Dissemination of evidence-based standards of care.

Standards of care pertain to crafting and implementing patient-centered treatment interventions. Standards of care must take into consideration the patient's gender, ethnicity, medical and dental history, insurance coverage (or socioeconomic level, if a private patient), and the timeliness of the targeted scientific evidence. This resolves into a process by which clinical decision-making about the optimal patient-centered treatment relies on the best available research evidence, and all other necessary inputs and factors to provide the best possible treatment. Standards of care must be evidence-based, and not merely based on the evidence - the dichotomy being critical in contemporary health services research and practice. Evidence-based standards of care must rest on the best available evidence that emerges from a concerted hypothesis-driven process of research synthesis and meta-analysis. Health information technology needs to become an every-day reality in health services research and practice to ensure evidence-based standards of care. Current trends indicate that user-friendly methodologies, for the dissemination of evidence-based standards of care, must be developed, tested and distributed. They should include approaches for the quantification and analysis of the textual content of systematic reviews and of their summaries in the form of critical reviews and lay-language summaries

Quantum Applications In Political Science

Undergraduate Research ScholarshipThis paper will show the current state of quantum computation and its application as a political science research method. It will look at contemporary empirical literature to assess the current state of the method in both political science and computer science. Then, by assessing the state of quantum computation, this paper will make predictions concerning quantum computation as a research tool and also assess its capability as a catalyst for international diplomacy and discourse. Quantum computation is an emerging technology with increasing scientific attention. This paper will use IBM’s quantum computer, accessed through the cloud, to model and execute quantum algorithms that show the utility for political science research. Furthermore, through the base mathematics of common quantum algorithms, this paper will show how these algorithms can be expanded. This paper finds that quantum computation is a valuable tool with remarkable potential. However, quantum computing has its limitations and currently resides in an important juncture that will decide whether technology involving it will be resigned as a niche theoretical tool or be continued to be developed into a mainstream technology.No embargoAcademic Major: World Politic

Unsupervised word embeddings capture latent knowledge from materials science literature.

The overwhelming majority of scientific knowledge is published as text, which is difficult to analyse by either traditional statistical analysis or modern machine learning methods. By contrast, the main source of machine-interpretable data for the materials research community has come from structured property databases1,2, which encompass only a small fraction of the knowledge present in the research literature. Beyond property values, publications contain valuable knowledge regarding the connections and relationships between data items as interpreted by the authors. To improve the identification and use of this knowledge, several studies have focused on the retrieval of information from scientific literature using supervised natural language processing3-10, which requires large hand-labelled datasets for training. Here we show that materials science knowledge present in the published literature can be efficiently encoded as information-dense word embeddings11-13 (vector representations of words) without human labelling or supervision. Without any explicit insertion of chemical knowledge, these embeddings capture complex materials science concepts such as the underlying structure of the periodic table and structure-property relationships in materials. Furthermore, we demonstrate that an unsupervised method can recommend materials for functional applications several years before their discovery. This suggests that latent knowledge regarding future discoveries is to a large extent embedded in past publications. Our findings highlight the possibility of extracting knowledge and relationships from the massive body of scientific literature in a collective manner, and point towards a generalized approach to the mining of scientific literature

NEIGHBORHOOD-BASED APPROACH OF COLLABORATIVE FILTERING TECHNIQUES FOR BOOK RECOMMENDATION SYSTEM

Recommendation System or Recommender System help the user to predict the "rating" or "preference" a user would give to an item. Recommender systems in general helps the users to find content, products, or services (such as digital products, books, music, movie, TV programs, and web sites) by combining and analyzing suggestions from other users, which mean rating from various people, and users. These recommendation systems use analytic technology to calculate the results that a user is willing to purchase, and the users will receive recommendations to a product of their interest. The aim of the System is to provide a recommendation based on users likes or reviews or ratings. Recommendation system comprises of content based and collaborative based filtering techniques. In this paper, collaborative based filtering has been used to get the expected outcome. The expected outcome has been achieved through collaborative filtering with the help of correlation techniques which in turn comprises of Pearson correlation, cosine similarity, Kendall’ s Tau correlation, Jaccard similarity, Spearman Rank Correlation, Mean-squared distance, etc. This paper tells about which similarity metrics such us Pearson correlation (PC), constrained Pearson correlation (CPC), spearman rank correlation (SRC) which is good in the context of book recommendation system and then applied with neighborhood algorithm

Backwards is the way forward: feedback in the cortical hierarchy predicts the expected future

Clark offers a powerful description of the brain as a prediction machine, which offers progress on two distinct levels. First, on an abstract conceptual level, it provides a unifying framework for perception, action, and cognition (including subdivisions such as attention, expectation, and imagination). Second, hierarchical prediction offers progress on a concrete descriptive level for testing and constraining conceptual elements and mechanisms of predictive coding models (estimation of predictions, prediction errors, and internal models)

Designing smart markets

Electronic markets have been a core topic of information systems (IS) research for last three decades. We focus on a more recent phenomenon: smart markets. This phenomenon is starting to draw considerable interdisciplinary attention from the researchers in computer science, operations research, and economics communities. The objective of this commentary is to identify and outline fruitful research areas where IS researchers can provide valuable contributions. The idea of smart markets revolves around using theoretically supported computational tools to both understand the characteristics of complex trading environments and multiechelon markets and help human decision makers make real-time decisions in these complex environments. We outline the research opportunities for complex trading environments primarily from the perspective o