Similarity Measure Development for Case-Based Reasoning- A Data-driven Approach

In this paper, we demonstrate a data-driven methodology for modelling the local similarity measures of various attributes in a dataset. We analyse the spread in the numerical attributes and estimate their distribution using polynomial function to showcase an approach for deriving strong initial value ranges of numerical attributes and use a non-overlapping distribution for categorical attributes such that the entire similarity range [0,1] is utilized. We use an open source dataset for demonstrating modelling and development of the similarity measures and will present a case-based reasoning (CBR) system that can be used to search for the most relevant similar cases

Monolithic Photoelectrochemical Device for Direct Water Splitting with 19% Efficiency

Recent rapid progress in efficiencies for solar water splitting by photoelectrochemical devices has enhanced its prospects to enable storable renewable energy. Efficient solar fuel generators all use tandem photoelectrode structures, and advanced integrated devices incorporate corrosion protection layers as well as heterogeneous catalysts. Realization of near thermodynamic limiting performance requires tailoring the energy band structure of the photoelectrode and also the optical and electronic properties of the surface layers exposed to the electrolyte. Here, we report a monolithic device architecture that exhibits reduced surface reflectivity in conjunction with metallic Rh nanoparticle catalyst layers that minimize parasitic light absorption. Additionally, the anatase TiO2 protection layer on the photocathode creates a favorable internal band alignment for hydrogen evolution. An initial solar-to-hydrogen efficiency of 19.3 % is obtained in acidic electrolyte and an efficiency of 18.5 % is achieved at neutral pH condition (under simulated sunlight)

Global Monthly Water Scarcity: Blue Water Footprints versus Blue Water Availability

Freshwater scarcity is a growing concern, placing considerable importance on the accuracy of indicators used to characterize and map water scarcity worldwide. We improve upon past efforts by using estimates of blue water footprints (consumptive use of ground- and surface water flows) rather than water withdrawals, accounting for the flows needed to sustain critical ecological functions and by considering monthly rather than annual values. We analyzed 405 river basins for the period 1996–2005. In 201 basins with 2.67 billion inhabitants there was severe water scarcity during at least one month of the year. The ecological and economic consequences of increasing degrees of water scarcity – as evidenced by the Rio Grande (Rio Bravo), Indus, and Murray-Darling River Basins – can include complete desiccation during dry seasons, decimation of aquatic biodiversity, and substantial economic disruption

Interpretation of Best Medical Coding Practices by Case-Based Reasoning - A User Assistance Prototype for Data Collection for Cancer Registries

International audienceIn the fight against cancer, cancer registries are an important tool. At the heart of these registries is the data collection and coding process. This process is ruled by complex international standards and numerous best practices, which can easily overwhelm (coding) operators. In this paper, a system assisting operators in the interpretation of best medical coding practices and a short evaluation are presented. By leveraging the arguments used by the coding experts to determine the best coding option, the proposed system answers coding questions from operators and provides a partial explanation for the proposed solution

A Case-base Approach to Workforces’ Satisfaction Assessment

It is well known that human resources play a valuable role in a sustainable organizational development. Indeed, this work will focus on the development of a decision support system to assess workers’ satisfaction based on factors related to human resources management practices. The framework is built on top of a Logic Programming approach to Knowledge Representation and Reasoning, complemented with a Case Based approach to computing. The proposed solution is unique in itself, once it caters for the explicit treatment of incomplete, unknown, or even self-contradictory information, either in terms of a qualitative or quantitative setting. Furthermore, clustering methods based on similarity analysis among cases were used to distinguish and aggregate collections of historical data or knowledge in order to reduce the search space, therefore enhancing the cases retrieval and the overall computational process

Mechanisms of Esophago-Pharyngeal Acid Regurgitation in Human Subjects

Esophago-pharyngeal regurgitation is implicated in various otolaryngologic and respiratory disorders. The pathophysiological mechanisms causing regurgitation are still largely unknown

Case-Based Interpretation of Best Medical Coding Practices — Application to Data Collection for Cancer Registries

International audienceCancer registries are important tools in the fight against cancer. At the heart of these registries is the data collection and coding process. Ruled by complex international standards and numerous best practices, operators are easily overwhelmed. In this paper, a system is presented to assist operators in the interpretation of best medical coding practices. By leveraging the arguments used by the coding experts to determine the best coding option, the proposed system is designed to answer the coding questions from operators and provide an answer associated with a partial explanation for the proposed solution

The D-score: a metric for interpreting the early development of infants and toddlers across global settings

Introduction: Early childhood development can be described by an underlying latent construct. Global comparisons of children’s development are hindered by the lack of a validated metric that is comparable across cultures and contexts, especially for children under age 3 years. We constructed and validated a new metric, the Developmental Score (D-score), using existing data from 16 longitudinal studies. / Methods: Studies had item-level developmental assessment data for children 0–48 months and longitudinal outcomes at ages >4–18 years, including measures of IQ and receptive vocabulary. Existing data from 11 low-income, middle-income and high-income countries were merged for >36 000 children. Item mapping produced 95 ‘equate groups’ of same-skill items across 12 different assessment instruments. A statistical model was built using the Rasch model with item difficulties constrained to be equal in a subset of equate groups, linking instruments to a common scale, the D-score, a continuous metric with interval-scale properties. D-score-for-age z-scores (DAZ) were evaluated for discriminant, concurrent and predictive validity to outcomes in middle childhood to adolescence. / Results: Concurrent validity of DAZ with original instruments was strong (average r=0.71), with few exceptions. In approximately 70% of data rounds collected across studies, DAZ discriminated between children above/below cut-points for low birth weight (<2500 g) and stunting (−2 SD below median height-for-age). DAZ increased significantly with maternal education in 55% of data rounds. Predictive correlations of DAZ with outcomes obtained 2–16 years later were generally between 0.20 and 0.40. Correlations equalled or exceeded those obtained with original instruments despite using an average of 55% fewer items to estimate the D-score. / Conclusion: The D-score metric enables quantitative comparisons of early childhood development across ages and sets the stage for creating simple, low-cost, global-use instruments to facilitate valid cross-national comparisons of early childhood development

Learning Tversky Similarity

In this paper, we advocate Tversky's ratio model as an appropriate basis for computational approaches to semantic similarity, that is, the comparison of objects such as images in a semantically meaningful way. We consider the problem of learning Tversky similarity measures from suitable training data indicating whether two objects tend to be similar or dissimilar. Experimentally, we evaluate our approach to similarity learning on two image datasets, showing that is performs very well compared to existing methods