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The “reverse engineering” approach to modelling is applied to T-cell vaccination. The novelty here is the representation of the diseased state as a transient.

Lifetime-standing psychosocial effects of congenital hand anomalies are inevitable in patients who have not received a comprehensive treatment with appropriate timing and approach. Herein, two adult cases of untreated thumb polydactyly are presented. Both of them had hands with striking appearance and late consequent psychosocial problems.

To generate a proper Korean predicate, a natural modal expression is the most important factor for a machine translation (MT) system. Tense, aspect, mood, negation, and voice are the major constituents related to modal expression. The linguistic encoding of a modal expression is quite different between Chinese and Korean in terms of linguistic typology and genealogy. In this paper, a new applicable categorization of Korean modality system viz. tense, aspect, mood, negation, and voice, will be proposed through a contrastive analysis of Chinese and Korean from the viewpoint of a practical MT system. In order to precisely determine the modal expression, effective feature selection frameworks for Chinese are presented with a variety of machine learning methods. As a result, our proposed approach achieved an accuracy of 83.10%.

The sentiment analysis relying on the aspect of online reviews is utilized for identifying the polarity of the given review. Nowadays, many methods are introduced for aspect-based sentiment analysis (ABSA) using neural networks, and many methods failed to consider contextual information exploitation to make the performance more accurate. Hence, this research proposed an optimized deep learning method for the detection of the aspect and to identify the polarity. Hence, in this research, an optimized deep learning technique for the ABSA is introduced by considering the online reviews, in which the deep learning classifiers are trained with the proposed Fabricius ringlet optimization (FRO) algorithm to reduce the loss that helps to enhance the accuracy of sentiment polarity prediction. The proposed FRO is developed by the hybridization of the behavioral nature of the Fabricius and the ringlet in feeding for the determination of the global best solution. The tuning of the weights and biases of the classifier enhance the performance of the classifier. The objective behind the tuning is to minimize the loss function while training and to enhance the accuracy of aspect extraction and polarity prediction of sentiment. Based on a study of the existing approach, the suggested FRO-based hybrid deep learning method is significantly improved; its accuracy, sensitivity, and specificity are 87.06%, 90.83%, and 79.37%, respectively, with a training percentage of 40%. The accuracy, sensitivity, and specificity of the existing technique have also been enhanced for aspect restaurant values, which are 87.53%, 96.06%, and 79.88% with a 60% training percentage. Similar to that, Twitter values for accuracy, sensitivity, and specificity are reported to be 89.08%, 99.35%, and 79.70%, respectively, with an 80% training percentage. The proposed method obtained the 90.13%, 99.35%, and 81.10% accuracy, sensitivity, and specificity from the assessment of the FRO-based hybrid deep learning.

Component-based software development approach is one of the most promising solutions for the emerging high development cost, low productivity, unmanageable software quality and high risk. This approach, however, encounters some problems about unseparated crosscutting concerns that are easy to lead to the code-tangling and code-scattering. Aspect-oriented programming enables the handling of crosscutting concerns and implements the separation of concerns. It will help to understand the software system better and strengthen the adaptability, maintainability and reusability of the final software. This paper works on the combination between CBSD and AOP. The combinatorial semantic constraint is described in theory, and then proposes a framework and sets up its supported environment. Taken book management system as an example, the framework is proved to be an effective way on the road of the combination between CBSD and AOP.

The influence of piezoelectric phase volume fraction and composite shape (height/width) on resonance of 1-3 and 1-3-2 type piezoelectric composites which applied on low frequency band has been investigated. 1-3 and 1-3-2 composite samples were fabricated by dice-fill technology. The performance of two type composites with volume fraction of ceramic was analyzed and compared. In sum, 1-3-2 composite was the same to 1-3 composite on performance when ceramic bottom volume fraction of 1-3-2 composite was less then 30%. In addition, height/width ratio of composite was altered by adjusting the height of samples. Then, the resonant frequency and electromechanical coupling coefficient of composites were measured. The result showed that the resonant frequency has been affected by the variety of geometries of samples and volume fraction of ceramic.