Measurement of Investment activity in China based on Natural language processing technology

The purpose of this study is to propose a new index to measure and reflect China's investment activity in time, and to analyze the changes of China's investment activity in the past five years. This study first uses the NEZHA model for semantic representation, and expand the indicator system based on semantic similarity. Then we calculate China's investment activity index by using the network search data. This study shows that China's investment activity began to decline in 2019, rebounded for a period of time after the outbreak of COVID-19 in 2020, and then continued to maintain a downward trend. Private investment activity has declined significantly, while government investment activity has increased. Among the provinces in Chinese Mainland, the investment activity of economically developed provinces has decreased significantly, while the investment activity of some economically less developed provinces in the north and south is higher. After the outbreak of COVID-19, the investment period became shorter. Our research will provide timely investment information for the government, decision makers and managers, as well as provide other researchers who also pay attention to investment with a perspective other than investment in fixed asset

Disentangled Generative Causal Representation Learning

This paper proposes a Disentangled gEnerative cAusal Representation (DEAR) learning method. Unlike existing disentanglement methods that enforce independence of the latent variables, we consider the general case where the underlying factors of interests can be causally correlated. We show that previous methods with independent priors fail to disentangle causally correlated factors. Motivated by this finding, we propose a new disentangled learning method called DEAR that enables causal controllable generation and causal representation learning. The key ingredient of this new formulation is to use a structural causal model (SCM) as the prior for a bidirectional generative model. The prior is then trained jointly with a generator and an encoder using a suitable GAN loss incorporated with supervision. We provide theoretical justification on the identifiability and asymptotic consistency of the proposed method, which guarantees disentangled causal representation learning under appropriate conditions. We conduct extensive experiments on both synthesized and real data sets to demonstrate the effectiveness of DEAR in causal controllable generation, and the benefits of the learned representations for downstream tasks in terms of sample efficiency and distributional robustness

Sustainability in Supply Chains with Behavioral Concerns

Environmental sustainability has received considerable attention in industry and academia. Many firms have begun to adopt sustainability practices, such as investing in cleaner technology and using organic or recyclable materials, to enhance sustainability in supply chains. Such sustainability practices affect corporate social responsibility and business performance. On the other hand, when consumers and supply chain managers make decisions, they may be constrained by behavioral concerns. Behavioral concerns can significantly influence optimization in supply chains. Thus, it is critical to consider the impacts of behavioral concerns on sustainability in supply chains. In this paper, we concisely examine studies in sustainability issues in supply chains with behavioral concerns and introduce the papers featured in this Special Issue

Propagation of tidal waves up in Yangtze Estuary during the dry season

Tide is one of the most important hydrodynamic driving forces and has unique features in the Yangtze Estuary (YE) due to the complex geometry of third-order bifurcations and four outlets. This paper characterizes the tidal oscillations, tidal dampening, tidal asymmetry, and tidal wave propagation, which provides insights into the response of the estuary to tides during the dry season. The structural components of tidal oscillations are initially attained by tidal analysis. The increasingly richer spectrum inside the estuary shows an energy transfer corresponding to the generation and development of nonlinear overtides and compound tides. A 2-D numerical model is further set up to reproduce tidal dynamics in the estuary. The results show that the estuary is a strongly dissipative estuary with a strong nonlinear phenomenon. Three amplifications are presented in the evolution process of tidal ranges due to the channel convergence. Tidal asymmetry is spatiotemporally characterized by the M-4/M-2 amplitude ratio, the 2M(2)-M-4 phase difference, and the flood-ebb duration-asymmetry parameter, and the estuary tends to be flood-dominant. There exists mimic standing waves with the phase difference of the horizontal and vertical tide close to 90 degrees when tidal wave propagates into the estuary, especially during the neap tide. In addition, the differences in tidal distortion, tidal ranges, and tidal waves along the two routes in the South Branch (S-B) suggest the branched system behaves differently from a single system