The impact of digital transformation on the total factor productivity of heavily polluting enterprises.

In the digital economy, the relationship between digital transformation and a company's total factor productivity has profound implications for high-quality business development. Heavy polluters are given more environmental responsibility because of their high pollution and emission characteristics. This paper analyses the theoretical framework for the impact of digital transformation on the total factor productivity of heavily polluting firms. Using a sample of Shanghai and Shenzhen A-share heavy polluters from 2010 to 2020, we explore how the digital transformation of heavy polluters affects the total factor productivity of firms. The study found that the digital transformation of heavily polluting companies can effectively improve total factor productivity, internally by increasing their level of green technology innovation and externally by increasing their willingness and capacity for corporate social responsibility. At the same time, digital transformation can improve total factor productivity by reducing cost stickiness, revealing the "black box" in which digital transformation affects the total factor productivity of an enterprise. It was further found that the digital transformation of companies with high levels of environmental investment, large enterprises, those in non-manufacturing industries, and heavy polluters of a state-owned nature had a more significant impact on total factor productivity. The findings of the study provide empirical evidence for the digital transformation of heavily polluting companies to improve productivity and the green transformation of the economy for companies under the low carbon goal.

Spatial differences of foreign capital R&D networks in China: quantitative analysis based on dissipative structure theory.

China's foreign capital R&D network has become a core component of the high-quality development of the national innovation ecosystem. Narrowing the spatial difference in the foreign R&D network is considered an essential strategy to improve China's holistic innovation capability. Previous studies only examined the internal factors of the system while mostly ignoring the quantitative analysis of the spatial differences diffusion. Based on the dissipative structure theory, the current paper demonstrates the construction of positive and negative entropy indexes of foreign capital R&D networks in China. Rested on a survey dataset of 340 foreign R&D institutions in 21 Chinese provinces from 2016 to 2017, this paper analyzes the spatial differences and driving factors of the innovation development of foreign capital R&D networks across the country using the Brusselator model. The results show that China's entire deviation degree of foreign capital R&D networks is small. Still, there are significant spatial differences in the deviation between the entropy value and the ideal status of dissipative structure in foreign R&D networks, with the minor deviation-value areas located in the eastern regions. In addition, the central and western regions where the late-mover layout of the foreign R&D network deviates are far from the ideal status. Among them, the western (i.e., Xi'an and Chengdu) foreign R&D network is significantly better than the central innovation performance. It is found that policy traction of talent advantage and intellectual property rights protection positively impact the negative entropy flow of foreign R&D networks in China, which becomes an influential driving force for the future system equilibrium. The paper concludes with several policy implications to guide China's innovation and ecological optimization of foreign capital R&D networks.

Novel Low-Power Construction of Chaotic S-Box in Multilayer Perceptron.

Multilayer perceptron is composed of massive distributed neural processors interconnected. The nonlinear dynamic components in these processors expand the input data into a linear combination of synapses. However, the nonlinear mapping ability of original multilayer perceptron is limited when processing high complexity information. The introduction of more powerful nonlinear components (e.g., S-box) to multilayer perceptron can not only reinforce its information processing ability, but also enhance the overall security. Therefore, we combine the methods of cryptography and information theory to design a low-power chaotic S-box (LPC S-box) with entropy coding in the hidden layer to make the multilayer perceptron process information more efficiently and safely. In the performance test, our S-box architecture has good properties, which can effectively resist main known attacks (e.g., Berlekamp Massey-attack and Ronjom-Helleseth attack). This interdisciplinary work can attract more attention from academia and industry to the security of multilayer perceptron.

High-Efficacy and Polymeric Solid-Electrolyte Interphase for Closely Packed Li Electrodeposition.

The industrial application of lithium metal anode requires less side reaction between active lithium and electrolyte, which demands the sustainability of the electrolyte-induced solid-electrolyte interface. Here, through a new diluted lithium difluoro(oxalato)borate-based (LiDFOB) high concentration electrolyte system, it is found that the oxidation behavior of aggregated LiDFOB salt has a great impact on solid-electrolyte interphase (SEI) formation and Li reversibility. Under the operation window of Cu/LiNi0.8Co0.1Mn0.1O2 full cells (rather than Li/Cu configuration), a polyether/coordinated borate containing solid-electrolyte interphase with inner Li2O crystalline can be observed with the increasing concentration of salt, which can be ascribed to the reaction between aggregated electron-deficient borate species and electron-rich alkoxide SEI components. The high Li reversibility (99.34%) and near-theoretical lithium deposition enable the stable cycling of LiNi0.8Co0.1Mn0.1O2/Li cells (N/P < 2, 350 Wh kg-1) under high cutoff voltage condition of 4.6 V and lean electrolyte condition (E/C ≈ 3.2 g Ah-1).

Profiling the Structural Determinants of Aryl Benzamide Derivatives as Negative Allosteric Modulators of mGluR5 by In Silico Study.

Glutamate plays a crucial role in the treatment of depression by interacting with the metabotropic glutamate receptor subtype 5 (mGluR5), whose negative allosteric modulators (NAMs) are thus promising antidepressants. At present, to explore the structural features of 106 newly synthesized aryl benzamide series molecules as mGluR5 NAMs, a set of ligand-based three-dimensional quantitative structure-activity relationship (3D-QSAR) analyses were firstly carried out applying comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods. In addition, receptor-based analysis, namely molecular docking and molecular dynamics (MD) simulations, were performed to further elucidate the binding modes of mGluR5 NAMs. As a result, the optimal CoMSIA model obtained shows that cross-validated correlation coefficient Q2 = 0.70, non-cross-validated correlation coefficient R2ncv = 0.89, predicted correlation coefficient R2pre = 0.87. Moreover, we found that aryl benzamide series molecules bind as mGluR5 NAMs at Site 1, which consists of amino acids Pro655, Tyr659, Ile625, Ile651, Ile944, Ser658, Ser654, Ser969, Ser965, Ala970, Ala973, Trp945, Phe948, Pro903, Asn907, Val966, Leu904, and Met962. This site is the same as that of other types of NAMs; mGluR5 NAMs are stabilized in the "linear" and "arc" configurations mainly through the H-bonds interactions, π-π stacking interaction with Trp945, and hydrophobic contacts. We hope that the models and information obtained will help understand the interaction mechanism of NAMs and design and optimize NAMs as new types of antidepressants.