High quality diesel by hydrotreating of atmospheric gas oil/light cycle oil blends

An improved process for high-quality diesel fuel production by hydrotreating atmospheric gas oil (SRGO) and light cycle oil (LCO) blends is presented in this paper. For this purpose, a set of blends of 5, 10 and 15% by volume of LCO with final boiling points of 300, 325 and 350 °C with a full range gas oil (FBP 350 °C) was hydrotreated in a pilot plant at 340-380 °C, 5.4 MPa, 2.5 h⁻¹ LHSV using a commercial Co-Mo catalyst. A relationship between the concentration of refractory sulfur compounds (those boiling above 316 °C) and aromatics content in the feedstock with the hydrotreating temperature required for meeting a 0.05% sulfur specification was found.The experimental data obtained during the desulfurization was quantitatively represented by a 1.50 to 1.56 order rate equation, with activation energies between 18.9 and 34.1 kcal/mol, depending on the feedstock.     

On Linux starvation of CPU-bound processes in the presence of network I/O

Process starvation is a critical and challenging design problem in operating systems. A slight starvation of processes can lead to undesirable response times. In this paper, we experimentally demonstrate that Linux can starve CPU-bound processes in the presence of network I/O-bound processes. Surprisingly, the starvation of CPU-bound processes can be encountered at only a particular range of traffic rates being received by network processes. Lower or higher traffic rates do not exhibit starvation. We have analyzed it under different network applications, system settings and network configurations. We show that such starvation may exist for the two Linux scheduler, namely the 2.6 O(1) scheduler and the more recent 2.6 Completely Fair Scheduler (CFS). We instrumented and profiled the Linux kernel to investigate the underlying root causes of such starvation. In addition, we suggest possible mitigation solutions for both schedulers.     

Network-based sparse Bayesian classification

In some classification problems there is prior information about the joint relevance of groups of features. This knowledge can be encoded in a network whose nodes correspond to features and whose edges connect features that should be either both excluded or both included in the predictive model. In this paper, we introduce a novel network-based sparse Bayesian classifier (NBSBC) that makes use of the information about feature dependencies encoded in such a network to improve its prediction accuracy, especially in problems with a high-dimensional feature space and a limited amount of available training data. Approximate Bayesian inference is efficiently implemented in this model using expectation propagation. The NBSBC method is validated on four real-world classification problems from different domains of application: phonemes, handwritten digits, precipitation records and gene expression measurements. A comparison with state-of-the-art methods (support vector machine, network-based support vector machine and graph lasso) show that NBSBC has excellent predictive performance. It has the best accuracy in three of the four problems analyzed and ranks second in the modeling of the precipitation data. NBSBC also yields accurate and robust rankings of the individual features according to their relevance to the solution of the classification problem considered. The accuracy and stability of these estimates is an important factor in the good overall performance of this method.     

An improved approach for constrained robust model predictive control

In this paper, we present a new technique to address constrained robust model predictive control. The main advantage of this new approach with respect to other well-known techniques is the reduced conservativeness. Specifically, the technique described in this paper can be applied to polytopic uncertain systems and is based on the use of several Lyapunov functions each one corresponding to a different vertex of the uncertainty's polytope.     

Diagnosis systems in medicine with reusable knowledge components

Constructing knowledge systems is viewed as a modeling activity for developing structured knowledge and reasoning models. To ensure well-formed models, the use of some knowledge engineering methodology is crucial. Additionally, reusing models can significantly reduce the time and costs of building a new application. Reusing knowledge components across different applications and domains can help acquire expert knowledge and accurately describe the reasoning process. In fact, current knowledge engineering research has taken major initiatives in the development of knowledge systems by reusing generic components, such as ontologies or problem-solving methods. The article shows how we developed a diagnosis-aid system by reusing and adapting genetic knowledge components for diagnosing eye emergencies.     

Integral Sliding‐Mode Observation and Control for Switched Uncertain Linear Time Invariant Systems: a Robustifying Strategy

A robustifying methodology for switched systems with matched and unmatched uncertainties/perturbations and autonomous location transitions is presented. We avail of such methodology to design a control strategy based on integral sliding modes, thereby ensuring theoretically exact compensation of the matched uncertainties/perturbations and the minimization of the effects of the unmatched ones. An output integral sliding‐mode control technique, based on a switched algebraic hierarchical observer is also proposed. This approach allows the theoretically exact compensation of the matched uncertainties/perturbations right after the first moment. The proposed approach is illustrated via numerical simulations.     

Virtual Blocks: a serious game for spatial ability improvement on mobile devices

This paper presents a novel spatial instruction system for improving spatial abilities of engineering students. A 3D mobile game application called Virtual Blocks has been designed to provide a 3D virtual environment to build models with cubes that help students to perform visualization tasks to promote the development of their spatial ability during a short remedial course. A validation study with 26 freshman engineering students at La Laguna University (Spain) has concluded that the training had a measurable and positive impact on students’ spatial ability. In addition, the results obtained using a satisfaction questionnaire show that Virtual Blocks is considered an easy to use and stimulating application.     

From types to type requirements: genericity for model-driven engineering

Model-driven engineering (MDE) is a software engineering paradigm that proposes an active use of models during the development process. This paradigm is inherently type-centric, in the sense that models and their manipulation are defined over the types of specific meta-models. This fact hinders the reuse of existing MDE artefacts with other meta-models in new contexts, even if all these meta-models share common characteristics. To increase the reuse opportunities of MDE artefacts, we propose a paradigm shift from type-centric to requirement-centric specifications by bringing genericity into models, meta-models and model management operations. For this purpose, we introduce so-called concepts gathering structural and behavioural requirements for models and meta-models. In this way, model management operations are defined over concepts, enabling the application of the operations to any meta-model satisfying the requirements imposed by the concept. Model templates rely on concepts to define suitable interfaces, hence enabling the definition of reusable model components. Finally, similar to mixin layers, templates can be defined at the meta-model level as well, to define languages in a modular way, as well as layers of functionality to be plugged-in into other meta-models. These ideas have been implemented in MetaDepth, a multi-level meta-modelling tool that integrates action languages from the Epsilon family for model management and code generation.     

Supporting skill integration in an intelligent tutoring system for code tracing

Background

Skill integration is vital in students' mastery development and is especially prominent in developing code tracing skills which are foundational to programming, an increasingly important area in the current STEM education. However, instructional design to support skill integration in learning technologies has been limited.

Objectives

The current work presents the development and empirical evaluation of instructional design targeting students' difficulties in code tracing particularly in integrating component skills in the Trace Table Tutor (T3), an intelligent tutoring system.

Methods

Beyond the instructional features of active learning, step-level support, and individualized problem selection of intelligent tutoring systems (ITS), the instructional design of T3 (e.g., hints, problem types, problem selection) was optimized to target skill integration based on a domain model where integrative skills were represented as combinations of component skills. We conducted an experimental study in a university-level introductory Python programming course and obtained three findings.

Results and Conclusions

First, the instructional features of the ITS technology support effective learning of code tracing, as evidenced by significant learning gains (medium-to-large effect sizes). Second, performance data supports the existence of integrative skills beyond component skills. Third, an instructional design focused on integrative skills yields learning benefits beyond a design without such focus, such as improving performance efficiency (medium-to-large effect sizes).

Major Takeaways

Our work demonstrates the value of designing for skill integration in learning technologies and the effectiveness of the ITS technology for computing education, as well as provides general implications for designing learning technologies to foster robust learning.