Elementary and high-level functions can be computed in hardware using polynomial approximation techniques. There are many techniques in the literature to calculate the coefficients of such polynomials. Remez algorithm [1] provides the optimal polynomial in the Chebyshev sense that is minimizing the maximum error (minimax approximation). This paper presents an algorithm for truncating the coefficients of the minimax polynomials obtained from Remez algorithm using an algorithmic method. A gain of 3 and 4 bits of accuracy over the direct rounding is reported. Muller [2] addressed the same problem but his algorithm is applicable for the second order polynomials only. This paper presents an algorithm that is applicable for any order.

BACKGROUND: This study applied tissue Doppler imaging and color tissue Doppler imaging to study atrial function changes in patients with hypertrophic cardiomyopathy (HCM). The profile of the segmental atrial velocities and the strain rate were determined and compared with those of normal matched control subjects.

METHODS: This study investigated 20 patients with HCM and 20 age-matched healthy control subjects. In a four-chamber apical view, tissue Doppler imaging was used to measure the lateral left and right atrial (LA and RA) and interatrial septal (IAS) wall systolic, early, and late diastolic velocities. Similarly, the atrial strain rate during ventricular systole (SR(S)) and the early (SR(E)) and late (SR(A)) diastolic phases in patients and control subjects were measured. The interventricular septal tissue Doppler-derived isovolumic relaxation time was calculated.

RESULTS: Only the IAS annular and middle segments showed a significant reduction in the early diastolic velocity (mean, 4.01 +/- 2.2 vs 8.7 +/- 1.1, p = 0.001; 3.23 +/- 2 vs 6.01 +/- 1.9, p = 0.001, respectively) for the patients with HCM in comparison with the control subjects. Generally, the atrial strain rate was clearly reduced. The systolic strain rate (SR(S)) was significantly reduced in the LA wall in the annular (p = 0.007) and middle (p = 0.001) segments and in the IAS middle segment (p = 0.007). Similarly, there was a reduction of the early diastolic strain rate (SR(E)) in the LA annular (p = 0.001) and middle (p = 0.01) segments and in the IAS annular (p = 0.05) and middle (p = 0.001) segments, as well as in the RA annular segment (p = 0.02). The RA middle segments showed insignificant changes.

CONCLUSION: Atrial function may be affected by HCM due to impairment of myocardial diastolic function. Strain rate imaging is reproducible, yields readily obtained parameters that provide unique data about global and longitudinal segmental atrial contraction, and can quantify the atrial dysfunction in patients with HCM.

Numerous endeavors have been made to improve the retrieval procedure in Textual Case-Based Reasoning (TCBR) utilizing clustering and feature selection strategies. SOPHisticated Information Analysis (SOPHIA) approach is one of the most successful efforts which is characterized by its ability to work without the domain of knowledge or language dependency. SOPHIA is based on the conditional probability, which facilitates an advanced Knowledge Discovery (KD) framework for case-based retrieval. SOPHIA attracts clusters by themes which contain only one word in each. However, using one word is not sufficient to construct cluster attractors because the exclusion of the other words associated with that word in the same context could not give a full picture of the theme. The main contribution of this chapter is to introduce an enhanced clustering approach called GloSOPHIA (GloVe SOPHIA) that extends SOPHIA by integrating word embedding technique to enhance KD in TCBR. A new algorithm is proposed to feed SOPHIA with similar terms vector space gained from Global Vector (GloVe) embedding technique. The proposed approach is evaluated on two different language corpora and the results are compared with SOPHIA, K-means, and Self- Organizing Map (SOM) in several evaluation criteria. The results indicate that GloSOPHIA outperforms the other clustering methods in most of the evaluation criteria.

Communication breakdowns in the operating room (OR) have been linked to errors during surgery. Robot-assisted surgery (RAS), a new surgical technology, can lead to new challenges in communication owing to the remote location of the surgeon away from the patient and bedside assistants. Nevertheless, few studies have studied communication strategies during RAS. In this study, 11 robot-assisted radical prostatectomies were recorded and the interaction events between the surgeon and two bedside surgical team members were categorized by modality (verbal/nonverbal), topic, and pair (sender and receiver). Both verbal and nonverbal modalities were used by all pairs. The percentage of nonverbal interactions differed significantly by pair: 66% for the Surgeon-Physician Assistant, 50% for the Physician Assistant-Scrub Nurse, and 25% for the Surgeon-Scrub Nurse, indicating different communication strategies across pairs. In addition, there was a significant dependence between topic and the percentages of verbal and nonverbal events for all pairs. Strategies to improve team communication during RAS should take into account the use of verbal and nonverbal communication means and the variation in interaction strategies based on the topic of communication.