Interactive and collaborative technological ecosystems for improving academic motivation and engagement

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Interactive Instruction in Bayesian Inference

An instructional approach is presented to improve human performance in solving Bayesian inference problems. Starting from the original text of the classic Mammography Problem, the textual expression is modified and visualizations are added according to Mayer’s principles of instruction. These principles concern coherence, personalization, signaling, segmenting, multimedia, spatial contiguity, and pretraining. Principles of self-explanation and interactivity are also applied. Four experiments on the Mammography Problem showed that these principles help participants answer the questions at significantly improved rates. Nonetheless, in novel interactivity conditions, performance was lowered suggesting that more interaction can add more difficulty for participants. Overall, a leap forward in accuracy was found, with more than twice the participant accuracy of previous work. This indicates that an instructional approach to improving human performance in Bayesian inference is a promising direction.     

Ten Arguments Against the Proclaimed Amount of Dark Matter

According to the Standard cosmological model, our universe needs a significant amount of dark matter (DM), about six times that of the usual baryonic matter, besides an even larger amount of dark energy. But to date, both DMand dark energy have remained conceptually delusive, without concrete evidence based on direct physical measurements. In this survey paper, we present ten counterarguments showing that such a claimed amount of DMcan be a result of vast overestimation and does not conform to reality. Some of those counterarguments can be convincingly verified even by simple hand calculations.     

A satellite remote-sensing multi-index approach to discriminate pelagic Sargassum in the waters of the Yucatan Peninsula,Mexico

Recently, the need for quantitative information on the spatiotemporal distribution of floating macroalgae, particularly the two species of genus Sargassum, has grown because of blooms of these species in the Gulf of Mexico and Caribbean Sea. Remote sensing is one of the most frequently used tools to assess pelagic Sargassum distribution. The purpose of this study was to implement a methodological approach to detect floating algae in an efficient and replicable manner at a moderate cost. We analyzed Landsat 8 imagery, from which we calculated four vegetation indices and one floating-algae index to implement a supervised classification, together with the bands 2 and 5, using the Random Forest algorithm. The analysis was performed monthly from 2014 to 2015 for the northeastern Yucatan Peninsula, Mexico, with a total of 91 analyzed images. The quantitative performance metrics of the classifier (overall, Kappa and Tau) were greater than 80%, whereas bands 2 and 5 as well as the atmospherically resistant vegetation index made the greatest contributions to the classifications. During summer 2015, more than 4,000 ha of Sargassum coverage per image were observed, which was substantially greater than that over the rest of the period. This approach constitutes a transferable alternative for the systematic detection of Sargassum, which enables a quantitative semi-automated time series comparison.     

A multiobjective GRASP–VND algorithm to solve the waste collection problem

In this paper, the waste collection problem (WCP) of a city in the south of Spain is addressed as a multiobjective routing problem that considers three objectives. From the company's perspective, the minimization of the travel cost is desired as well as that of the total number of vehicles. Additionally, from the employee's point of view, a set of balanced routes is also sought. Four variants of a multiobjective hybrid algorithm are proposed. Specifically, a GRASP (greedy randomized adaptive search procedure) with a VND (variable neighborhood descent) is combined. The best GRASP–VND algorithm found is applied in order to solve the real‐world WCP of a city in the south of Spain.     

Morphological co-processing unit for embedded devices

This paper focuses on the development of a fully programmable morphological coprocessor for embedded devices. It is a well-known fact that the majority of morphological processing operations are composed of a (potentially large) number of sequential elementary operators. At the same time, the industrial context induces a high demand on robustness and decision liability that makes the application even more demanding. Recent stationary platforms (PC, GPU, clusters) no more represent a computational bottleneck in real-time vision or image processing applications. However, in embedded solutions such applications still hit computational limits. The morphological co-processing unit (MCPU) replies to this demand. It assembles the previously published efficient dilation/erosion units with geodesic units and ALUs to support a larger collection of morphological operations, from a simple dilation to serial filters involving a geodesic reconstruction step. The coprocessor has been integrated into an FPGA platform running a server that is able to respond to client’s requests over the ethernet. The experimental performance of the MCPU measured on a wide set of operations brings as results in orders of magnitude better than another embedded platform, built around an ARM A9 quad-core processor.     

Runtime verification of autopilot systems using a fragment of MTL- $${int }$$

International Journal on Software Tools for Technology Transfer - Current real-time embedded systems development frameworks lack support for the verification of properties using explicit time where...     

Short-Term Land use Planning and Optimal Subsidies

Networks and Spatial Economics - Urban planning is a complex problem which includes choosing a social objective for a city, finding the associated optimal allocation of agents and identifying...