A conceptual framework for supply chain collaboration:empirical evidence from the agri-food industry

Purpose - The purpose of this paper is to analyse the concept of supply chain collaboration and to provide an overall framework that can be used as a conceptual landmark for further empirical research. In addition, the concept is explored in the context of agri-food industry and particularities are identified. Finally, the paper submits empirical evidence from an exploratory case study in the agri-food industry, at the grower-processor interface, and information regarding the way the concept is actually applied in small medium-sized enterprises (SMEs) is presented. Design/methodology/approach - The paper employed case study research by conducting in-depth interviews in the two companies. Findings - Supply chain collaboration concept is of significant importance for the agri-food industry however, some constraints arise due to the nature of industry's products, and the specific structure of the sector. Subsequently, collaboration in the supply chain is often limited to operational issues and to logistics-related activities. Research limitations/implications - Research is limited to a single case study and further qualitative testing of the conceptual model is needed in order to adjust the model before large scale testing. Practical implications - Case study findings may be transferable to other similar dual relationships at the grower-processor interface. Weaker parts in asymmetric relationships have opportunities to improve their position, altering the dependence balance, by achieving product/process excellence. Originality/value - The paper provides evidence regarding the applicability of the supply chain collaboration concept in the agri-food industry. It takes into consideration not relationships between big multinational companies, but SMEs

Research Cloud Data Communities

Big Data, big science, the data deluge, these are topics we are hearing about more and more in our research pursuits. Then, through media hype, comes cloud computing, the saviour that is going to resolve our Big Data issues. However, it is difficult to pinpoint exactly what researchers can actually do with data and with clouds, how they get to exactly solve their Big Data problems, and how they get help in using these relatively new tools and infrastructure. Since the beginning of 2012, the NeCTAR Research Cloud has been running at the University of Melbourne, attracting over 1,650 users from around the country. This has not only provided an unprecedented opportunity for researchers to employ clouds in their research, but it has also given us an opportunity to clearly understand how researchers can more easily solve their Big Data problems. The cloud is now used daily, from running web servers and blog sites, through to hosting virtual laboratories that can automatically create hundreds of servers depending on research demand. Of course, it has also helped us understand that infrastructure isn’t everything. There are many other skillsets needed to help researchers from the multitude of disciplines use the cloud effectively. How can we solve Big Data problems on cloud infrastructure? One of the key aspects are communities based on research platforms: Research is built on collaboration, connection and community, and researchers employ platforms daily, whether as bio-imaging platforms, computational platforms or cloud platforms (like DropBox). There are some important features which enabled this to work.. Firstly, the borders to collaboration are eased, allowing communities to access infrastructure that can be instantly built to be completely open, through to completely closed, all managed securely through (nationally) standardised interfaces. Secondly, it is free and easy to build servers and infrastructure, but it is also cheap to fail, allowing for experimentation not only at a code-level, but at a server or infrastructure level as well. Thirdly, this (virtual) infrastructure can be shared with collaborators, moving the practice of collaboration from sharing papers and code to sharing servers, pre-configured and ready to go. And finally, the underlying infrastructure is built with Big Data in mind, co-located with major data storage infrastructure and high-performance computers, and interconnected with high-speed networks nationally to research instruments. The research cloud is fundamentally new in that it easily allows communities of researchers, often connected by common geography (research precincts), discipline or long-term established collaborations, to build open, collaborative platforms. These open, sharable, and repeatable platforms encourage coordinated use and development, evolving to common community-oriented methods for Big Data access and data manipulation. In this paper we discuss in detail critical ingredients in successfully establishing these communities, as well as some outcomes as a result of these communities and their collaboration enabling platforms. We consider astronomy as an exemplar of a research field that has already looked to the cloud as a solution to the ensuing data tsunami

Plastic Solid Waste (PSW) in the Context of Life Cycle Assessment (LCA) and Sustainable Management

Over the past few decades, life cycle assessment (LCA) has been established as a critical tool for the evaluation of the environmental burdens of chemical processes and materials cycles. The increasing amount of plastic solid waste (PSW) in landfills has raised serious concern worldwide for the most effective treatment. Thermochemical post-treatment processes, such as pyrolysis, seem as the most appropriate method to treat this type of waste in an effective manner. This is because such processes lead to the production of useful chemicals or hydrocarbon oil of high calorific value (i.e. bio-oil in the case of pyrolysis). LCA seems as the most appropriate tool for the process design from an environmental context, however, addressed limitations including initial assumptions, functional unit and system boundaries, as well as lack of regional database and exclusion of socio-economic aspects, may hinder the final decision. This review aims to address the benefits of pyrolysis as a method for PSW treatment and raise the limitations and gaps of conducted research via an environmental standpoint

Non-political anger shifts political preferences towards stronger leaders

Past research has shown that anger is associated with support for confrontational and punitive responses during crises, and notably with the endorsement of authoritarian ideologies. One important question is whether it is anger generated specifically in a political context that explains the association between anger and specific political preferences or whether any feeling of anger would be associated with changes in political attitudes. Here, we tested the effect of non-politically motivated incidental anger on the preference for strong leaders. In line with past research, we predicted that anger would increase preferences for strong leaders. Across two experiments, we exposed participants to an anger induction task. Before and after this experimental manipulation, we measured participants’ political leader preferences by asking them to choose between the faces of two leaders they would vote for in a hypothetical election. The level of self-reported anger predicted the probability of choosing more dominant-looking and less trustworthy-looking leaders after the induction, suggesting that even non-political incidental anger increases preferences for strong leaders

What Can Computational Models Contribute to Neuroimaging Data Analytics?

Over the past years, nonlinear dynamical models have significantly contributed to the general understanding of brain activity as well as brain disorders. Appropriately validated and optimized mathematical models can be used to mechanistically explain properties of brain structure and neuronal dynamics observed from neuroimaging data. A thorough exploration of the model parameter space and hypothesis testing with the methods of nonlinear dynamical systems and statistical physics can assist in classification and prediction of brain states. On the one hand, such a detailed investigation and systematic parameter variation are hardly feasible in experiments and data analysis. On the other hand, the model-based approach can establish a link between empirically discovered phenomena and more abstract concepts of attractors, multistability, bifurcations, synchronization, noise-induced dynamics, etc. Such a mathematical description allows to compare and differentiate brain structure and dynamics in health and disease, such that model parameters and dynamical regimes may serve as additional biomarkers of brain states and behavioral modes. In this perspective paper we first provide very brief overview of the recent progress and some open problems in neuroimaging data analytics with emphasis on the resting state brain activity. We then focus on a few recent contributions of mathematical modeling to our understanding of the brain dynamics and model-based approaches in medicine. Finally, we discuss the question stated in the title. We conclude that incorporating computational models in neuroimaging data analytics as well as in translational medicine could significantly contribute to the progress in these fields

Pseudomonas aeruginosa AES-1 exhibits increased virulence gene expression during chronic infection of cystic fibrosis lung

Pseudomonas aeruginosa, the leading cause of morbidity and mortality in people with cystic fibrosis (CF), adapts for survival in the CF lung through both mutation and gene expression changes. Frequent clonal strains such as the Australian Epidemic Strain-1 (AES-1), have increased ability to establish infection in the CF lung and to superimpose and replace infrequent clonal strains. Little is known about the factors underpinning these properties. Analysis has been hampered by lack of expression array templates containing CF-strain specific genes. We sequenced the genome of an acute infection AES-1 isolate from a CF infant (AES-1R) and constructed a non-redundant micro-array (PANarray) comprising AES-1R and seven other sequenced P. aeruginosa genomes. The unclosed AES-1R genome comprised 6.254Mbp and contained 6957 putative genes, including 338 not found in the other seven genomes. The PANarray contained 12,543 gene probe spots; comprising 12,147 P. aeruginosa gene probes, 326 quality-control probes and 70 probes for non-P. aeruginosa genes, including phage and plant genes. We grew AES-1R and its isogenic pair AES-1M, taken from the same patient 10.5 years later and not eradicated in the intervening period, in our validated artificial sputum medium (ASMDM) and used the PANarray to compare gene expression of both in duplicate. 675 genes were differentially expressed between the isogenic pairs, including upregulation of alginate, biofilm, persistence genes and virulence-related genes such as dihydroorotase, uridylate kinase and cardiolipin synthase, in AES-1M. Non-PAO1 genes upregulated in AES-1M included pathogenesis-related (PAGI-5) genes present in strains PACS2 and PA7, and numerous phage genes. Elucidation of these genes' roles could lead to targeted treatment strategies for chronically infected CF patients. © 2011 Naughton et al