Experimenting towards a low-carbon city: Policy evolution and nested structure of innovation

Cities can play a key role in the low-carbon transition, with an increasing number of cities engaging in carbon mitigation actions. The literature on urban low-carbon transition shows that low-carbon urban development is an inevitable trend of urban sustainable future; there is a great potential albeit with some limitations for cities to reduce its carbon footprints, and there are diverse pathways for cities to achieve low-carbon development. There is, however, a limited understanding in terms of the internal mechanism of urban low-carbon transition, especially in rapidly developing economies. This paper attempts to address this gap. We examine how low-carbon policies emerge and evolve, and what are the enabling mechanisms, taking Shanghai as a case study. We developed an analytical framework drawing on system innovation theory and sustainability experiments for this purpose. A total of 186 relevant policies were selected and analyzed, which is supplemented by the interviews with stakeholders in the government to gain a deeper insight into the policy contexts in Shanghai. We found that the city's low-carbon initiatives are embedded and integrated into its existing policy frameworks. A strong vertical linkage between the central and the local governments, and more importantly, a nested structure for innovative policy practices were identified, where a top-down design is met with bottom-up innovation and proactive adoption of enabling mechanism. The structure includes two layers of experiments that facilitate learning through policy experiments across scales. The uniqueness, effectiveness, applicability and limitations of these efforts are discussed. The findings provide new theoretical and empirical insights into the multilevel governance of low-carbon transition in cities

ESCAPING FROM FRIENDS: EXPLORING THE NEED TO BE DIFFERENT IN SOCIAL COMMERCE SITES

This paper studies the influence of observational learning and herding in networks of friends versus informants on consumer purchase decisions. We explore how people trade off their needs to belong and to be different by first developing an exponential random graph model to predict online purchasing decision while taking into considerations of product properties, consumer demographics, online rating, as well as consumer social networks. We test our model through collecting panel data on a leading social commerce site in Asia. Contrary to the popular belief that people tend to follow friends’ choices, subjects in our context are more likely to diverge from the popular choice among their friends. As our study shows that the need to be different can dominate the need to be belong in certain contexts, we discuss managerial implications of our results for social media marketing

State Estimation of Wireless Sensor Networks in the Presence of Data Packet Drops and Non-Gaussian Noise

Distributed Kalman filter approaches based on the maximum correntropy criterion have recently demonstrated superior state estimation performance to that of conventional distributed Kalman filters for wireless sensor networks in the presence of non-Gaussian impulsive noise. However, these algorithms currently fail to take account of data packet drops. The present work addresses this issue by proposing a distributed maximum correntropy Kalman filter that accounts for data packet drops (i.e., the DMCKF-DPD algorithm). The effectiveness and feasibility of the algorithm are verified by simulations conducted in a wireless sensor network with intermittent observations due to data packet drops under a non-Gaussian noise environment. Moreover, the computational complexity of the DMCKF-DPD algorithm is demonstrated to be moderate compared with that of a conventional distributed Kalman filter, and we provide a sufficient condition to ensure the convergence of the proposed algorithm

Control of astrocyte progenitor specification, migration and maturation by Nkx6.1 homeodomain transcription factor.

Although astrocytes are the most abundant cell type in the central nervous system (CNS), little is known about their molecular specification and differentiation. It has previously been reported that transcription factor Nkx6.1 is expressed in neuroepithelial cells that give rise to astrocyte precursors in the ventral spinal cord. In the present study, we systematically investigated the function of Nkx6.1 in astrocyte development using both conventional and conditional Nkx6.1 mutant mice. At early postnatal stages, Nkx6.1 was expressed in a subpopulation of astrocytes in the ventral spinal cord. In the conventional Nkx6.1KO spinal cord, the initial specification of astrocyte progenitors was affected by the mutation, and subsequent migration and differentiation were disrupted in newborn mice. In addition, the development of VA2 subtype astrocytes was also inhibited in the white matter. Further studies with Nkx6.1 conditional mutants revealed significantly delayed differentiation and disorganized arrangement of fibrous astrocytes in the ventral white matter. Together, our studies indicate that Nkx6.1 plays a vital role in astrocyte specification and differentiation in the ventral spinal cord

Distributed fusion filter over lossy wireless sensor networks with the presence of non-Gaussian noise

The information transmission between nodes in a wireless sensor networks (WSNs) often causes packet loss due to denial-of-service (DoS) attack, energy limitations, and environmental factors, and the information that is successfully transmitted can also be contaminated by non-Gaussian noise. The presence of these two factors poses a challenge for distributed state estimation (DSE) over WSNs. In this paper, a generalized packet drop model is proposed to describe the packet loss phenomenon caused by DoS attacks and other factors. Moreover, a modified maximum correntropy Kalman filter is given, and it is extended to distributed form (DM-MCKF). In addition, a distributed modified maximum correntropy Kalman filter incorporating the generalized data packet drop (DM-MCKF-DPD) algorithm is provided to implement DSE with the presence of both non-Gaussian noise pollution and packet drop. A sufficient condition to ensure the convergence of the fixed-point iterative process of the DM-MCKF-DPD algorithm is presented and the computational complexity of the DM-MCKF-DPD algorithm is analyzed. Finally, the effectiveness and feasibility of the proposed algorithms are verified by simulations

Impact of dietary manganese on intestinal barrier and inflammatory response in broilers challenged with Salmonella Typhimurium

Growing concern for public health and food safety has prompted a special interest in developing nutritional strategies for removing waterborne and foodborne pathogens, including Salmonella. Strong links between manganese (Mn) and intestinal barrier or immune function hint that dietary Mn supplementation is likely to be a promising approach to limit the loads of pathogens in broilers. Here, we provide evidence that Salmonella Typhimurium (S. Typhimurium, 4 × 108 CFUs) challenge-induced intestinal injury along with systemic Mn redistribution in broilers. Further examining of the effect of dietary Mn treatments (a basal diet plus additional 0, 40, or 100 mg Mn/kg for corresponding to Mn-deficient, control, or Mn-surfeit diet, respectively) on intestinal barrier and inflammation status of broilers infected with S. Typhimurium revealed that birds fed the control and Mn-surfeit diets exhibited improved intestinal tight junctions and microbiota composition. Even without Salmonella infection, dietary Mn deficiency alone increased intestinal permeability by impairing intestinal tight junctions. In addition, when fed the control and Mn-surfeit diets, birds showed decreased Salmonella burdens in cecal content and spleen, with a concomitant increase in inflammatory cytokine levels in spleen. Furthermore, the dietary Mn-supplementation-mediated induction of cytokine production was probably associated with the nuclear factor kappa-B (NF-κB)/hydrogen peroxide (H2O2) pathway, as judged by the enhanced manganese superoxide dismutase activity and the increased H2O2 level in mitochondria, together with the increased mRNA level of NF-κB in spleen. Ingenuity-pathway analysis indicated that acute-phase response pathways, T helper type 1 pathway, and dendritic cell maturation were significantly activated by the dietary Mn supplementation. Our data suggest that dietary Mn supplementation could enhance intestinal barrier and splenic inflammatory response to fight against Salmonella infection in broilers

Interaction of CK1δ with γTuSC ensures proper microtubule assembly and spindle positioning.

Casein kinase 1δ (CK1δ) family members associate with microtubule-organizing centers (MTOCs) from yeast to humans, but their mitotic roles and targets have yet to be identified. We show here that budding yeast CK1δ, Hrr25, is a γ-tubulin small complex (γTuSC) binding factor. Moreover, Hrr25's association with γTuSC depends on its kinase activity and its noncatalytic central domain. Loss of Hrr25 kinase activity resulted in assembly of unusually long cytoplasmic microtubules and defects in spindle positioning, consistent with roles in regulation of γTuSC-mediated microtubule nucleation and the Kar9 spindle-positioning pathway, respectively. Hrr25 directly phosphorylated γTuSC proteins in vivo and in vitro, and this phosphorylation promoted γTuSC integrity and activity. Because CK1δ and γTuSC are highly conserved and present at MTOCs in diverse eukaryotes, similar regulatory mechanisms are expected to apply generally in eukaryotes

Grey Relational Analysis and Its Application Based on the Angle Perspective in Time Series

The research scope of grey relational analysis has not been developed in recent years, and the exiting GRA models are all based on three perspectives, including distance perspective, slope perspective, and area perspective. Under this situation, a novel model called grey relational analysis based on angle perspective (GRAAP) was developed in this paper for the first time, and this model can expand the research scope of GRA. Like other GRA models, GRAAP not only has the properties of symmetry, uniqueness, comparability, and so forth but also can be used to make predictions, assessments, classifications, and so on. Finally, this novel model was proved to be rational and effective through a comparative study on the similar degree of four time series