Is the red dragon green? An examination of the antecedents and consequences of environmental proactivity in China

China is the world’s second largest economy and the largest emitter of carbon dioxide, yet we know little about environmental proactivity in the most populated country in the world. We address this gap through a survey of 161 Chinese companies with two respondents per firm (N = 322) where we seek to identify the antecedents and consequences of environmental proactivity. We identify two categorizations of environmental proactivity: Environmental operational improvements and environmental reporting. We find that ecological motivations and regulatory stakeholder pressure are positively related to both types of environmental proactivity, and external stakeholder pressure is negatively related to environmental reporting. Furthermore, we find that (1) if a firm is environmentally proactive (as it relates to either measure) and they are ecologically motivated, there is a positive and significant cost advantage, and (2) if a firm makes use of environmental operational improvement and they are competitively motivated, there is a positive and significant reputation advantage. Implications for researchers, managers, and policy-makers in China are discussed

Decreased lung function with mediation of blood parameters linked to e-waste lead and cadmium exposure in preschool children

Blood lead (Pb) and cadmium (Cd) levels have been associated with lower lung function in adults and smokers, but whether this also holds for children from electronic waste (e-waste) recycling areas is still unknown. To investigate the contribution of blood heavy metals and lung function levels, and the relationship among living area, the blood parameter levels, and the lung function levels, a total of 206 preschool children from Guiyu (exposed area), and Haojiang and Xiashan (reference areas) were recruited and required to undergo blood tests and lung function tests during the study period. Preschool children living in e-waste exposed areas were found to have a 1.37 mu g/dL increase in blood Pb, 1.18 mu g/L. increase in blood Cd, and a 41.00 x 10(9)/L increase in platelet counts, while having a 2.82 decrease in hemoglobin, 92 mL decrease in FVC and 86 mL decrease in FEV1. Each unit of hemoglobin (1 g/L) decline was associated with 5 mL decrease in FVC and 4 mL decrease in FEV1. We conclude that children living in e-waste exposed area have higher levels of blood Pb, Cd and platelets, and lower levels of hemoglobin and lung function. Hemoglobin can be a good predictor for lung function levels. (C) 2017 Elsevier Ltd. All rights reserved.</p

Behavioural Psychology of Unique Family Firms Toward R&D Investment in the Digital Era: The Role of Ownership Discrepancy

This study examines the R&amp;D investment behaviour of different types of family-controlled firms with the moderating role of ownership discrepancy between cash-flow rights and excess voting rights by using the sufficiency conditions’ theoretical framework of ability and willingness developed by De Massis. It uses data from family firms that have issued A-shares from 2008 to 2018. They used pooled OLS regression for data analysis and Tobit regression for robustness checks. This study classifies family firm types into two categories, namely, the lone-controller family firms (LCFFs) and the multi-controller family firms (MCFFs), with each being further classified as “excess” or “no excess” voting rights. Both LCFFs without excess voting rights and MCFFs with excess voting rights have the “ability” and “willingness” toward R&amp;D investment. LCFFs with excess voting rights and MCFFs without excess voting rights only have the ability but low willingness to invest in R&amp;D. The study also establishes that Chinese family-controlled firms are heterogeneous toward risky investment. To the best of our knowledge, this study is the first to differentiate Chinese family firms by their unique ownership structure characteristics in investigating the effect of the family firm structure on R&amp;D investment. The study is a novel attempt to test the willingness and ability framework of LCFFs and MCFFs. Previous studies based on agency theory have tacitly assumed that ability and willingness exist in family-controlled firms. However, this study challenges this implicit assumption

Learning-based Nonlinear Model Predictive Control

© 2017 This paper presents stabilizing Model Predictive Controllers (MPC) in which prediction models are inferred from experimental data of the inputs and outputs of the plant. Using a nonparametric machine learning technique called LACKI, the estimated (possibly nonlinear) model function together with an estimation of Holder constant is provided. Based on these, a number of predictive controllers with stability guaranteed by design are proposed. Firstly, the case when the prediction model is estimated offline is considered and robust stability and recursive feasibility is ensured by using tightened constraints in the optimisation problem. This controller has been extended to the more interesting and complex case: the online learning of the model, where the new data collected from feedback is added to enhance the prediction model. An on-line learning MPC based on a double sequence of predictions is proposed.Spanish MINECO Grant PRX15-00300 and projects DPI2013-48243-C2-2-R and DPI2016-76493-C3-1-R. UK Engineering and Physical Research Council, grant no.EP/J012300/1

Patient-derived iPSCs link elevated mitochondrial respiratory complex I function to osteosarcoma in Rothmund-Thomson syndrome

Rothmund-Thomson syndrome (RTS) is an autosomal recessive genetic disorder characterized by poikiloderma, small stature, skeletal anomalies, sparse brows/lashes, cataracts, and predisposition to cancer. Type 2 RTS patients with biallelic RECQL4 pathogenic variants have multiple skeletal anomalies and a significantly increased incidence of osteosarcoma. Here, we generated RTS patient-derived induced pluripotent stem cells (iPSCs) to dissect the pathological signaling leading to RTS patient-associated osteosarcoma. RTS iPSC-derived osteoblasts showed defective osteogenic differentiation and gain of in vitro tumorigenic ability. Transcriptome analysis of RTS osteoblasts validated decreased bone morphogenesis while revealing aberrantly upregulated mitochondrial respiratory complex I gene expression. RTS osteoblast metabolic assays demonstrated elevated mitochondrial respiratory complex I function, increased oxidative phosphorylation (OXPHOS), and increased ATP production. Inhibition of mitochondrial respiratory complex I activity by IACS-010759 selectively suppressed cellular respiration and cell proliferation of RTS osteoblasts. Furthermore, systems analysis of IACS-010759-induced changes in RTS osteoblasts revealed that chemical inhibition of mitochondrial respiratory complex I impaired cell proliferation, induced senescence, and decreased MAPK signaling and cell cycle associated genes, but increased H19 and ribosomal protein genes. In summary, our study suggests that mitochondrial respiratory complex I is a potential therapeutic target for RTS-associated osteosarcoma and provides future insights for clinical treatment strategies

Neutrino Physics with JUNO

The Jiangmen Underground Neutrino Observatory (JUNO), a 20 kton multi-purposeunderground liquid scintillator detector, was proposed with the determinationof the neutrino mass hierarchy as a primary physics goal. It is also capable ofobserving neutrinos from terrestrial and extra-terrestrial sources, includingsupernova burst neutrinos, diffuse supernova neutrino background, geoneutrinos,atmospheric neutrinos, solar neutrinos, as well as exotic searches such asnucleon decays, dark matter, sterile neutrinos, etc. We present the physicsmotivations and the anticipated performance of the JUNO detector for variousproposed measurements. By detecting reactor antineutrinos from two power plantsat 53-km distance, JUNO will determine the neutrino mass hierarchy at a 3-4sigma significance with six years of running. The measurement of antineutrinospectrum will also lead to the precise determination of three out of the sixoscillation parameters to an accuracy of better than 1\%. Neutrino burst from atypical core-collapse supernova at 10 kpc would lead to ~5000inverse-beta-decay events and ~2000 all-flavor neutrino-proton elasticscattering events in JUNO. Detection of DSNB would provide valuable informationon the cosmic star-formation rate and the average core-collapsed neutrinoenergy spectrum. Geo-neutrinos can be detected in JUNO with a rate of ~400events per year, significantly improving the statistics of existing geoneutrinosamples. The JUNO detector is sensitive to several exotic searches, e.g. protondecay via the $p\to K^++\bar\nu$ decay channel. The JUNO detector will providea unique facility to address many outstanding crucial questions in particle andastrophysics. It holds the great potential for further advancing our quest tounderstanding the fundamental properties of neutrinos, one of the buildingblocks of our Universe

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Video Recovery via Learning Variation and Consistency of Images

Matrix completion algorithms have been popularly used to recover images with missing entries, and they are proved to be very effective. Recent works utilized tensor completion models in video recovery assuming that all video frames are homogeneous and correlated. However, real videos are made up of different episodes or scenes, i.e. heterogeneous. Therefore, a video recovery model which utilizes both video spatiotemporal consistency and variation is necessary. To solve this problem, we propose a new video recovery method Sectional Trace Norm with Variation and Consistency Constraints (STN-VCC). In our model, capped L1-norm regularization is utilized to learn the spatial-temporal consistency and variation between consecutive frames in video clips. Meanwhile, we introduce a new low-rank model to capture the low-rank structure in video frames with a better approximation of rank minimization than traditional trace norm. An efficient optimization algorithm is proposed, and we also provide a proof of convergence in the paper. We evaluate the proposed method via several video recovery tasks and experiment results show that our new method consistently outperforms other related approaches