The impacts of carbon emissions on global manufacturing value chain relocation: Theoretical and empirical development of a meso-level model

As a stark contrast to the diminishing media profile of the UN climate change talks, the global manufacturers appear to have become more carbon aware than ever before. Carbon audits have been carried out within many corporations to assess the carbon intensity of production processes. This is partly to address cost issues of the present (i.e. the recent rise in fossil fuel prices) and of the future (e.g. new carbon related taxes and trade tariffs). Moreover, the adoption of low carbon, clean manufacturing processes has become an increasingly prominent part of branding for many products, which could affect market share and business performance in ways that go beyond questions of cost competitiveness. How will this carbon awareness affect the configuration of the value chains of global manufacturing? Will the individual manufacturers’ decisions lead to an effective reduction of total carbon emissions at the global value chain scale? Our paper aims to answer these questions through developing a theoretical model and testing it empirically through case studies of global value chains. The model accounts explicitly costs of energy, carbon, other intermediate inputs and primary inputs in the production and transport of each component, product assembly and delivery to the market. Much work has been done on the value chain location problem – e.g. on the production unbundling among different countries from a macro-economic perspective, or on operations management at the microscopic or individual manufacturer level. It is only until recently that the economic and technology aspects have been combined in the study of global value chains (for example in the paper by Baldwin and Venables in last year’s ERSA Congress). The appropriate spatial scale for our research questions would appear to be at a meso-level: i.e. the model goes beyond the micro-level operational analysis of a single plant to cover the entire value chain for a given product, but does not cover the full interactions at the macro level. This perspective is relatively rare in the literature and provides a tool that connects the micro level and macro level perspectives.

Two weight inequality for Hankel form on weighted Bergman spaces induced by doubling weights

The boundedness of the small Hankel operator $h_f^\nu(g)=P_\nu(f\bar{g})$, induced by an analytic symbol $f$ and the Bergman projection $P_\nu$ associated to $\nu$, acting from the weighted Bergman space A^p_\om to $A^q_\nu$ is characterized on the full range $0<p,q<\infty$ when $\omega,\nu$ belong to the class $\mathcal{D}$ of radial weights admitting certain two-sided doubling conditions. Certain results obtained are equivalent to the boundedness of bilinear Hankel forms, which are in turn used to establish the weak factorization $A_{\eta}^{q}=A_{\omega}^{p_{1}}\odot A_{\nu}^{p_{2}}$, where $1<q,p_{1},p_{2}<\infty$ such that $q^{-1}=p_{1}^{-1}+p_{2}^{-1}$ and $\widetilde{\eta}^{\frac{1}{q}}\asymp\widetilde{\omega}^{\frac{1}{p_{1}}}\widetilde{\nu}^{\frac{1}{p_{2}}}$. Here $\widetilde{\tau}(r)=\int_r^1\tau(t)\,dt/(1-t)$ for all $0\le r<1$

Establishment of an isotope dilution LC-MS/MS method revealing kinetics and distribution of co-occurring mycotoxins in rats

An isotope dilution liquid chromatography-tandem mass spectrometry (LC-MS/MS) method with a fast sample preparation using homemade clean-up cartridges was developed for simultaneous determination of co-occurring mycotoxins exemplified with aflatoxin B1 (AFB1) and T-2 toxin (T-2) in representative biomatrices of rat plasma, heart, liver, kidney, spleen, lung and brain in a total run time of 7 min. The established approach using stable internal standards of [C-13(17)]-AFB1 and [C-13(24)]-T-2 was extensively validated by determining the specificity, linearity (R-2 >= 0.9990), sensitivity (lower limit of quantitation at 0.05 ng mL(-1)), accuracy (70.9-107.7%), precision (RSD = 70.8%). Based on this methodological advance, the subsequent kinetics and tissue distribution after oral administration of 0.5 mg kg(-1) b.w. of both AFB1 and T-2 in rats were thoroughly studied. As revealed, both AFB1 and T-2 were rapidly eliminated with the half-life time (t(1/2)) in plasma of 8.44 +/- 4.02 h and 8.12 +/- 4.05 h, respectively. Moreover, AFB1 accumulated in all organs where the highest concentration was observed in liver (1.34 mu g kg(-1)), followed by kidney (0.76 mu g kg(-1)). Notably, only low levels of T-2 were observed in spleen (0.70 mu g kg(-1)) and in liver (0.15 mu g kg(-1)). The achieved data as supporting evidence would substantially promote the practical application of the proposed LC-MS/MS method for in vivo toxicokinetics and toxicity studies of co-occurring mycotoxins imitating natural incidence in rat system

Application of particle swarm optimization-based least square support vector machine in quantitative analysis of extraction solution of yangxinshi tablet using near infrared spectroscopy

A particle swarm optimization (PSO)-based least square support vector machine (LS-SVM) method was investigated for quantitative analysis of extraction solution of Yangxinshi tablet using near infrared (NIR) spectroscopy. The usable spectral region (5400–6200 cm-1) was identified, then the first derivative spectra smoothed using a Savitzky–Golay filter were employed to establish calibration models. The PSO algorithm was applied to select the LS-SVM hyperparameters (including the regularization and kernel parameters). The calibration models of total flavonoids, puerarin, salvianolic acid B and icariin were established using the optimum hyperparameters of LS-SVM. The performance of LS-SVM models were compared with partial least squares (PLS) regression, feed-forward back-propagation network (BPANN) and support vector machine (SVM). Experimental results showed that both the calibration results and prediction accuracy of the PSO-based LS-SVM method were superior to PLS, BP-ANN and SVM. For PSO-based LS-SVM models, the determination coefficients (R2) for the calibration set were above 0.9881, and the RSEP values were controlled within 5.772%. For the validation set, the RMSEP values were close to RMSEC and less than 0.042, the RSEP values were under 8.778%, which were much lower than the PLS, BP-ANN and SVM models. The PSO-based LS-SVM algorithm employed in this study exhibited excellent calibration performance and prediction accuracy, which has definite practice significance and application value

Multiplex Immunoassays of Plant Viruses Based on Functionalized Upconversion Nanoparticles Coupled with Immunomagnetic Separation

A sensitive, specific and rapid method for the detection of three different kinds of plant viruses including tomato ringspot virus (ToRSV), bean pod mottle virus (BPMV) and arabis mosaic virus (ArMV) was demonstrated using novel upconversion nanoparticles (UCNPs) as a fluorescence marker coupled with immunomagnetic separation. Magnetic nanoparticles (MNPs, ∼100 nm) were coated with different antibodies were employed to capture and enrich the target viruses. Then antibody-conjugated UCNPs as signal probes were added to form sandwich complexes. This was followed by a fluorescence measurement using a 980 nm laser. This method not only avoids the difficulty in simultaneous detection of multiple independent organic fluorphores that require distinct excitation wavelengths, and auto-fluorescence of biological samples due to the higher-energy excitation of QDs but also amplifies detection signal by UCNPs-tags together with easy separation of samples by magnetic forces, demonstrating the potential to be used for detecting virus in the field of environmental safety and other fields

Nurturing Business Ecosystems for Growth in a Foreign Market: Incubating, Identifying and Integrating Stakeholders

This paper explores the process of nurturing a business ecosystem to facilitate corporate growth in an unfamiliar foreign market with high product uncertainty and no network resources. The authors conducted a qualitative, longitudinal study by examining a successful business case — ARM (a leader in microprocessor intellectual property) — to demonstrate how firms nurture their business ecosystems to develop in the Chinese market and to stimulate demand even with- out the advantages of resources and stabilized products. Based on the road map method, this paper develops a framework of creating a business ecosystem in three sequential stages namely, incubating complementary partners, identifying leader partners, and integrating ecosystem part- ners. The findings enrich classic international business and demand chain theories by highlighting different roles stakeholders adopt to cope with uncertain products in a foreign market. In practical terms, these findings also provide Mode 2 knowledge with application context (Gibbons et al., 1997) on entering new markets by building up an ecosystem

A protocol of Chinese expert consensuses for the management of health risk in the general public

IntroductionNon-communicable diseases (NCDs) represent the leading cause of mortality and disability worldwide. Robust evidence has demonstrated that modifiable lifestyle factors such as unhealthy diet, smoking, alcohol consumption and physical inactivity are the primary causes of NCDs. Although a series of guidelines for the management of NCDs have been published in China, these guidelines mainly focus on clinical practice targeting clinicians rather than the general population, and the evidence for NCD prevention based on modifiable lifestyle factors has been disorganized. Therefore, comprehensive and evidence-based guidance for the risk management of major NCDs for the general Chinese population is urgently needed. To achieve this overarching aim, we plan to develop a series of expert consensuses covering 15 major NCDs on health risk management for the general Chinese population. The objectives of these consensuses are (1) to identify and recommend suitable risk assessment methods for the Chinese population; and (2) to make recommendations for the prevention of major NCDs by integrating the current best evidence and experts’ opinions.Methods and analysisFor each expert consensus, we will establish a consensus working group comprising 40–50 members. Consensus questions will be formulated by integrating literature reviews, expert opinions, and an online survey. Systematic reviews will be considered as the primary evidence sources. We will conduct new systematic reviews if there are no eligible systematic reviews, the methodological quality is low, or the existing systematic reviews have been published for more than 3 years. We will evaluate the quality of evidence and make recommendations according to the GRADE approach. The consensuses will be reported according to the Reporting Items for Practice Guidelines in Healthcare (RIGHT)

Effects of Peptidoglycan, Lipoteichoic Acid and Lipopolysaccharide on Inflammation, Proliferation and Milk Fat Synthesis in Bovine Mammary Epithelial Cells

The mammary gland of the cow is particularly susceptible to infections of a wide range of pathogenic bacteria, including both Gram-positive and Gram-negative bacteria. The endotoxins of these pathogenic bacteria include peptidoglycan (PGN), lipoteichoic acid (LTA) and lipopolysaccharide (LPS), and they are the pathogen-associated molecular patterns (PAMPs) to induce mastitis. LPS can directly inhibit proliferation and milk fat synthesis of bovine mammary epithelial cells (BMECs) while inducing mastitis, but it is unclear whether PGN and LTA also have such effects. Furthermore, since the three PAMPs usually appear simultaneously in the udder of cows with mastitis, their synergistic effects on proliferation and milk fat synthesis of BMECs are worth investigating. The immortalized BMECs (MAC-T cells) were stimulated for 24 h using various concentrations of PGN, LTA and LPS, respectively, to determine the doses that could effectively cause inflammatory responses. Next, the cells were stimulated for 24 h with no endotoxins (CON), PGN, LTA, LPS, PGN + LTA, and PGN + LTA + LPS, respectively, with the predetermined doses to analyze their effects on proliferation and milk fat synthesis of BMECs. PGN, LTA and LPS successfully induced inflammatory responses of BMECs with doses of 30, 30 and 0.1 &mu;g/mL, respectively. Although the proliferation of BMECs was significantly inhibited in the following order: LTA &lt; PGN + LTA &lt; PGN + LTA + LPS, there was no change in cell morphology and cell death. LTA significantly promoted the expression of fatty acid synthesis-related genes but did not change the content of intracellular triglyceride (TG), compared with the CON group. The mRNA expression of fatty acid synthesis-related genes in the LPS group was the lowest among all the groups. Meanwhile, LPS significantly decreased the content of intracellular non-esterified fatty acids (NEFAs) and TG, compared with the CON group. PGN had no effects on milk fat synthesis. Co-stimulation with PGN, LTA and LPS significantly increased the expression of fat acid synthesis-related genes and the intracellular NEFAs, but decreased intracellular TG, compared with sole LPS stimulation. Collectively, PGN, LTA and LPS showed an additive effect on inhibiting proliferation of BMECs. The promoting role of LTA in fatty acid synthesis might offset the negative effects of LPS in this regard, but co-stimulation with PGN, LTA and LPS significantly decreased intracellular TG content

Improvement of NIR models for quality parameters of leech and earthworm medicines using outlier multiple diagnoses

Leeches and earthworms are the main ingredients of Shuxuetong injection compositions, which are natural biomedicines. Near infrared (NIR) diffuse reflection spectroscopy has been used for quality assurance of Chinese medicines. In the present work, NIR spectroscopy was proposed as a rapid and nondestructive technique to assess the moisture content (MC), soluble solid content (SSC) and hypoxanthine content (HXC) of leeches and earthworms. This study goal was to improve NIR models for accurate quality control of leech and earthworm using outlier multiple diagnoses (OMD). OMD was composed of four outlier detection methods: spectrum outlier diagnostic (MD), leverage diagnostic (LD), principal component scores diagnostic (PCSD) and factor loading diagnostic (FLD). Conventional outlier diagnoses (MD, LD) and OMD were compared, and the best NIR models were those based on OMD. The correlation coefficients (R) for leech were 0.9779, 0.9616 and 0.9406 for MC, SSC and HXC, respectively. The values of relative standard error of prediction (RSEP) for leech were 2.3%, 5.1% and 9.0% for MC, SSC and HXC, respectively. The values of R for earthworm were 0.9478, 0.9991 and 0.9605 for MC, SSC and HXC, respectively. The values of RSEP for earthworm were 8.8%, 2.4% and 12% for MC, SSC and HXC, respectively. The performance of the NIR models was certainly improved by OMD