Building institutional capacity for industrial symbiosis development : a case study of an industrial symbiosis coordination network in China

Recent research has examined how the concept of institutional capacity relates to the ability of organisations to deliver industrial symbiosis, and in particular how that ability itself can develop over time. One approach to developing industrial symbiosis has been to build a network of local bodies to work together to this end. Terming such a body an industrial symbiosis coordination network, this study innovatively applies institutional capacity building theory in the context of a Chinese eco-industrial park. It examines how the coordination network developed the expertise to encourage local companies to engage in industrial symbiosis. This research consisted of a qualitative study, including participant observation, semi-structured interviews and document analysis to analyse the development of an industrial symbiosis coordination network in Tianjin Binhai New Area. It is found that the network increased institutional capacity for local IS development by promoting relational links across organisational divisions and governance levels, and by increasing various types of knowledge for coordinating IS. The concept of institutional capacity building is shown to have cross-cultural applicability. Reflections on this study indicate that local government can play a vital role in building and maintaining an IS coordination network in the Chinese context, but that other bodies are also needed to mobilise institutional capacity for IS development

Potentially Polluting Marine Sites GeoDB: An S-100 Geospatial Database as an Effective Contribution to the Protection of the Marine Environment

Potentially Polluting Marine Sites (PPMS) are objects on, or areas of, the seabed that may release pollution in the future. A rationale for, and design of, a geospatial database to inventory and manipu-late PPMS is presented. Built as an S-100 Product Specification, it is specified through human-readable UML diagrams and implemented through machine-readable GML files, and includes auxiliary information such as pollution-control resources and potentially vulnerable sites in order to support analyses of the core data. The design and some aspects of implementation are presented, along with metadata requirements and structure, and a perspective on potential uses of the database

An Expressive Language and Efficient Execution System for Software Agents

Software agents can be used to automate many of the tedious, time-consuming information processing tasks that humans currently have to complete manually. However, to do so, agent plans must be capable of representing the myriad of actions and control flows required to perform those tasks. In addition, since these tasks can require integrating multiple sources of remote information ? typically, a slow, I/O-bound process ? it is desirable to make execution as efficient as possible. To address both of these needs, we present a flexible software agent plan language and a highly parallel execution system that enable the efficient execution of expressive agent plans. The plan language allows complex tasks to be more easily expressed by providing a variety of operators for flexibly processing the data as well as supporting subplans (for modularity) and recursion (for indeterminate looping). The executor is based on a streaming dataflow model of execution to maximize the amount of operator and data parallelism possible at runtime. We have implemented both the language and executor in a system called THESEUS. Our results from testing THESEUS show that streaming dataflow execution can yield significant speedups over both traditional serial (von Neumann) as well as non-streaming dataflow-style execution that existing software and robot agent execution systems currently support. In addition, we show how plans written in the language we present can represent certain types of subtasks that cannot be accomplished using the languages supported by network query engines. Finally, we demonstrate that the increased expressivity of our plan language does not hamper performance; specifically, we show how data can be integrated from multiple remote sources just as efficiently using our architecture as is possible with a state-of-the-art streaming-dataflow network query engine

From Supply Chains to Total Product Systems

The evolution of supply chain management and practice has had an integral and expanding role in contemporary global economic and socio-political change over the past 25 years or so. Thi srole is moving closer to centre stage with the emergence of business models equating to 'total product systems'. The impacts of advanced supply chain practice include driving fundamental changes in approach to product design, the concept of 'product', production methods, distribution, marketing, aftermarket support and end-of-life (EOL) reprocessing. Viewed in their full context, methods in supply chain management (SCM) have major influences on societal functioning and on economic development at global, national and local levels. Even the supply chains for simple products can involve several different industries and link many companies, large and small. Those for complex products may span several technological domains and economic sectors, linking hundreds or sometimes thousands of companies

Learning and Interpreting Multi-Multi-Instance Learning Networks

We introduce an extension of the multi-instance learning problem where examples are organized as nested bags of instances (e.g., a document could be represented as a bag of sentences, which in turn are bags of words). This framework can be useful in various scenarios, such as text and image classification, but also supervised learning over graphs. As a further advantage, multi-multi instance learning enables a particular way of interpreting predictions and the decision function. Our approach is based on a special neural network layer, called bag-layer, whose units aggregate bags of inputs of arbitrary size. We prove theoretically that the associated class of functions contains all Boolean functions over sets of sets of instances and we provide empirical evidence that functions of this kind can be actually learned on semi-synthetic datasets. We finally present experiments on text classification, on citation graphs, and social graph data, which show that our model obtains competitive results with respect to accuracy when compared to other approaches such as convolutional networks on graphs, while at the same time it supports a general approach to interpret the learnt model, as well as explain individual predictions.Comment: JML

Vectorwise: Beyond Column Stores

textabstractThis paper tells the story of Vectorwise, a high-performance analytical database system, from multiple perspectives: its history from academic project to commercial product, the evolution of its technical architecture, customer reactions to the product and its future research and development roadmap. One take-away from this story is that the novelty in Vectorwise is much more than just column-storage: it boasts many query processing innovations in its vectorized execution model, and an adaptive mixed row/column data storage model with indexing support tailored to analytical workloads. Another one is that there is a long road from research prototype to commercial product, though database research continues to achieve a strong innovative influence on product development

A Conceptual Framework of Reverse Logistics Impact on Firm Performance

This study aims to examine the reverse logistics factors that impact upon firm performance. We review reverse logistics factors under three research streams: (a) resource-based view of the firm, including: Firm strategy, Operations management, and Customer loyalty (b) relational theory, including: Supply chain efficiency, Supply chain collaboration, and institutional theory, including: Government support and Cultural alignment. We measured firm performance with 5 measures: profitability, cost, innovativeness, perceived competitive advantage, and perceived customer satisfaction. We discuss implications for research, policy and practice