Energy-Water Balance and Ecosystem Response to Climate Change in Southwest China

It is important to highlight energy-water balance and ecosystem response to climate changes. The change of water-energy balance and ecosystem due to climate change will affect the regional ecological and human living significantly, especially in Southwest China which is an ecologically fragile area. This chapter presents the retrieval methodology of parameters (reconstruction of vegetation index, land cover semi-automatic classification, a time series reconstruction of land surface temperature based on Kalman filter and precipitation interpolation based on thin plate smoothing splines), time-series analysis methodology (land cover change, vegetation succession and drought index) and correlate analysis methodology (correlation coefficient and principal component analysis). Then, based on the above method, remote sensing data were integrated, a time series analysis on a 30-year data was used to illustrate the water-energy balance and ecosystem variability in Southwest China. The result showed that energy-water balance and ecosystem (ecosystem structures, vegetation and droughts) have severe response to climate change

Enhanced thermopower in an intergrowth cobalt oxide Li0.48_{0.48}Na0.35_{0.35}CoO2_{2}

We report the measurements of thermopower, electrical resistivity and thermal conductivity in a complex cobalt oxide Li$_{0.48}$Na$_{0.35}$CoO$_{2}$, whose crystal structure can be viewed as an intergrowth of the O3 phase of Li$_{x}$CoO$_{2}$ and the P2 phase of Na$_{y}$CoO$_{2}$ along the c axis. The compound shows large room-temperature thermopower of $\sim$180 $\mu$V/K, which is substantially higher than those of Li$_{x}$CoO$_{2}$ and Na$_{y}$CoO$_{2}$. The figure of merit for the polycrystalline sample increases rapidly with increasing temperature, and it achieves nearly 10$^{-4}$ K$^{-1}$ at 300 K, suggesting that Li$_{x}$Na$_{y}$CoO$_{2}$ system is a promising candidate for thermoelectric applications.Comment: Submitted to AP

6G Non-Terrestrial Networks Enabled Low-Altitude Economy: Opportunities and Challenges

The unprecedented development of non-terrestrial networks (NTN) utilizes the low-altitude airspace for commercial and social flying activities. The integration of NTN and terres- trial networks leads to the emergence of low-altitude economy (LAE). A series of LAE application scenarios are enabled by the sensing, communication, and transportation functionalities of the aircrafts. The prerequisite technologies supporting LAE are introduced in this paper, including the network coverage and aircrafts detection. The LAE functionalities assisted by aircrafts with respect to sensing and communication are then summarized, including the terrestrial and non-terrestrial targets sensing, ubiquitous coverage, relaying, and traffic offloading. Finally, several future directions are identified, including aircrafts collaboration, energy efficiency, and artificial intelligence enabled LAE.Comment: This paper has been submitted to IEEE for possible publicatio

Integrated Sensing-Communication-Computation for Edge Artificial Intelligence

Edge artificial intelligence (AI) has been a promising solution towards 6G to empower a series of advanced techniques such as digital twin, holographic projection, semantic communications, and auto-driving, for achieving intelligence of everything. The performance of edge AI tasks, including edge learning and edge AI inference, depends on the quality of three highly coupled processes, i.e., sensing for data acquisition, computation for information extraction, and communication for information transmission. However, these three modules need to compete for network resources for enhancing their own quality-of-services. To this end, integrated sensing-communication-computation (ISCC) is of paramount significance for improving resource utilization as well as achieving the customized goals of edge AI tasks. By investigating the interplay among the three modules, this article presents various kinds of ISCC schemes for federated edge learning tasks and edge AI inference tasks in both application and physical layers

Numerical analysis of yield properties of closed-cell aluminum foam under multiaxial loads by 3D voronoi model

Metallic foam is a typical porous material whose yield surface is related to not only von Mises equivalent stress but also the hydrostatic pressure. It is essential to study the yield properties of closed-cell aluminum foam under complex loading conditions. However, because the current experimental technique may support only a few proportions of multiaxial loading, it is hard to learn the yield surface well especially for the tensile hydrostatic pressure. In this article, we explored a numerical method to learn the yield properties of aluminum foam, in which the meso structures of aluminum foam were simulated by 3D Voronoi method. In addition, the yield surface of aluminum foam was drawn successfully with the numerical method. The main works included: (1) In our numerical simulation, we tested the calculating parameters such as mass scaling, elements number, and loading velocity on simulation results and verified the homogeneity of the 3D Voronoi model firstly. Furthermore, the optimized calculating parameters were determined by considering both reliability and feasibility of the calculation. Homogeneity of 3D Voronoi model was checked by the compression behaviors of aluminum in different directions. (2) In order to overcome the difficulty to determine critical yield state of metallic foams under complex loads, we recommended criterion by setting a dimensionless plastic dissipation value to determine the onset yield state of the foam under multiaxial loads. The critical value of dimensionless plastic dissipation was deduced from the common criterion—0.2% plastic strain in uniaxial loading, and the effect of relative densities on critical values was analyzed. (3) Three normal stresses were applied on cubic aluminum foam proportionally to implement the proportional loading. The different loading proportional factors of the three normal stresses were set widely to insure the yield surface including enough data, such as the hydrostatic loads cover from minimum negative to maximum positive values; each proportion has three loading proportional factors. Further, effects of the relative density on yield surface were investigated

Prompt, Plan, Perform: LLM-based Humanoid Control via Quantized Imitation Learning

In recent years, reinforcement learning and imitation learning have shown great potential for controlling humanoid robots' motion. However, these methods typically create simulation environments and rewards for specific tasks, resulting in the requirements of multiple policies and limited capabilities for tackling complex and unknown tasks. To overcome these issues, we present a novel approach that combines adversarial imitation learning with large language models (LLMs). This innovative method enables the agent to learn reusable skills with a single policy and solve zero-shot tasks under the guidance of LLMs. In particular, we utilize the LLM as a strategic planner for applying previously learned skills to novel tasks through the comprehension of task-specific prompts. This empowers the robot to perform the specified actions in a sequence. To improve our model, we incorporate codebook-based vector quantization, allowing the agent to generate suitable actions in response to unseen textual commands from LLMs. Furthermore, we design general reward functions that consider the distinct motion features of humanoid robots, ensuring the agent imitates the motion data while maintaining goal orientation without additional guiding direction approaches or policies. To the best of our knowledge, this is the first framework that controls humanoid robots using a single learning policy network and LLM as a planner. Extensive experiments demonstrate that our method exhibits efficient and adaptive ability in complicated motion tasks

An Analytical Model for Fatigue Crack Propagation Prediction with Overload Effect

In this paper a theoretical model was developed to predict the fatigue crack growth behavior under the constant amplitude loading with single overload. In the proposed model, crack growth retardation was accounted for by using crack closure and plastic zone. The virtual crack annealing model modified by Bauschinger effect was used to calculate the crack closure level in the outside of retardation effect region. And the Dugdale plastic zone model was employed to estimate the size of retardation effect region. A sophisticated equation was developed to calculate the crack closure variation during the retardation area. Model validation was performed in D16 aluminum alloy and 350WT steel specimens subjected to constant amplitude load with single or multiple overloads. The predictions of the proposed model were contrasted with experimental data, and fairly good agreements were observed