Examining green production and its role within the competitive strategy of manufacturers

Purpose: This paper reviews current literature and contributes a set of findings that capture the current state-of-the-art of the topic of green production. Design/methodology/approach: A literature review to capture, classify and summarize the main body of knowledge on green production and, translate this into a form that is readily accessible to researchers and practitioners in the more mainstream operations management community. Findings: The existing knowledge base is somewhat fragmented. This is a relatively unexplored topic within mainstream operations management research and one which could provide rich opportunities for further exploration. Originality/value: This paper sets out to review current literature, from a more conventional production operations perspective, and contributes a set of findings that capture the current state-of-the-art of this topic

A Recipe for State-and-Effect Triangles

In the semantics of programming languages one can view programs as state transformers, or as predicate transformers. Recently the author has introduced state-and-effect triangles which capture this situation categorically, involving an adjunction between state- and predicate-transformers. The current paper exploits a classical result in category theory, part of Jon Beck's monadicity theorem, to systematically construct such a state-and-effect triangle from an adjunction. The power of this construction is illustrated in many examples, covering many monads occurring in program semantics, including (probabilistic) power domains

Learning semantic sentence representations from visually grounded language without lexical knowledge

Current approaches to learning semantic representations of sentences often use prior word-level knowledge. The current study aims to leverage visual information in order to capture sentence level semantics without the need for word embeddings. We use a multimodal sentence encoder trained on a corpus of images with matching text captions to produce visually grounded sentence embeddings. Deep Neural Networks are trained to map the two modalities to a common embedding space such that for an image the corresponding caption can be retrieved and vice versa. We show that our model achieves results comparable to the current state-of-the-art on two popular image-caption retrieval benchmark data sets: MSCOCO and Flickr8k. We evaluate the semantic content of the resulting sentence embeddings using the data from the Semantic Textual Similarity benchmark task and show that the multimodal embeddings correlate well with human semantic similarity judgements. The system achieves state-of-the-art results on several of these benchmarks, which shows that a system trained solely on multimodal data, without assuming any word representations, is able to capture sentence level semantics. Importantly, this result shows that we do not need prior knowledge of lexical level semantics in order to model sentence level semantics. These findings demonstrate the importance of visual information in semantics

Final state interaction effects in mu-capture induced two-body decay of 3He

The mu-capture process on 3He leading to a neutron, a deuteron and a mu-neutrino in the final state is studied. Three-nucleon Faddeev wave functions for the initial 3He bound and the final neutron-deuteron scattering states are calculated using the BonnB and Paris nucleon-nucleon potentials. The nuclear weak current operator is restricted to impulse approximation. Large effects on the decay rates of the final state interaction are found. The comparison to recent experimental data shows that the inclusion of final state interactions drastically improves the description of the data.Comment: 14 pages, 6 eps figure