Green Governance and International Business Strategies of Emerging Economies’ Multinational Enterprises: A Multiple-Case Study of Chinese Firms in Pollution-Intensive Industries

With the global consensus on the need for sustainability practices, green governance has attracted increasing attention from international business (IB) scholars and multinational enterprise (MNE) managers. In this study, we propose a more fine-grained framework of the green governance context along two dimensions: foreign direct investment (FDI) policy and environmental regulation. Then, we examine the framework using cluster analysis. On the basis of a multiple-case study comprising 11 Chinese MNEs in pollution-intensive industries operating in four different green governance contexts, we conclude that (1) the green governance context is a significant factor in MNEs’ global location choices and is an important driving force behind MNEs’ response patterns; (2) environmental capabilities enable MNEs to surmount a host country’s environmental entry barrier and facilitate wider global business deployment; (3) technological capabilities increase MNEs’ competitive edge and allow them to better harness a host country’s growth opportunities; (4) there are four types of green governance response patterns, and the details of the proposed classification structure and its validation are presented; and (5) both strict environmental regulation and friendly FDI policy can positively influence MNEs’ adoption of more active response patterns, and greater availability of environmental and technological capabilities does not affect MNEs’ environmental commitment. This study contributes to the international strategy-capability-environment alignment of emerging economies’ multinational enterprises (EMNEs) in different green governance contexts

Multi-distribution noise quantisation: an extreme compression scheme for transformer according to parameter distribution

With the development of deep learning, neural networks are widely used in various fields, and the improved model performance also introduces a considerable number of parameters and computations. Model quantisation is a technique that turns floating-point computing into low-specific-point computing, which can effectively reduce model computation strength, parameter size, and memory consumption but often bring a considerable loss of accuracy. This paper mainly addresses the problem where the distribution of parameters is too concentrated during quantisation aware training (QAT). In the QAT process, we use a piecewise function to statistics the parameter distributions and simulate the effect of quantisation noise in each round of training, based on the statistical results. Experimental results show that by quantising the Transformer network, we lose less precision and significantly reduce the storage cost of the model; compared with the full precision LSTM network, our model has higher accuracy under the condition of a similar storage cost. Meanwhile, compared with other quantisation methods on language modelling task, our approach is more accurate. We validated the effectiveness of our policy on the WikiText-103 and PENN Treebank datasets. The experiments show that our method extremely compresses the storage cost and maintains high model performance

Sustained and Targeted Delivery of Self-Assembled Doxorubicin Nonapeptides Using pH-Responsive Hydrogels for Osteosarcoma Chemotherapy

While chemotherapeutic agents have particularly potent effects in many types of cancer, their clinical applications are still far from satisfactory due to off-target drug exposure, chemotherapy resistance, and adverse effects, especially in osteosarcoma. Therefore, it is clinically promising to construct a novel tumor-targeted drug delivery system to control drug release and alleviate side effects. In this study, a pH-responsive nonapeptide hydrogel was designed and fabricated for the tumor-targeted drug delivery of doxorubicin (DOX). Using a solid-phase synthesis method, a nonapeptide named P1 peptide that is structurally akin to surfactant-like peptides (SLPs) due to its hydrophobic tail and hydrophilic head was synthesized. The physicochemical properties of the P1 hydrogel were characterized via encapsulation capacity, transmission electron microscopy (TEM), circular dichroism (CD), zeta potential, rheological analysis, and drug release studies. We also used in vitro and in vivo experiments to investigate the cytocompatibility and tumor inhibitory efficacy of the drug-loaded peptide hydrogel. The P1 peptide could self-assemble into biodegradable hydrogels under neutral conditions, and the prepared drug-loaded hydrogels exhibited good injectability and biocompatibility. The in vitro drug release studies showed that DOX-P1 hydrogels had high sensitivity to acidic conditions (pH 5.8 versus 7.4, up to 3.6-fold). Furthermore, the in vivo experiments demonstrated that the DOX-P1 hydrogel could not only amplify the therapeutic effect but also increase DOX accumulation at the tumor site. Our study proposes a promising approach to designing a pH-responsive hydrogel with controlled doxorubicin-release action based on self-assembled nonapeptides for targeted chemotherapy

Evolutionary origins of a bioactive peptide buried within Preproalbumin

The de novo evolution of proteins is now considered a frequented route for biological innovation, but the genetic and biochemical processes that lead to each newly created protein are often poorly documented. The common sunflower (Helianthus annuus) contains the unusual gene PawS1 (Preproalbumin with SFTI-1) that encodes a precursor for seed storage albumin; however, in a region usually discarded during albumin maturation, its sequence is matured into SFTI-1, a protease-inhibiting cyclic peptide with a motif homologous to unrelated inhibitors from legumes, cereals, and frogs. To understand how PawS1 acquired this additional peptide with novel biochemical functionality, we cloned PawS1 genes and showed that this dual destiny is over 18 million years old. This new family of mostly backbone-cyclic peptides is structurally diverse, but the protease-inhibitory motif was restricted to peptides from sunflower and close relatives from its subtribe. We describe a widely distributed, potential evolutionary intermediate PawS-Like1 (PawL1), which is matured into storage albumin, but makes no stable peptide despite possessing residues essential for processing and cyclization from within PawS1. Using sequences we cloned, we retrodict the likely stepwise creation of PawS1's additional destiny within a simple albumin precursor. We propose that relaxed selection enabled SFTI-1 to evolve its inhibitor function by converging upon a successful sequence and structure

Data from: Evolutionary origins of a bioactive peptide buried within preproalbumin

The de novo evolution of proteins is now considered a frequented route for biological innovation, but the genetic and biochemical processes that lead to each newly created protein are often poorly documented. The common sunflower (Helianthus annuus) contains the unusual gene PawS1 (Preproalbumin with SFTI-1) that encodes a precursor for seed storage albumin; however, in a region usually discarded during albumin maturation, its sequence is matured into SFTI-1, a protease-inhibiting cyclic peptide with a motif homologous to unrelated inhibitors from legumes, cereals, and frogs. To understand how PawS1 acquired this additional peptide with novel biochemical functionality, we cloned PawS1 genes and showed that this dual destiny is over 18 million years old. This new family of mostly backbone-cyclic peptides is structurally diverse, but the protease-inhibitory motif was restricted to peptides from sunflower and close relatives from its subtribe. We describe a widely distributed, potential evolutionary intermediate PawS-Like1 (PawL1), which is matured into storage albumin, but makes no stable peptide despite possessing residues essential for processing and cyclization from within PawS1. Using sequences we cloned, we retrodict the likely stepwise creation of PawS1’s additional destiny within a simple albumin precursor. We propose that relaxed selection enabled SFTI-1 to evolve its inhibitor function by converging upon a successful sequence and structure

Evolutionary origins of a bioactive peptide buried within preproalbumin

The de novo evolution of proteins is now considered a frequented route for biological innovation, but the genetic and biochemical processes that lead to each newly created protein are often poorly documented. The common sunflower (Helianthus annuus) contains the unusual gene PawS1 (Preproalbumin with SFTI-1) that encodes a precursor for seed storage albumin; however, in a region usually discarded during albumin maturation, its sequence is matured into SFTI-1, a protease-inhibiting cyclic peptide with a motif homologous to unrelated inhibitors from legumes, cereals, and frogs. To understand how PawS1 acquired this additional peptide with novel biochemical functionality, we cloned PawS1 genes and showed that this dual destiny is over 18 million years old. This new family of mostly backbone-cyclic peptides is structurally diverse, but the protease-inhibitory motif was restricted to peptides from sunflower and close relatives from its subtribe. We describe a widely distributed, potential evolutionary intermediate PawS-Like1 (PawL1), which is matured into storage albumin, but makes no stable peptide despite possessing residues essential for processing and cyclization from within PawS1. Using sequences we cloned, we retrodict the likely stepwise creation of PawS1's additional destiny within a simple albumin precursor. We propose that relaxed selection enabled SFTI-1 to evolve its inhibitor function by converging upon a successful sequence and structure

Iterative integration of deep learning in hybrid Earth surface system modelling

Earth system modelling (ESM) is essential for understanding past, present and future Earth processes. Deep learning (DL), with the data-driven strength of neural networks, has promise for improving ESM by exploiting information from Big Data. Yet existing hybrid ESMs largely have deep neural networks incorporated only during the initial stage of model development. In this Perspective, we examine progress in hybrid ESM, focusing on the Earth surface system, and propose a framework that integrates neural networks into ESM throughout the modelling lifecycle. In this framework, DL computing systems and ESM-related knowledge repositories are set up in a homogeneous computational environment. DL can infer unknown or missing information, feeding it back into the knowledge repositories, while the ESM-related knowledge can constrain inference results of the DL. By fostering collaboration between ESM-related knowledge and DL systems, adaptive guidance plans can be generated through question-answering mechanisms and recommendation functions. As users interact iteratively, the hybrid system deepens its understanding of their preferences, resulting in increasingly customized, scalable and accurate guidance plans for modelling Earth processes. The advancement of this framework necessitates interdisciplinary collaboration, focusing on explainable DL and maintaining observational data to ensure the reliability of simulations