Implementing the Objective Structured Clinical Examination in a Geriatrics Fellowship Program-A 3-Year Experience

The Objective Structured Clinical Examination (OSCE) appears to be an effective alternative for assessing not only medical knowledge, but also clinical skills, including effective communication and physical examination skills. The purpose of the current study was to implement an OSCE model in a geriatrics fellowship program and to compare the instrument with traditional essay examination. Seventy first- and second-year geriatric fellows were initially submitted to a traditional essay examination and scored from 0 to 10 by a faculty member. The same fellows subsequently underwent an OSCE with eight 10-minute stations covering a wide range of essential aspects of geriatric knowledge. Each OSCE station had an examiner responsible for its evaluation according to a predefined checklist. Checklist items were classified for analysis purposes as clinical knowledge items (CKI) and communication skills items (CSI); fellow responses were scored from 0 to 10.Although essay examinations took from 30 to 45 minutes to complete, 180200 minutes were required to evaluate fellows using the proposed OSCE method. Fellows scored an average of 6.2 +/- 1.2 on the traditional essay examination and 6.6 +/- 1.0 on the OSCE (P < .001). Subanalyses of OSCE scores indicated that average performance on CKI was lower than the average on CSI (6.4 +/- 1.1 vs. 8.4 +/- 1.1; P < .001). Fellow performance on the essay examination was similar to their performance on CKI (P = .13). Second-year fellows performed better than first-year fellows on the essay examination (P < .001) and CKI (P = .05), but not on CSI (P = .25).The OSCE was successfully implemented as an educational strategy during a geriatrics fellowship program. Combining different testing modalities may provide the best assessment of competence for various domains of knowledge, skills, and behavior.

Video Quality Assessments on Digital TV and Video Streaming Services Using Objective Metrics

There is a wide range of telecommunications services that transmit voice, video and data through complex transmission networks and in some cases, the service has not an acceptable quality level for the end user. In this sense the study of methods for assessing video quality and voice have a very important role. This paper presents a classification scheme, based on different criteria, of the methods and metrics that are being studied in recent years. This paper presents how the video quality is affected by degradation in the transmission channel in two kinds of services: Digital TV (ISDB-TB) due the fading in the air interface and video streaming service on an IP network due packet loss. For Digital TV tests was set up a scenario where the digital TV transmitter is connected to an RF channel emulator, where are inserted different fading models and at the end, the videos are saved in a mobile device. The tests of streaming video were performed in an isolated scenario of IP network, which are scheduled several network conditions, resulting in different qualities of video reception. The video quality assessment is performed using objective assessment methods: PSNR, SSIM and VQM. The results show how the losses in the transmission channel affects the quality of end-user experience on both services studied.

Tricritical-like behavior of the nonlinear optical refraction at the nematic-isotropic transition in the E7 thermotropic liquid crystal

We use Z-scan technique to investigate the nonlinear optical response of the thermotropic liquid crystal E7 in the neighborhood of the nematic-isotropic phase transition. The analysis of the data for the nonlinear optical birefringence is compatible with an effective critical exponent of the order parameter, beta = 0.28 +/- 0.03, which is close to the classical value, beta = 0.25, for a tricritical point. The nonlinear optical absorption in the nematic range depends on the geometrical configuration of the nematic director with respect to the polarization beam, and vanishes in the isotropic phase.

A multi-objective adaptive immune algorithm for multi-application NoC mapping

Current SoC design trends are characterized by the integration of larger amount of IPs targeting a wide range of application fields. Such multi-application systems are constrained by a set of requirements. In such scenario network-on-chips (NoC) are becoming more important as the on-chip communication structure. Designing an optimal NoC for satisfying the requirements of each individual application requires the specification of a large set of configuration parameters leading to a wide solution space. It has been shown that IP mapping is one of the most critical parameters in NoC design, strongly influencing the SoC performance. IP mapping has been solved for single application systems using single and multi-objective optimization algorithms. In this paper we propose the use of a multi-objective adaptive immune algorithm (M(2)AIA), an evolutionary approach to solve the multi-application NoC mapping problem. Latency and power consumption were adopted as the target multi-objective functions. To compare the efficiency of our approach, our results are compared with those of the genetic and branch and bound multi-objective mapping algorithms. We tested 11 well-known benchmarks, including random and real applications, and combines up to 8 applications at the same SoC. The experimental results showed that the M(2)AIA decreases in average the power consumption and the latency 27.3 and 42.1 % compared to the branch and bound approach and 29.3 and 36.1 % over the genetic approach.

Objective reduction in adenoid tissue after mometasone furoate treatment

Objective: Chronic rhinitis and adenoid hypertrophy are the main causes of nasal obstruction in children and proper treatment of these factors seem essential for controlling nasal obstructive symptoms. This study aims to evaluate the effects of topical mometasone treatment on symptoms and size of adenoid tissue in children with complaints of nasal obstruction and to compare this approach to continuous nasal saline douching plus environmental control alone. Methods: Fifty-one children with nasal obstructive complaints were submitted to a semi-structured clinical questionnaire on nasal symptoms, prick test and nasoendoscopy. Nasoendoscopic images were digitalized, and both adenoid and nasopharyngeal areas were measured in pixels. The relation adenoid/nasopharyngeal area was calculated. Patients were subsequently re-evaluated in two different periods: following 40 days of treatment with nasal douching and environmental prophylaxis alone; and after an subsequent 40 day-period, when topical mometasone furoate (total dose: 100 mu g/day) was superposed. Results: Nasal symptoms and snoring significantly improved after nasal douching, and an additional gain was observed when mometasone furoate was included to treatment. Saline douching did not influence the adenoid area, whereas a significant reduction on adenoid tonsil was observed after 40 days of mometasone treatment (P < 0.0001). Conclusion: Nasal saline douching significantly improved nasal symptoms without interfering in adenoid dimension. In contrast, mometasone furoate significantly reduced adenoid tissue, and led to a supplementary improvement of nasal symptoms. (C) 2012 Elsevier Ireland Ltd. All rights reserved.

A new method for decision making in multi-objective optimization problems

Many engineering sectors are challenged by multi-objective optimization problems. Even if the idea behind these problems is simple and well established, the implementation of any procedure to solve them is not a trivial task. The use of evolutionary algorithms to find candidate solutions is widespread. Usually they supply a discrete picture of the non-dominated solutions, a Pareto set. Although it is very interesting to know the non-dominated solutions, an additional criterion is needed to select one solution to be deployed. To better support the design process, this paper presents a new method of solving non-linear multi-objective optimization problems by adding a control function that will guide the optimization process over the Pareto set that does not need to be found explicitly. The proposed methodology differs from the classical methods that combine the objective functions in a single scale, and is based on a unique run of non-linear single-objective optimizers.

Evolving decision trees with beam search-based initialization and lexicographic multi-objective evaluation

Decision tree induction algorithms represent one of the most popular techniques for dealing with classification problems. However, traditional decision-tree induction algorithms implement a greedy approach for node splitting that is inherently susceptible to local optima convergence. Evolutionary algorithms can avoid the problems associated with a greedy search and have been successfully employed to the induction of decision trees. Previously, we proposed a lexicographic multi-objective genetic algorithm for decision-tree induction, named LEGAL-Tree. In this work, we propose extending this approach substantially, particularly w.r.t. two important evolutionary aspects: the initialization of the population and the fitness function. We carry out a comprehensive set of experiments to validate our extended algorithm. The experimental results suggest that it is able to outperform both traditional algorithms for decision-tree induction and another evolutionary algorithm in a variety of application domains.

Multi-objective evolutionary algorithm for single and multiple fault service restoration in large-scale distribution systems

Network reconfiguration for service restoration (SR) in distribution systems is a complex optimization problem. For large-scale distribution systems, it is computationally hard to find adequate SR plans in real time since the problem is combinatorial and non-linear, involving several constraints and objectives. Two Multi-Objective Evolutionary Algorithms that use Node-Depth Encoding (NDE) have proved able to efficiently generate adequate SR plans for large distribution systems: (i) one of them is the hybridization of the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) with NDE, named NSGA-N; (ii) the other is a Multi-Objective Evolutionary Algorithm based on subpopulation tables that uses NDE, named MEAN. Further challenges are faced now, i.e. the design of SR plans for larger systems as good as those for relatively smaller ones and for multiple faults as good as those for one fault (single fault). In order to tackle both challenges, this paper proposes a method that results from the combination of NSGA-N, MEAN and a new heuristic. Such a heuristic focuses on the application of NDE operators to alarming network zones according to technical constraints. The method generates similar quality SR plans in distribution systems of significantly different sizes (from 3860 to 30,880 buses). Moreover, the number of switching operations required to implement the SR plans generated by the proposed method increases in a moderate way with the number of faults.