Deep learning and support vector machines for transcription start site identification.

Recognizing transcription start sites is key to gene identification. Several approaches have been employed in related problems such as detecting translation initiation sites or promoters, many of the most recent ones based on machine learning. Deep learning methods have been proven to be exceptionally effective for this task, but their use in transcription start site identification has not yet been explored in depth.

Nonlinear physics opens a new paradigm for accurate transcription start site prediction.

There is evidence that DNA breathing (spontaneous opening of the DNA strands) plays a relevant role in the interactions of DNA with other molecules, and in particular in the transcription process. Therefore, having physical models that can predict these openings is of interest. However, this source of information has not been used before either in transcription start sites (TSSs) or promoter prediction.

Early and extremely early multi-label fault diagnosis in induction motors.

The detection of faulty machinery and its automated diagnosis is an industrial priority because efficient fault diagnosis implies efficient management of the maintenance times, reduction of energy consumption, reduction in overall costs and, most importantly, the availability of the machinery is ensured. Thus, this paper presents a new intelligent multi-fault diagnosis method based on multiple sensor information for assessing the occurrence of single, combined, and simultaneous faulty conditions in an induction motor. The contribution and novelty of the proposed method include the consideration of different physical magnitudes such as vibrations, stator currents, voltages, and rotational speed as a meaningful source of information of the machine condition.

Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools.

The analysis of functional abilities and their development in early childhood (0-6 years old) are fundamental aspects among young children with certain types of developmental difficulties that can facilitate prevention, through programmed interventions adapted to the needs of each user (student or patient). There are, however, few investigations to date, that have analyzed the use of automated tools for recording and interpreting the results of the initial assessment. Here, a protocol is presented to examine the functional abilities in early childhood in young children, aged between 3-6 years old, with intellectual disabilities, but the protocol can also be used for ages 0 to 6 years.

Observation of Metacognitive Skills in Natural Environments: A Longitudinal Study With Mixed Methods.

Recent studies pointing to evaluation methods in natural environments suggest that their use in the analysis of metacognitive skills provides more precise information than the use of off-line evaluation methods. In this research, mixed methods are used over one academic year for the evaluation of the metacognitive skills that students of Secondary Education apply to solve physics problems. The objectives of this study are to analyze the use of metacognitive skills in natural environments and to study behavioral patterns of student learning through a longitudinal study.

Differential efficacy of the resources used in B-learning environments.

Background: Learning is increasingly frequent in B-Learning spaces. It is therefore necessary to study the characteristics that guarantee deeper and more successful learning in these learning environments.

Method: We work with sample of 233 university students using the Moodle 3.

Does the Use of Learning Management Systems With Hypermedia Mean Improved Student Learning Outcomes?

Learning management systems (LMSs) that incorporate hypermedia Smart Tutoring Systems and personalized student feedback can increase self-regulated learning (SRL), motivation, and effective learning. These systems are studied with the following aims: (1) to verify whether the use of LMS with hypermedia Smart Tutoring Systems improves student learning outcomes; (2) to verify whether the learning outcomes will be grouped into performance clusters (Satisfactory, Good, and Excellent); and (3) to verify whether those clusters will group together the different learning outcomes assessed in four different evaluation procedures. Use of the LMS with hypermedia Smart Tutoring Systems was studied among students of Health Sciences, all of whom had similar test results in the use of metacognitive skills.

How Do B-Learning and Learning Patterns Influence Learning Outcomes?

Learning Management System (LMS) platforms provide a wealth of information on the learning patterns of students. Learning Analytics (LA) techniques permit the analysis of the logs or records of the activities of both students and teachers on the on-line platform. The learning patterns differ depending on the type of Blended Learning (B-Learning).