and a public night-time dataset to assess the performance of our proposed approach. Experimental results show that our proposed framework achieves state-of-the-art performance in semantic segmentation tasks in low-light conditions. [ABSTRACT FROM AUTHOR]

The need for cross-comparison and validation of all-weather Land Surface Temperature (LST) products has arisen due to the release of multiple such products aimed at providing comprehensive all-weather monitoring capabilities. In this study, we focus on validating two well-established all-weather LST products (i.e. MLST-AS and TRIMS LST) against in-situ measurements obtained from four high-quality LST validation sites: Evora, Gobabeb, KIT-Forest, and Lake Constance. For the land sites, MLST-AS exhibits better accuracy, with RMSEs ranging from 1.6 K to 2.1 K, than TRIMS LST, the RMSEs of which range from 1.9 K to 3.1 K. Because MLST-AS pixels classified as "inland water" are masked out, the validation over Lake Constance is limited to TRIMS LST: it yields a RMSE of 1.6 K. Furthermore, the validation results show that MLST-AS and TRIMS LST exhibit better accuracy under clear-sky conditions than unclear-sky conditions across all sites. Since the accuracy of the all-weather LST products is considerably affected by the input clear-sky LST products, we further compare the all-weather LST with the corresponding input clear-sky LST to conduct an error source analysis. Considering the clear-sky pixels on MLST-AS directly using the estimates from MLST, the error source analysis is limited to examining TRIMS LST and its input (i.e. MODIS LST). The findings indicate that TRIMS LST is highly correlated with MODIS LST. The investigation and validation of the two selected all-weather LST products objectively evaluate their accuracy and stability, which provides important information for applications of these all-weather LST products. [ABSTRACT FROM AUTHOR]

Upper Palaeozoic lacustrine basin deposits not only record local tectonism but are also an archive to evaluate global palaeoclimate changes linked to the Late Palaeozoic Gondwanan ice age. The Tournaisian Horton Group of Nova Scotia, south‐east Canada, accumulated in rift basins following the final accretion of peri‐Gondwanan terranes to the Appalachians. Sedimentology, mineralogy and geochemistry of the well‐exposed sandstones and shales at the classic Blue Beach section (ca 353.5 to 352 Ma) reveal the interplay of local tectonism and global climatic controls on lacustrine sedimentation. The lacustrine depositional environment gradually transitioned from deep water offshore at the base of the section to wave‐dominated and fluvial‐dominated nearshore at the top. Multiple small transgressive–regressive sedimentation cycles have an average 21 ka duration, likely related to Milankovitch cyclicity. Unusually abundant soft‐sediment deformation structures and landslides are the sedimentary responses to frequent earthquakes during the most active phase of rift subsidence. The overall succession shows changes from a shallowing‐up balanced‐filled to an overfilled lacustrine basin. The chemical weathering intensity index and the Th/K ratio show a longer‐term trend from dry and cool conditions low in the section to humid and warm conditions near the top, with rapid change in the transition period. The section records the peak of the global mid‐Tournaisian carbon isotope excursion and the corresponding cooling event (354 Ma to approximately 351 Ma). The sedimentary succession is a response to long‐term and short‐term climatic cycles influencing lake level and sediment supply during the time of maximum rift basin subsidence recorded by the soft‐sediment deformation structures. [ABSTRACT FROM AUTHOR]

Integrated nanoelectromechanical systems (NEMS) with nm-scale piezoelectric films exhibit enormous advantages compared with traditional microelectromechanical systems. Herein, the flexible PZT ferroelectric films with 50–200 nm thickness were achieved, and their piezoelectric d33 rapidly enhanced with the thickness of film increasing and the thickness of mica substrate decreasing. The d33 value of the 80 nm-thick PZT film increases from 180 to 220 pC/N with the thickness of mica substrate reducing from 0.2 mm to 10 μm to largely decrease clamping strain between substrate and PZT film. Furthermore, the flexible mica substrate largely increases both the piezoelectric effect and the inverse piezoelectric effect of PZT films. These nm-scale PZT films with robust piezoelectricity will encourage the creation of new NEMS and piezoelectric devices that are flexible. [ABSTRACT FROM AUTHOR]

High‐power piezoelectric ceramics are typically driven to output vibration velocity (v0) under high AC electric fields. Herein, the Fe2O3 doped 0.125 Pb(Zn1/3Nb2/3) O3–0.075 Pb(Mn1/3Nb2/3)O3–0.8 Pb(Zr0.48Ti0.52)O3(PZMNZT–xFe; x = 0.05–0.35) piezoelectric ceramics were prepared to enhance v0, and the favorable comprehensive electrical properties, such as d33 = 315 pC/N, Qm = 1738, kp = 0.58, kt = 0.48, εr = 1156, tan δ = 0.4%, and Tc = 320°C, were achieved in the PZMNZT–0.15Fe ceramic. Most importantly, the PZMNZT–0.15Fe ceramic presented a reliable v0 of 0.90 m/s, which was 2.25 times of the commercial PZT4 ceramic (∼0.40 m/s). The excellent high‐power performance should be attributed to ordering functional elements such as crystal grains and ferroelectric domains. Overall, this work reveals that the PZMNZT–0.15Fe ceramic is competitive for high‐power applications. [ABSTRACT FROM AUTHOR]

To enhance the anti-submarine and search capabilities of multiple Unmanned Aerial Vehicle (UAV) groups in complex marine environments, this paper proposes a flexible action-evaluation algorithm known as Knowledge-Driven Soft Actor-Critic (KD-SAC), which can effectively interact with real-time environmental information. KD-SAC is a reinforcement learning algorithm that consists of two main components: UAV Group Search Knowledge Base (UGSKB) and path planning strategy. Firstly, based on the UGSKB, we establish a cooperation search framework that comprises three layers of information models: the data layer provides prior information and fundamental search rules to the system, the knowledge layer enriches search rules and database in continuous searching processes, and the decision layer utilizes above two layers of information models to enable autonomous decision-making by UAVs. Secondly, we propose a rule-based deductive inference return visit (RDIRV) strategy to enhance the knowledge base of search. The core concept of this strategy is to enable UAVs to learn from both successful and unsuccessful experiences, thereby enriching the search rules based on optimal decisions as exemplary cases. This approach can significantly enhance the learning performance of KD-SAC. The subsequent step involves designing an event-based UGSKB calling mechanism at the decision-making level, which calls a template based on the target and current motion. Finally, it uses a punishment function, and is then employed to achieve optimal decision-making for UAV actions and states. The feasibility and superiority of our proposed algorithm are demonstrated through experimental comparisons with alternative methods. The final results demonstrate that the proposed method achieves a success rate of 73.63% in multi-UAV flight path planning within complex environments, surpassing the other three algorithms by 17.27%, 29.88%, and 33.51%, respectively. In addition, the KD-SAC algorithm outperforms the other three algorithms in terms of synergy and average search reward. [ABSTRACT FROM AUTHOR]

The aircraft final assembly line is a highly labour-dependent manufacturing system, which leads to complexity and uncertainty. Random on-field disturbances mean that worker allocation is in an ever-changing state. Therefore, this article proposes a worker allocation optimization framework with efficiency and accuracy. First, an artificial neural network (ANN)-based model is developed as the alternative to simulation to evaluate worker allocations. Secondly, the non-dominated sorting genetic algorithm-II (NSGA-II)-based algorithm and ANN-based alternative model are integrated to realize the optimization of worker allocation. Finally, an initial population generation method combining historical and randomly generated allocations is proposed to make full use of the domain knowledge in historical data. Based on a real case, the ANN-based alternative model shows a strong advantage over the simulation in terms of efficiency, with competitive accuracy as well. The experimental results also indicate that the proposed initial population generation method effectively improves the performance of the algorithm. [ABSTRACT FROM AUTHOR]