３Ｄ計測による自然景観及び都市造形物の点群データ処理とその応用

岩手県立大

SelFLoc: Selective Feature Fusion for Large-scale Point Cloud-based Place Recognition

Point cloud-based place recognition is crucial for mobile robots and autonomous vehicles, especially when the global positioning sensor is not accessible. LiDAR points are scattered on the surface of objects and buildings, which have strong shape priors along different axes. To enhance message passing along particular axes, Stacked Asymmetric Convolution Block (SACB) is designed, which is one of the main contributions in this paper. Comprehensive experiments demonstrate that asymmetric convolution and its corresponding strategies employed by SACB can contribute to the more effective representation of point cloud feature. On this basis, Selective Feature Fusion Block (SFFB), which is formed by stacking point- and channel-wise gating layers in a predefined sequence, is proposed to selectively boost salient local features in certain key regions, as well as to align the features before fusion phase. SACBs and SFFBs are combined to construct a robust and accurate architecture for point cloud-based place recognition, which is termed SelFLoc. Comparative experimental results show that SelFLoc achieves the state-of-the-art (SOTA) performance on the Oxford and other three in-house benchmarks with an improvement of 1.6 absolute percentages on mean average recall@1

A Deep-learning Real-time Bias Correction Method for Significant Wave Height Forecasts in the Western North Pacific

Significant wave height is one of the most important parameters characterizing ocean waves, and accurate numerical ocean wave forecasting is crucial for coastal protection and shipping. However, due to the randomness and nonlinearity of the wind fields that generate ocean waves and the complex interaction between wave and wind fields, current forecasts of numerical ocean waves have biases. In this study, a spatiotemporal deep-learning method was employed to correct gridded SWH forecasts from the ECMWF-IFS. This method was built on the trajectory gated recurrent unit deep neural network,and it conducts real-time rolling correction for the 0-240h SWH forecasts from ECMWF-IFS. The correction model is co-driven by wave and wind fields, providing better results than those based on wave fields alone. A novel pixel-switch loss function was developed. The pixel-switch loss function can dynamically fine-tune the pre-trained correction model, focusing on pixels with large biases in SWH forecasts. According to the seasonal characteristics of SWH, four correction models were constructed separately, for spring, summer, autumn, and winter. The experimental results show that, compared with the original ECMWF SWH predictions, the correction was most effective in spring, when the mean absolute error decreased by 12.972~46.237%. Although winter had the worst performance, the mean absolute error decreased by 13.794~38.953%. The corrected results improved the original ECMWF SWH forecasts under both normal and extreme weather conditions, indicating that our SWH correction model is robust and generalizable.Comment: 21 page

Spatial distribution and diversity of the heterotrophic flagellates in the Cosmonaut Sea, Antarctic

As predators of bacteria and viruses and as food sources for microzooplankton, heterotrophic flagellates (HFs) play an important role in the marine micro-food web. Based on the global climate change’s impact on marine ecosystems, particularly sea ice melting, we analyzed the community composition and diversity of heterotrophic flagellates, focusing on the Antarctic Cosmonaut Sea. During the 36th China Antarctic research expedition (2019-2020), we collected seawater samples, subsequently analyzing HFs through IlluminaMiSeq2000 sequencing to assess community composition and diversity. Notable variations in HFs abundance were observed between the western and eastern sectors of the Cosmonaut Sea, with a distinct concentration at a 100-meter water depth. Different zones exhibited diverse indicators and dominants taxa influenced by local ocean currents. Both the northern Antarctic Peninsula and the western Cosmonaut Sea, where the Weddell Eddy and Antarctic Land Slope Current intersect, showcased marine stramenopiles as dominant HFs species. Our findings offer insights into dominant taxa, spatial distribution patterns among heterotrophic flagellates, correlations between taxa distribution and environmental factors, and the exploration of potential indicator taxa

Riemannian Surface on Carbon Anodes Enables Li-Ion Storage at −35 °C

Since sluggish Li$^{+}$ desolvation leads to severe capacity degradation of carbon anodes at subzero temperatures, it is urgently desired to modulate electron configurations of surface carbon atoms toward high capacity for Li-ion batteries. Herein, a carbon-based anode material (O-DF) was strategically synthesized to construct the Riemannian surface with a positive curvature, which exhibits a high reversible capacity of 624 mAh g$^{-1}$ with an 85.9% capacity retention at 0.1 A g$^{-1}$ as the temperature drops to −20 °C. Even if the temperature drops to −35 °C, the reversible capacity is still effectively retained at 160 mAh g$^{-1}$ after 200 cycles. Various characterizations and theoretical calculations reveal that the Riemannian surface effectively tunes the low-temperature sluggish Li$^{+}$ desolvation of the interfacial chemistry via locally accumulated charges of non-coplanar sp$^{x}$ (2 < x < 3) hybridized orbitals to reduce the rate-determining step of the energy barrier for the charge-transfer process. Ex-situ measurements further confirm that the sp$^{x}$-hybridized orbitals of the pentagonal defect sites should denote more negative charges to solvated Li$^{+}$ adsorbed on the Riemannian surface to form stronger Li–C coordinate bonds for Li$^{+}$ desolvation, which not only enhances Li-adsorption on the curved surface but also results in more Li$^{+}$ insertion in an extremely cold environment

The construction of a nomogram to predict the prognosis and recurrence risks of UPJO

ObjectiveThis study was conducted to explore the risk factors for the prognosis and recurrence of ureteropelvic junction obstruction (UPJO).MethodsThe correlation of these variables with the prognosis and recurrence risks was analyzed by binary and multivariate logistic regression. Besides, a nomogram was constructed based on the multivariate logistic regression calculation. After the model was verified by the C-statistic, the ROC curve was plotted to evaluate the sensitivity of the model. Finally, the decision curve analysis (DCA) was conducted to estimate the clinical benefits and losses of intervention measures under a series of risk thresholds.ResultsPreoperative automated peritoneal dialysis (APD), preoperative urinary tract infection (UTI), preoperative renal parenchymal thickness (RPT), Mayo adhesive probability (MAP) score, and surgeon proficiency were the high-risk factors for the prognosis and recurrence of UPJO. In addition, a nomogram was constructed based on the above 5 variables. The area under the curve (AUC) was 0.8831 after self cross-validation, which validated that the specificity of the model was favorable.ConclusionThe column chart constructed by five factors has good predictive ability for the prognosis and recurrence of UPJO, which may provide more reasonable guidance for the clinical diagnosis and treatment of this disease

Design of Efficient Exciplex Emitters by Decreasing the Energy Gap Between the Local Excited Triplet (3LE) State of the Acceptor and the Charge Transfer (CT) States of the Exciplex

A series of thermally activated delayed fluorescence (TADF) exciplex based on the TX-TerPy were constructed. The electronic coupling between the triplet local excited states (3LE) of the donors and acceptor and the charge transfer states had a great influence on the triplet exciton harvesting and ΦPL. Herein, based on this strategy, three donor molecules TAPC, TCTA, and m-MTDATA were selected. The local triplet excited state (3LE) of the three donors are 2.93, 2.72 and 2.52 eV in pure films. And the 3LE of TX-TerPy is 2.69 eV in polystyrene film. The energy gap between the singlet charge transfer (1CT) states of TAPC:TX-TerPy (7:1), TCTA:TX-TerPy (7:1) and the 3LE of TX-TerPy are 0.30 eV and 0.20 eV. Finally, the ΦPL of TAPC:TX-TerPy (7:1) and TCTA:TX-TerPy (7:1) are 65.2 and 69.6%. When we changed the doping concentration of the exciplex from 15% to 50%, the ratio of the triplet decreased, and ΦPL decreased by half, perhaps due to the increased energy gap between 1CT and 3LE. Therefore, optimizing the 1CT, 3CT, and 3LE facilitated the efficient exciplex TADF molecules