FDI and labour share of home-country: empirical evidence from micro data of Chinese enterprises

When an enterprise is operating globally, it will surely lead to the flow of production factors, and thus change the factor income distribution in the home country. This paper studies the influence of FDI on the labour share in enterprises’home country under the background of continuous economic globalisation. Based on the theory of Heterogeneity of FDI Motivation, this paper first analyzes the influence mechanism of FDI on home country labour share. Then, with the micro data of Chinese enterprises, this paper adopts Mahalanobis distance matching and Difference-in- Differences (DID) estimation to have empirical test on the influence of FDI on the labour share in enterprises’home country. The results show that, overall, enterprises’ FDI and labour share in the home country present a negative correlation. In terms of hetero- geneity of FDI motivation, market-seeking FDI significantly decreases the labour share in the home country, while resources- seeking and technology-seeking FDI significantly increase the labour share in the home country. From the perspective of host country heterogeneity, FDI in developed countries significantly increases the labour share in the home country, while the FDI in developing countries inhibits the increase of labour share in the home countr

When Distributed Consensus Meets Wireless Connected Autonomous Systems: A Review and A DAG-based Approach

The connected and autonomous systems (CAS) and auto-driving era is coming into our life. To support CAS applications such as AI-driven decision-making and blockchain-based smart data management platform, data and message exchange/dissemination is a fundamental element. The distributed message broadcast and forward protocols in CAS, such as vehicular ad hoc networks (VANET), can suffer from significant message loss and uncertain transmission delay, and faulty nodes might disseminate fake messages to confuse the network. Therefore, the consensus mechanism is essential in CAS with distributed structure to guaranteed correct nodes agree on the same parameter and reach consistency. However, due to the wireless nature of CAS, traditional consensus cannot be directly deployed. This article reviews several existing consensus mechanisms, including average/maximum/minimum estimation consensus mechanisms that apply on quantity, Byzantine fault tolerance consensus for request, state machine replication (SMR) and blockchain, as well as their implementations in CAS. To deploy wireless-adapted consensus, we propose a Directed Acyclic Graph (DAG)-based message structure to build a non-equivocation data dissemination protocol for CAS, which has resilience against message loss and unpredictable forwarding latency. Finally, we enhance this protocol by developing a two-dimension DAG-based strategy to achieve partial order for blockchain and total order for the distributed service model SMR

GUPNet++: Geometry Uncertainty Propagation Network for Monocular 3D Object Detection

Geometry plays a significant role in monocular 3D object detection. It can be used to estimate object depth by using the perspective projection between object's physical size and 2D projection in the image plane, which can introduce mathematical priors into deep models. However, this projection process also introduces error amplification, where the error of the estimated height is amplified and reflected into the projected depth. It leads to unreliable depth inferences and also impairs training stability. To tackle this problem, we propose a novel Geometry Uncertainty Propagation Network (GUPNet++) by modeling geometry projection in a probabilistic manner. This ensures depth predictions are well-bounded and associated with a reasonable uncertainty. The significance of introducing such geometric uncertainty is two-fold: (1). It models the uncertainty propagation relationship of the geometry projection during training, improving the stability and efficiency of the end-to-end model learning. (2). It can be derived to a highly reliable confidence to indicate the quality of the 3D detection result, enabling more reliable detection inference. Experiments show that the proposed approach not only obtains (state-of-the-art) SOTA performance in image-based monocular 3D detection but also demonstrates superiority in efficacy with a simplified framework.Comment: 18 pages, 9 figure

Effects of temporal and spatial scales on soil yeast communities in the peach orchard

Shihezi Reclamation Area is located at the southern edge of the Junggar Basin, with natural, soil, and climatic conditions unique to the production of peaches. In turn, peach orchards have accumulated rich microbial resources. As an important taxon of soil fungi, the diversity and community structure changes of yeast in the soil of peach orchards on spatial and temporal scales are still unknown. Here, we aimed to investigate the changes in yeast diversity and community structure in non-rhizosphere and rhizosphere soils of peach trees of different ages in the peach orchard and the factors affecting them, as well as the changes in the yeast co-occurrence network in the peach orchard at spatial and temporal scales. High-through put sequencing results showed that a total of 114 yeast genera were detected in all soil samples, belonging to Ascomycota (60 genera) and Basidiomycota (54 genera). The most dominant genus, Cryptococcus, was present in greater than 10% abundance in each sample. Overall, the differences in yeast diversity between non-rhizosphere and rhizosphere soil of peach trees at 3, 8 and 15 years were not significant. Principal coordinate analysis (PCoA) showed that differences in yeast community structure were more pronounced at the temporal scale compared to the spatial scale. The results of soil physical and chemical analysis showed that the 15-year-old peach rhizosphere soil had the lowest pH, while the OM, TN, and TP contents increased significantly. Redundancy analysis showed that soil pH and CO were key factors contributing to changes in soil yeast community structure in the peach orchard at both spatial and temporal scales. The results of co-occurrence network analysis showed that the peach orchard soil yeast network showed synergistic effects as a whole, and the degree of interactions and connection tightness of the 15-year-old peach orchard soil yeast network were significantly higher than the 3- and 8-year-old ones on the time scale. The results reveal the distribution pattern and mechanism of action of yeast communities in peach orchard soils, which can help to develop effective soil management strategies and improve the stability of soil microecology, thus promoting crop growth

Angelica Dahurica Regulated the Polarization of Macrophages and Accelerated Wound Healing in Diabetes: A Network Pharmacology Study and In Vivo Experimental Validation

Diabetic wounds exhibit retarded and partial healing processes. Therefore, patients are exposed to an elevated risk of infection. It has been verified that Angelica dahurica (Hoffm.) Benth. and Hook. f. ex Franch. and Sav (A. dahurica) is conducive for wound healing. However, the pharmacological mechanisms of A. dahurica are yet to be established. The present study uses network pharmacology and in vivo experimental validation to investigate the underlying process that makes A. dahurica conducive for faster wound healing in diabetes patients. 54 potential targets in A. dahurica that act on wound healing were identified through network pharmacology assays, such as signal transducer and activator of transcription 3 (STAT3), JUN, interleukin-1β (IL-1β), tumor necrosis factor (TNF), and prostaglandin G/H synthase 2 (PTGS2). Furthermore, in vivo validation showed that A. dahurica accelerated wound healing through anti-inflammatory effects. More specifically, it regulates the polarization of M1 and M2 subtypes of macrophages. A. dahurica exerted a curative effect on diabetic wound healing by regulating the inflammation. Hence, pharmacologic network analysis combined with in vivo validation elucidated the probable effects and underlying mechanisms of A. dahurica’s therapeutic effect on diabetic wound healing

Identification of anthropogenic parameters for a regional nitrogen balance model via field investigation of six ecosystems in China

To evaluate the impact of human behavior (with regard food consumption, waste disposal and farming method) on nitrogen flow, a field investigation was conducted in six typical ecosystems in China. A number of parameters for regional nitrogen balance models were identified during the investigation. The results show that the average per-capita daily protein intake is 107 g. While there is an insignificant difference in total protein intake among the different ecosystems, protein intake from all food groups, except for eggs, is significantly different (P a parts per thousand currency sign 0.05). Differences in diet, along with those in socio-economic conditions, reflect differences in the characteristics of the ecosystems. Regarding per-capita annual potential nitrogen loading from human excrement, a considerable difference exists between the urban rich and the rural poor. In urban areas, approximately 1.02 kg N is returned to farmlands and 5.49 kg N is directly discharged into rivers. In rural regions, on the other hand, approximately 4.33 kg N is returned to farmlands and 1.60 kg N is directly discharged into rivers. Furthermore, urea and mixed fertilizers constitute the most common chemical fertilizers in the study area. Fertilizer diversification is practiced in a range of agricultural lands, paddy-fields and irrigated plains. In the oasis and paddy-field agricultural systems, many of the agricultural by-products (e.g., straw) are burned or mixed with base-fertilizers and plowed into the soil. In irrigated agricultural systems, over 70% of agricultural by-products are recycled as livestock feed. In most instances, livestock excrement is directly reduced in the pasturelands or reused in the fields as manure. Occasionally, as in the case of large-scale breeding, excrements are usually abandoned

Neuroprotective Mechanisms of Lycium barbarum Polysaccharides Against Ischemic Insults by Regulating NR2B and NR2A Containing NMDA Receptor Signaling Pathways

Glutamate excitotoxicity plays an important role in neuronal death after ischemia. However, all clinical trials using glutamate receptor inhibitors have failed. This may be related to the evidence that activation of different subunit of NMDA receptor will induce different effects. Many studies have shown that activation of the intrasynaptic NR2A subunit will stimulate survival signaling pathways, whereas upregulation of extrasynaptic NR2B will trigger apoptotic pathways. A Lycium barbarum polysaccharide (LBP) is a mixed compound extracted from Lycium barbarum fruit. Recent studies have shown that LBP protects neurons against ischemic injury by anti-oxidative effects. Here we first reported that the effect of LBP against ischemic injury can be achieved by regulating NR2B and NR2A signaling pathways. By in vivo study, we found LBP substantially reduced CA1 neurons from death after transient global ischemia and ameliorated memory deficit in ischemic rats. By in vitro study, we further confirmed that LBP increased the viability of primary cultured cortical neurons when exposed to oxygen-glucose deprivation (OGD) for 4 h. Importantly, we found that LBP antagonized increase in expression of major proteins in the NR2B signal pathway including NR2B, nNOS, Bcl-2-associated death promoter (BAD), cytochrome C (cytC) and cleaved caspase-3, and also reduced ROS level, calcium influx and mitochondrial permeability after 4 h OGD. In addition, LBP prevented the downregulation in the expression of NR2A, pAkt and pCREB, which are important cell survival pathway components. Furthermore, LBP attenuated the effects of a NR2B co-agonist and NR2A inhibitor on cell mortality under OGD conditions. Taken together, our results demonstrated that LBP is neuroprotective against ischemic injury by its dual roles in activation of NR2A and inhibition of NR2B signaling pathways, which suggests that LBP may be a superior therapeutic candidate for targeting glutamate excitotoxicity for the treatment of ischemic stroke

Lifelike Agility and Play on Quadrupedal Robots using Reinforcement Learning and Generative Pre-trained Models

Summarizing knowledge from animals and human beings inspires robotic innovations. In this work, we propose a framework for driving legged robots act like real animals with lifelike agility and strategy in complex environments. Inspired by large pre-trained models witnessed with impressive performance in language and image understanding, we introduce the power of advanced deep generative models to produce motor control signals stimulating legged robots to act like real animals. Unlike conventional controllers and end-to-end RL methods that are task-specific, we propose to pre-train generative models over animal motion datasets to preserve expressive knowledge of animal behavior. The pre-trained model holds sufficient primitive-level knowledge yet is environment-agnostic. It is then reused for a successive stage of learning to align with the environments by traversing a number of challenging obstacles that are rarely considered in previous approaches, including creeping through narrow spaces, jumping over hurdles, freerunning over scattered blocks, etc. Finally, a task-specific controller is trained to solve complex downstream tasks by reusing the knowledge from previous stages. Enriching the knowledge regarding each stage does not affect the usage of other levels of knowledge. This flexible framework offers the possibility of continual knowledge accumulation at different levels. We successfully apply the trained multi-level controllers to the MAX robot, a quadrupedal robot developed in-house, to mimic animals, traverse complex obstacles, and play in a designed challenging multi-agent Chase Tag Game, where lifelike agility and strategy emerge on the robots. The present research pushes the frontier of robot control with new insights on reusing multi-level pre-trained knowledge and solving highly complex downstream tasks in the real world