2,348 research outputs found
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
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