SHAFTS (v2022.3): a deep-learning-based Python package for simultaneous extraction of building height and footprint from sentinel imagery

Abstract. Building height and footprint are two fundamental urban morphological features required by urban climate modelling. Although some statistical methods have been proposed to estimate average building height and footprint from publicly available satellite imagery, they often involve tedious feature engineering which makes it hard to achieve efficient knowledge discovery in a changing urban environment with ever-increasing earth observations. In this work, we develop a deep-learning-based (DL) Python package – SHAFTS (Simultaneous building Height And FootprinT extraction from Sentinel imagery) to extract such information. Multi-task deep-learning (MTDL) models are proposed to automatically learn feature representation shared by building height and footprint prediction. Besides, we integrate digital elevation model (DEM) information into developed models to inform models of terrain-induced effects on the backscattering displayed by Sentinel-1 imagery. We set conventional machine-learning-based (ML) models and single-task deep-learning (STDL) models as benchmarks and select 46 cities worldwide to evaluate developed models’ patch-level prediction skills and city-level spatial transferability at four resolutions (100, 250, 500 and 1000 m). Patch-level results of 43 cities show that DL models successfully produce discriminative feature representation and improve the coefficient of determination (R2) of building height and footprint prediction more than ML models by 0.27–0.63 and 0.11–0.49, respectively. Moreover, stratified error assessment reveals that DL models effectively mitigate the severe systematic underestimation of ML models in the high-value domain: for the 100 m case, DL models reduce the root mean square error (RMSE) of building height higher than 40 m and building footprint larger than 0.25 by 31 m and 0.1, respectively, which demonstrates the superiority of DL models on refined 3D building information extraction in highly urbanized areas. For the evaluation of spatial transferability, when compared with an existing state-of-the-art product, DL models can achieve similar improvement on the overall performance and high-value prediction. Furthermore, within the DL family, comparison in building height prediction between STDL and MTDL models reveals that MTDL models achieve higher accuracy in all cases and smaller bias uncertainty for the prediction in the high-value domain at the refined scale, which proves the effectiveness of multi-task learning (MTL) on building height estimation

Improvement of the Response Speed for Switched Reluctance Generation System Based on Modified PT Control

The Switched Reluctance Generator (SRG) is suitable for wind power generation due to its good reliability and robustness. However, The SRG system adopting the conventional control algorithm with Pulse Width Modulation (PWM) method has a drawback, low response speed. The pulse train (PT) control has been widely used in dc/dc power converters operating in the discontinuous conduction mode due to its advantages of simple implementation and fast response. In this paper, for the first time, the PT control method is modified and adopted for controlling the output voltage of SRG system in order to achieve fast response. The capacitor current on the output side is sampled and combined with the output voltage to select the pulse trains and the low frequency oscillation cased by PT can be suppressed by tuning the feedback coefficient of the capacitor current. Also, good performance can be guaranteed with a wide range of voltage regulations, fast response, and no overshoot. The experimental platform of an 8/6 SRG system is built, and the experimental results show that the PT control can be used for SRG system with good practicability

Assessment of Hydrodynamic Performance and Motion Suppression of Tension Leg Floating Platform Based on Tuned Liquid Multi-Column Damper

To address the unstable motion of a tension leg platform (TLP) for floating wind turbines in various sea conditions, an improved method of incorporating a tuned liquid multi-column damper (TLMCD) into the TLP foundation is proposed. In order to evaluate the control effect of TLMCD on the motion response of the floating foundation, a multiphase flow solver based on a viscous flow CFD method and overlapping grid technique is applied to model the coupled multi-body dynamics interaction problem involving liquid tanks, waves, and a spring mooring system. This method has been proven to accurately capture the high-frequency motions of the structure and account for complex viscous interferences affecting the geometric motions. Additionally, the volume of fluid (VOF) method and the first-order linear superposition method are used to model the focused wave, enabling a simulation of the effects of transient wave loads on the floating foundation. The results show that the tuned damping effect of TLMCD on the TLP is mainly in the pitch motion, with the maximum pitch amplitude control volume ratio of TLMCD reaching up to 86% and the maximum surge amplitude control volume ratio of TLMCD reaching up to 25.2% under the operating conditions. These findings highlight the potential for additional research on and implementation of TLMCD technology

Improving LSTM hydrological modeling with spatiotemporal deep learning and multi-task learning: a case study of three mountainous areas on the Tibetan Plateau

Long short-term memory (LSTM) networks have demonstrated their excellent capability in processing long-length temporal dynamics and have proven to be effective in precipitation-runoff modeling. However, the current LSTM hydrological models lack the incorporation of multi-task learning and spatial information, which limits their ability to make full use of meteorological and hydrological data. To address this issue, this study proposes a spatiotemporal deep-learning (DL)-based hydrological model that couples the 2-Dimension convolutional neural network (CNN) and LSTM and introduces actual evaporation ( ) as an additional training target. The proposed CNN-LSTM model is tested on three large mountainous basins on the Tibetan Plateau, and the results are compared to those obtained from the LSTM-only model. Additionally, a probe method is used to decipher the internal embedding layers of the proposed DL models. The results indicate that both LSTM and CNN-LSTM hydrological models perform well in simulating runoff ( ) and , with Nash-Sutcliffe efficiency coefficients ( ) higher than 0.82 and 0.95, respectively. The higher suggest that introducing spatial information into LSTM-only models can improve the overall and peak model performance. Moreover, multi-task simulation with LSTM-only models shows better accuracy in the estimation of volume and performance, with increasing by approximately 0.02. The probe method also reveals that CNN can capture the basin-averaged meteorological values in CNN-LSTM models, while LSTM ( ) models contain the information about the known ( ) process. Overall, this study demonstrates the value of spatial information and multi-task learning in LSTM hydrological modeling and provides a perspective for interpreting the internal embedding layers of DL models

Assembly of Metallacages into Soft Suprastructures with Dimensions of up to Micrometers and the Formation of Composite Materials

This work provides a platform for the rapid generation of superstructure assemblies with a wide range of lengths that can be used to access a variety of metal–organic complex-based soft superstructures. Metallacage-based microneedles that are nanometers in diameter and millimeters in length were generated in dichloromethane and ethyl acetate; their size could be controlled by adjusting the ratio of the two solvents. Interestingly, microflower structures could be formed by further assembly of the microneedles during solvent evaporation. Our study establishes a feasible method designed to broaden the range of suprastructures with emissions from blue and green to red through the co-assembly of lysine-modified perylene. Similar to the co-assembly of lysine-modified perylene with microflowers, chlorophyll-a and vitamin B12 were introduced into the microflowers during the assembly process, which may be exploited in studies of energy capture and nerve repair in the future

Synthesis, Quantification, and Characterization of Fatty Acid Amides from In Vitro and In Vivo Sources

Fatty acid amides are a diverse family of underappreciated, biologically occurring lipids. Herein, the methods for the chemical synthesis and subsequent characterization of specific members of the fatty acid amide family are described. The synthetically prepared fatty acid amides and those obtained commercially are used as standards for the characterization and quantification of the fatty acid amides produced by biological systems, a fatty acid amidome. The fatty acid amidomes from mouse N18TG2 cells, sheep choroid plexus cells, Drosophila melanogaster, Bombyx mori, Apis mellifera, and Tribolium castaneum are presented

Development of a Three-Dimensional Multi-Modal Perfusion-Thermal Electrode System for Complete Tumor Eradication

Background: Residual viable tumor cells after ablation at the tumor periphery serve as the source for tumor recurrence, leading to treatment failure. Purpose: To develop a novel three-dimensional (3D) multi-modal perfusion-thermal electrode system completely eradicating medium-to-large malignancies. Materials and Methods: This study included five steps: (i) design of the new system; (ii) production of the new system; (iii) ex vivo evaluation of its perfusion-thermal functions; (iv) mathematic modeling and computer simulation to confirm the optimal temperature profiles during the thermal ablation process, and; (v) in vivo technical validation using five living rabbits with orthotopic liver tumors. Results: In ex vivo experiments, gross pathology and optical imaging demonstrated the successful spherical distribution/deposition of motexafin gadolinium administered through the new electrode, with a temperature gradient from the electrode core at 80 °C to its periphery at 42 °C. An excellent repeatable correlation of temperature profiles at varying spots, from the center to periphery of the liver tumor, was found between the mathematic simulation and actual animal tumor models (Pearson coefficient ≥0.977). For in vivo validation, indocyanine green (ICG) was directly delivered into the peritumoral zones during simultaneous generation of central tumoral lethal radiofrequency (RF) heat (>60 °C) and peritumoral sublethal RF hyperthermia (<60 °C). Both optical imaging and fluorescent microscopy confirmed successful peritumoral ICG distribution/deposition with increased heat shock protein 70 expression. Conclusion: This new 3D, perfusion-thermal electrode system provided the evidence on the potential to enable simultaneous delivery of therapeutic agents and RF hyperthermia into the difficult-to-treat peritumoral zones, creating a new strategy to address the critical limitation, i.e., the high incidence of residual and recurrent tumor following thermal ablation of unresectable medium-to-large and irregular tumors