Radar-based solutions have attracted great attention in human activity recognition (HAR) for their advantages in accuracy, robustness, and privacy protection. The conventional approaches transform radar signals into feature maps and then directly process them as visual images. While effective, these image-based methods may not be the best solutions in terms of representation efficiency to encode the relevant information for classification. This article proposes a novel HAR method combining sparse theory and PointNet network, with both operations in the time-Doppler (TD) and range-Doppler (RD) domains. First, sparsity-based feature extraction is introduced to use a limited number of sparse solutions to characterize human activities in the form of TD sparse point clouds (TDSP) or dynamic RD sparse point clouds (DRDSP). This new representation is validated by comparing the reconstructed and original signals. Then, PointNet networks are adopted to summarize multidomain features and predict human activity labels by a sparse set of input point clouds. Comprehensive experiments were conducted to demonstrate that the proposed method can yield a higher representation efficiency, classification accuracy, and better generalization capability than existing ones.

Understanding the relationship between structure, ionic conductivity, and synthesis is the key to the development of superionic conductors. Here, a series of Li_3-3xM_1+xCl₆ (−0.14 < x ≤ 0.5, M = Tb, Dy, Ho, Y, Er, Tm) solid electrolytes with orthorhombic and trigonal structures are reported. The orthorhombic phase of Li–M–Cl shows an approximately one order of magnitude increase in ionic conductivities when compared to their trigonal phase. Using the Li–Ho–Cl components as an example, their structures, phase transition, ionic conductivity, and electrochemical stability are studied. Molecular dynamics simulations reveal the facile diffusion in the z-direction in the orthorhombic structure, rationalizing the improved ionic conductivities. All-solid-state batteries of NMC811/Li_2.73Ho_1.09Cl₆/In demonstrate excellent electrochemical performance at both 25 and −10 °C. As relevant to the vast number of isostructural halide electrolytes, the present structure control strategy guides the design of halide superionic conductors.

The Belt and Road initiative has a significant focus on infrastructure, trade, and economic development across a vast region, and it also provides significant opportunities for sustainable development. The combined pressure of climate variability, intensified use of resources, and the fragility of ecosystems make it very challenging, however, to achieve future sustainability. To develop the path in a sustainable way, it is important to have a comprehensive understanding of these issues across nations and evaluate them in a scientific and well-informed approach. In this context, the Digital Belt and Road (DBAR) program was initiated as an international venture to share expertise, knowledge, technologies, and data to demonstrate the role of Earth observation science and technology and big Earth data applications to support large-scale development. In this paper, we identify pressing challenges, present the research priorities and foci of the DBAR program, and propose solutions where big Earth data can make significant contributions. This paper calls for further joint actions and collaboration to build a digital silk road in support of sustainable development at national, regional and global levels.