Diagnosis of learner dropout based on learning styles for online distance learning

The amount of data generated by computer systems in Online Distance Learning (ODL) contains rich information. One example of this information we define as the Learner Learning Trail (LLT), which is the sequence of interactions between the students and the virtual environment. Another example is the Learner Learning Style (LLS), which is associated with the student behavior and choices during the learning process. This information can be used to identify learner behavior and learning style. We perceived, after the study of related literature, that the research field of learner diagnosis for ODL does not apply the conjoint use of LLT and LLS. In this article, we propose a model capable of integrating data generated from the behavior of students in ODL with cognitive aspects of them, such as their Learning Styles, by crossing LLT with LLS. We also propose the CPAD method (Collect, Preprocessing, Analysis, Diagnosis), which is implemented by collecting the raw data regarding learning activities, preprocessing the data into structured time sequences, analyzing the sequences regarding the learning styles and using this analysis to diagnose the learner behavior. We selected the dropout to investigate, once the dropout rate in ODL is a real problem in universities around the world. In addition, the dropout is a student decision which can be associated with previous students behaviors. We performed a study with 202 learners to evaluate if learning styles are capable of explaining aspects of the student behavior. The results suggest that Sequential/Global learning style dimension is more capable of explaining the dropout than the other dimensions. Also, we performed four classification experiments to verify how the dimensions of Felder-Silverman Learning Style Model influence the learner diagnosis. We perceived that the Sequential/Global dimension could provide a higher accuracy average with lower variation independently of the diagnosis technique.     

Superoleophilic Magnetic Iron Oxide Nanoparticles for Effective Hydrocarbon Removal from Water

Various hydrocarbons are efficiently extracted from water by using a new sorbent material based on covalently functionalized magnetic nanoparticles. The functionalization of the magnetite nanoparticles with a self‐assembled monolayer of hexadecylphosphonic acid renders the nanoparticles oleophilic and the magnetic nature of magnetite allows for simple extraction of the hydrocarbon‐soaked sorbent. The sorbent material is capable of extracting single contaminants such as alkanes and aromatics and complex hydrocarbon mixtures such as crude oils in high extraction rates of up to 14 times the sorbent volume. Experimental results are explained by molecular dynamics simulations on the adsorption of single components from a hydrocarbon‐water mixture to the alkylphosphonic acid layer on the nanoparticles. The core–shell sorbent material is highly stable and therefore, reusable over several successive extraction cycles without degradation. The extraction performance is determined at different water temperatures, different water sources, and different magnetic core materials and evaluated compared to heptadecanoic acid functionalized magnetite. The new sorbent material provides the opportunity for an efficient, reliable, inexpensive, and environmental friendly removal of hydrocarbons from water.     

Regularized signal reconstruction for level-crossing sampling using Slepian functions

In this paper, we propose a method for efficient signal reconstruction from non-uniformly spaced samples collected using level-crossing sampling. Level-crossing (LC) sampling captures samples whenever the signal crosses predetermined quantization levels. Thus the LC sampling is a signal-dependent, non-uniform sampling method. Without restriction on the distribution of the sampling times, the signal reconstruction from non-uniform samples becomes ill-posed. Finite-support and nearly band-limited signals are well approximated in a low-dimensional subspace with prolate spheroidal wave functions (PSWF) also known as Slepian functions. These functions have finite support in time and maximum energy concentration within a given bandwidth and as such are very appropriate to obtain a projection of those signals. However, depending on the LC quantization levels, whenever the LC samples are highly non-uniformly spaced obtaining the projection coefficients requires a Tikhonov regularized Slepian reconstruction. The performance of the proposed method is illustrated using smooth, bursty and chirp signals. Our reconstruction results compare favorably with reconstruction from LC-sampled signals using compressive sampling techniques.     

Weighted Hyperbolic DV-Hop Positioning Node Localization Algorithm in WSNs

The localization of nodes plays a fundamental role in Wireless Sensor and Actors Networks (WSAN) identifying geographically where an event occurred, which facilitates timely response to this action. This article presents a performance evaluation of multi-hop localization range-free algorithms used in WSAN, such as Distance Vector Hop (DV-Hop), Improved DV-Hop (IDV-Hop), and the Weighted DV-Hop (WDV-Hop). In addition, we propose a new localization algorithm, merging WDV-Hop, with the weighted hyperbolic localization algorithm (WH), which includes weights to the correlation matrix of the estimated distances between the node of interest (NOI) and the reference nodes (RN) in order to improve accuracy and precision. As performance metrics, the accuracy, precision, and computational complexity are evaluated. The algorithms are evaluated in three scenarios where all nodes are randomly distributed in a given area, varying the number of RNs, the density of nodes in the network, and radio coverage of the nodes. The results show that in networks with 100 nodes, WDV-Hop outperforms the DV-Hop and IDV-Hop even if the number of RNs is reduced to 10. Moreover, our proposal shows an improvement in terms of accuracy and precision at the cost of increased computational complexity, specifically in the algorithm execution time, but without affecting the hardware cost or power consumption.     

In search of computer-aided social support in non-communicable diseases care

Non-communicable diseases burden is well-known and care for these diseases goes beyond patients’ engagement, extending to their family, friends, and acquaintances. The ability of social relations in alleviating the harmful effects of health risks is known as social support. Computing can be used to promote social support to enhance the care of non-communicable diseases. However, it is unclear how computing obtains such enhancement. This paper presents a systematic review, in the form of a mapping study, aiming to answer how computing enhances non-communicable diseases care by using social data and by promoting social support. It also looks for available computing models focused on social support promotion in non-communicable diseases care. The study was guided by a two-phase process review, resulting in 38 reviewed papers from journals, conferences, and chapters in the period from 2010 to 2016. In general, the reviewed papers focus on controlled trials, frameworks and systems, knowledge discovery, simulation models or social media usage analysis. Knowledge discovery was the predominant subject, followed by social media usage analysis, and frameworks and systems.     

A fast algorithm to formulate Markov system equations

In this paper a new and efficient algorithm is presented to formulate a set of equations corresponding to a system with repair without pass through Markov graphs [1,2]. Using Karnaugh maps [3] for the states of the system and by the introduction of the concept of contiguous states, a basic method by inspection is derived. The hazard rates are assumed to be constant.     

Reliability analysis of two special three-state devices

In this paper Markov models of two special three-state devices are developed. Both of them have a single operating state considered as a good state. The first device has two mutually exclusive failure modes with repair. The second device has two mutually exclusive failure modes, one with repair and another without repair. Functions reliability and availability are computed.     

Autonomous and teleoperation control of a mobile robot

The present work proposes an autonomous tracking control system and a control structure to combine autonomous and teleoperation commands in a bicycle-type mobile robot. This compounded operation renders great flexibility to the control system of the mobile robot. For autonomous operation, a simple tracking controller that includes compensation of the robot dynamics is developed. This tracking control system is proved to be stable in the sense that it asymptotically reaches the tracking objective. Teleoperation with visual access to the robot’s workspace is integrated via a joystick with the autonomous operation of the robot. Simulations and experimental results on a prototype robot show the feasibility and performance of the proposed control system.     

An iterative physical optics approach for analyzing theelectromagnetic scattering by large open-ended cavities

A formulation based on the high frequency asymptotic principles of physical optics is developed for analyzing the scattering by relatively arbitrary open-ended waveguide cavities containing complex interior terminations. A magnetic field integral equation (MFIE) is obtained for the equivalent currents on the interior cavity walls and is solved using an iterative physical optics (IPO) algorithm which iteratively applies physical optics to account for multiple reflections inside the cavity. The number of iterations required for convergence is related to the expected number of important reflections. The IPO method is more approximate than a matrix solution of the MFIE, but it is quite accurate for electrically large cavities and is much more efficient. Numerical results are presented which demonstrate the convergence and accuracy of the method by comparison with modal reference solutions     

Improved Message Forwarding for Multi-Hop HaRTES Real-Time Ethernet Networks

Nowadays, switched Ethernet networks are used in complex systems that encompass tens to hundreds of nodes and thousands of signals. Such scenarios require multi-switch architectures where communications frequently occur in multiple hops. In this paper we investigate techniques to allow efficient multi-hop communication using HaRTES switches. These are modified Ethernet switches that provide real-time traffic scheduling, dynamic bandwidth management and temporal isolation between real-time and non-real-time traffic. This paper addresses the problem of forwarding traffic in HaRTES networks. Two methods have been recently proposed, namely Distributed Global Scheduling (DGS) that buffers traffic between switches, and Reduced Buffering Scheme (RBS), that uses immediate forwarding. In this paper, we discuss the design and implementation of RBS within HaRTES and we carry out an experimental validation with a prototype implementation. Then, we carry out a comparison between RBS and DGS using worst-case response time analysis and simulation. The comparison clearly establishes the superiority of RBS concerning end-to-end response times. In fact, with sample message sets, we achieved reductions in end-to-end delay that were as high as 80 %.