The Standard Model a la Connes-Lott

The relations among coupling constants and masses in the standard model \`a la Connes-Lott with general scalar product are computed in detail. We find a relation between the top and the Higgs masses. For $m_t=174\pm22\ GeV$ it yields $m_H=277\pm40\ GeV$. The Connes-Lott theory privileges the masses $m_t=160.4\ GeV$ and $m_H=251.8\ GeV$.Comment: 20 pages, LaTe

Connes' interpretation of the Standard Model and massive neutrinos

Massive neutrinos can be accommodated into the noncommutative geometry reinterpretation of the Standard Model. The constrained Standard Model Lagrangian is computed anew under the assumption of nonzero neutrino masses. This gives the ``prediction" of a mass for the Higgs particle somewhat higher than in the vanishing neutrino mass case.Comment: Final version, to appear in Phys Lett

Inner fluctuations of the spectral action

We prove in the general framework of noncommutative geometry that the inner fluctuations of the spectral action can be computed as residues and give exactly the counterterms for the Feynman graphs with fermionic internal lines. We show that for geometries of dimension less or equal to four the obtained terms add up to a sum of a Yang-Mills action with a Chern-Simons action.Comment: 18 pages, 4 figures Equation 1.6 correcte

A generalized Lichnerowicz formula, the Wodzicki Residue and Gravity

We prove a generalized version of the well-known Lichnerowicz formula for the square of the most general Dirac operator $\widetilde{D}$\ on an even-dimensional spin manifold associated to a metric connection $\widetilde{\nabla}$. We use this formula to compute the subleading term $\Phi_1(x,x, \widetilde{D}^2)$\ of the heat-kernel expansion of $\widetilde{D}^2$. The trace of this term plays a key-r\hat {\petit\rm o}le in the definition of a (euclidian) gravity action in the context of non-commutative geometry. We show that this gravity action can be interpreted as defining a modified euclidian Einstein-Cartan theory.Comment: 10 pages, plain te

The Standard Model within Non-associative Geometry

We present the construction of the standard model within the framework of non--associative geometry. For the simplest scalar product we get the tree--level predictions $m_W=\frac{1}{2} m_t\,,$ $m_H=\frac{3}{2} m_t$ and $\sin^2 \theta_W= \frac{3}{8}.$ These relations differ slightly from predictions derived in non--commutative geometry.Comment: 9 pages. LaTeX 2e, styles: amsart, a4, array, eqnarray, bbm, mathrsfs, cit

Standardized Critical Thinking Tests as a Predictor of Success in Nursing Programs

High attrition rates and a nursing shortage across the nation have led schools of nursing to seek out ways to better identify which applicants will be most successful in graduating from the nursing program and passing the National Council Licensure Examination for Registered Nurses (NCLEX-RN). Nursing programs have historically included standardized entrance exam scores and prerequisite scores among their admission criteria but have not used standardized critical thinking assessments (CTA), even though critical thinking is an integral part of being a successful nursing professional. Using Astin\u27s input-environment-output (I-E-O) model, the purpose of this retrospective correlational study was to determine whether a significant relationship exists between prerequisite grade point average (GPA), Test for Essential Academic Skills (TEAS) composite scores, entrance and exit CTA scores, and nursing GPA and the outcome of interest, passing the NCLEX-RN exam. Archival data for 64 students enrolled in a baccalaureate degree program at a Texas university were analyzed using binary logistic regression. A significant positive relationship was found between prerequisite GPA, TEAS composite scores, entrance and exit CTA scores, and nursing GPA, and the outcome of interest, passing the NCLEX-RN exam. However, in looking at each independent variable separately, no significant relationship was revealed between the individual scores of the prerequisite GPA, TEAS composite, entrance and exit critical thinking assessment, nursing GPA, and the outcome of passing the NCLEX-RN exam on the first attempt. These findings have implications for positive social change by illuminating the complexities of nursing program retention and graduation and informing efforts to train the most talented nurses

Gravity, Non-Commutative Geometry and the Wodzicki Residue

We derive an action for gravity in the framework of non-commutative geometry by using the Wodzicki residue. We prove that for a Dirac operator $D$ on an $n$ dimensional compact Riemannian manifold with $n\geq 4$, $n$ even, the Wodzicki residue Res$(D^{-n+2})$ is the integral of the second coefficient of the heat kernel expansion of $D^{2}$. We use this result to derive a gravity action for commutative geometry which is the usual Einstein Hilbert action and we also apply our results to a non-commutative extension which, is given by the tensor product of the algebra of smooth functions on a manifold and a finite dimensional matrix algebra. In this case we obtain gravity with a cosmological constant.Comment: 17p., MZ-TH/93-3

Fuzzy Mass Relations in the Standard Model

Recently Connes has proposed a new geometric version of the standard model including a non-commutative charge conjugation. We present a systematic analysis of the relations among masses and coupling constants in this approach. In particular, for a given top mass, the Higgs mass is constrained to lie in an interval. Therefore this constraint is locally stable under renormalization flow.Comment: 14 pages LaTeX, one figure postscrip

A Linked Open Data Approach for Sentiment Lexicon Adaptation

Social media platforms have recently become a gold mine for organisations to monitor their reputation by extracting and analysing the sentiment of the posts generated about them, their markets, and competitors. Among the approaches to analyse sentiment from social media, approaches based on sentiment lexicons (sets of words with associated sentiment scores) have gained popularity since they do not rely on training data, as opposed to Machine Learning approaches. However, sentiment lexicons consider a static sentiment score for each word without taking into consideration the different contexts in which the word is used (e.g, great problem vs. great smile). Additionally, new words constantly emerge from dynamic and rapidly changing social media environments that may not be covered by the lexicons. In this paper we propose a lexicon adaptation approach that makes use of semantic relations extracted from DBpedia to better understand the various contextual scenarios in which words are used. We evaluate our approach on three different Twitter datasets and show that using semantic information to adapt the lexicon improves sentiment computation by 3.7% in average accuracy, and by 2.6% in average F1 measure