Sustainability ranking of desalination plants using Mamdani Fuzzy Logic Inference Systems

As water desalination continues to expand globally, desalination plants are continually under pressure to meet the requirements of sustainable development. However, the majority of desalination sustainability research has focused on new desalination projects, with limited research on sustainability performance of existing desalination plants. This is particularly important while considering countries with limited resources for freshwater such as the United Arab Emirates (UAE) as it is heavily reliant on existing desalination infrastructure. In this regard, the current research deals with the sustainability analysis of desalination processes using a generic sustainability ranking framework based on Mamdani Fuzzy Logic Inference Systems. The fuzzy-based models were validated using data from two typical desalination plants in the UAE. The promising results obtained from the fuzzy ranking framework suggest this more in-depth sustainability analysis should be beneficial due to its flexibility and adaptability in meeting the requirements of desalination sustainability

Reducing Intimate Partner Violence through Leveraging Cultural Values

In this brief the results of the "Strengthening What Works: Preventing Intimate Partner Violence in Immigrant and Refugee Communities" (SWW) initiative funded by the Robert Wood Johnson Foundation will be provided. Implications of the results will be suggested as well as recommendations for policy

Equality in Health: An Annotated Bibliography With Resources on Health Disparities and Cultural and Linguistic Competency

Provides citations for articles, reports, books, and online resources on racial/ethnic disparities in health and health care, strategies to reduce them, assessment tools for cultural and linguistic competency, training and education, and other issues

Building professional discourse in emerging markets: Language, context and the challenge of sensemaking

Using ethnographic evidence from the former Soviet republics, this article examines a relatively new and mainly unobserved in the International Business (IB) literature phenomenon of communication disengagement that manifests itself in many emerging markets. We link it to the deficiencies of the local professional business discourse rooted in language limitations reflecting lack of experience with the market economy. This hampers cognitive coherence between foreign and local business entities, adding to the liability of foreignness as certain instances of professional experience fail to find adequate linguistic expression, and complicates cross-cultural adjustments causing multi-national companies (MNCs) financial losses. We contribute to the IB literature by examining cross-border semantic sensemaking through a retrospectively constructed observational study. We argue that a relative inadequacy of the national professional idiom is likely to remain a feature of business environment in post-communist economies for some time and therefore should be factored into business strategies of MNCs. Consequently, we recommend including discursive hazards in the risk evaluation of international projects

Towards Building a Knowledge Base of Monetary Transactions from a News Collection

We address the problem of extracting structured representations of economic events from a large corpus of news articles, using a combination of natural language processing and machine learning techniques. The developed techniques allow for semi-automatic population of a financial knowledge base, which, in turn, may be used to support a range of data mining and exploration tasks. The key challenge we face in this domain is that the same event is often reported multiple times, with varying correctness of details. We address this challenge by first collecting all information pertinent to a given event from the entire corpus, then considering all possible representations of the event, and finally, using a supervised learning method, to rank these representations by the associated confidence scores. A main innovative element of our approach is that it jointly extracts and stores all attributes of the event as a single representation (quintuple). Using a purpose-built test set we demonstrate that our supervised learning approach can achieve 25% improvement in F1-score over baseline methods that consider the earliest, the latest or the most frequent reporting of the event.Comment: Proceedings of the 17th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL '17), 201