Course Recommendation based on Sequences: An Evolutionary Search of Emerging Sequential Patterns

To provide a good study plan is key to avoid students’ failure. Academic advising based on student’s preferences, complexity of the semester, or even background knowledge is usually considered to reduce the dropout rate. This article aims to provide a good course index to recommend courses to students based on the sequence of courses already taken by each student. Hence, unlike existing long-term course planning methods, it is based on graduate students to model the course and not on external factors that might introduce some bias in the process. The proposal includes a novel sequential pattern mining algorithm, called (ES)2P (Evolutionary Search of Emerging Sequential Patterns), that properly identifies paths followed by good students and not followed by not so good students, as a long-term course planning approach. A major feature of the proposed (ES)2P algorithm is its ability to extract the best k solutions, that is, those with a best recommendation index score instead of returning the whole set of solutions above a predefined threshold. A real study case is performed including more than 13,000 students belonging to 13 faculties to demonstrate the usefulness of the proposal not only to recommend study plans but also to give advices at different stages of the students’ learning process

Course Recommendation based on Sequences: An Evolutionary Search of Emerging Sequential Patterns

Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work was supported by the Spanish Ministry of Science and Innovation, project PID2020-115832GBI00, and the University of Cordoba, project UCO-FEDER 18 REF.1263116 MOD.A. Both projects were also supported by the European Fund of Regional Development.To provide a good study plan is key to avoid students’ failure. Academic advising based on student’s preferences, complexity of the semester, or even background knowledge is usually considered to reduce the dropout rate. This article aims to provide a good course index to recommend courses to students based on the sequence of courses already taken by each student. Hence, unlike existing long-term course planning methods, it is based on graduate students to model the course and not on external factors that might introduce some bias in the process. The proposal includes a novel sequential pattern mining algorithm, called (ES)2 P (Evolutionary Search of Emerging Sequential Patterns), that properly identifies paths followed by good students and not followed by not so good students, as a long-term course planning approach. A major feature of the proposed (ES)2 P algorithm is its ability to extract the best k solutions, that is, those with a best recommendation index score instead of returning the whole set of solutions above a predefined threshold. A real study case is performed including more than 13,000 students belonging to 13 faculties to demonstrate the usefulness of the proposal not only to recommend study plans but also to give advices at different stages of the students’ learning process.CRUE-CSICSpringer NatureSpanish Government PID2020-115832GBI00University of Cordoba UCO-FEDER 18 REF.1263116 MOD.

Trade sustainability impact assessment (SIA) on the comprehensive economic and trade agreement (CETA) between the EU and Canada: Final report

Commissioned by the European Commission, the Final Report for the EU-Canada Sustainability Impact Assessment (SIA) on the EU-Canada Comprehensive Economic and Trade Agreement (CETA) provides a comprehensive assessment of the potential impacts of trade liberalisation under CETA. The analysis assesses the economic, social and environmental impacts in Canada and the European Union in three main sectors, sixteen sub-sectors and across seven cross-cutting issues. It predicts a number of macro-economic and sector-specific impacts. The macro analysis suggests the EU may see increases in real GDP of 0.02-0.03% in the long-term from CETA, whereas Canada may see increases of 0.18-0.36%. The Investment section of the report suggests these numbers could be higher when factoring in investment increases. At the sectoral level, the study predicts the greatest gains in output and trade to be stimulated by services liberalisation and by the removal of tariffs applied on sensitive agricultural products. It also suggests CETA could have a positive social impact if it includes provisions on the ILO’s Core Labour Standards and Decent Work Agenda. The study also details a variety of impacts in various “cross-cutting” components of CETA. It finds CETA would stimulate investment in Canada, and to a lesser extent in the EU; and finds costs outweigh the benefits of including controversial NAFTA-style investor-state dispute settlement (ISDS) provisions in CETA. It predicts potentially imbalanced benefits from a government procurement (GP) chapter. The study assumes CETA will lead to an upward harmonisation in intellectual property rights (IPR) regulations, particularly in Canada, which will have a number of effects. It predicts some notable impacts in terms of competition policy, as well as trade facilitation, free circulation of goods and labour mobility.EU-Canada Sustainability Impact Assessment; SIA; EU-Canada Comprehensive Economic and Trade Agreement; Comprehensive Economic and Trade Agreement; CETA; government procurement; investor-state provisions; ISDS; competition policy; Dan Prud'homme; trade impact assessment

Novel methods to predict solid-state material properties

Solid-state materials find ubiquitous use in modern technology - from semiconductors in electronics to steel in buildings and superconductors in MRI machines. Theoretical understanding of the atomic-scale behaviour of these materials can be leveraged to design new materials with desirable properties. In this thesis, we investigate the challenges that arise when this is attempted in practice. Accurate and inexpensive methods to tackle the atomic-scale problem are a prerequisite for materials discovery. We begin with a description of existing methods. This is followed by the development of a Monte Carlo method to calculate expectation values from the many-body picture without the need for a trial wavefunction, which is both a fundamental, and practical, limitation in existing techniques. Having explored first-principles methods, we turn to their use in understanding materials, beginning with an investigation of the structure of Lithium. Structure searching calculations result in a mixed-phase model at low temperatures, in good agreement with previous experimental and theoretical results. The quasi-harmonic treatment of finite-temperature thermodynamics is extended to include anharmonic nuclear vibrations, which are shown to not alter the phase diagram despite the small mass of the Li atoms. Focus then shifts towards leveraging these same methods to discover novel superconductors. This begins with an investigation of the LaH$_{10}$ and YH$_{10}$ compounds, where a new hexagonal phase of LaH$_{10}$ provides an explanation for recent experimental measurements. Machine-learning techniques and novel screening methods are then employed to discover hydrides of Rb and Cs that exhibit superconductivity at significantly lower pressures than LaH$_{10}$. Optimizations to, and automation of, the workflow then enables the discovery of superconductors on an unprecedented scale, leading to hundreds of new high-temperature superconductors. Throughout the thesis, the importance of structures that are saddle-points of the energy landscape becomes apparent. The thesis closes with the development of a new algorithm to locate saddle-points that requires no additional information beyond that used by the cheapest existing methods. This thesis demonstrates that there is progress to be made at every stage of the first-principles materials discovery process and highlights that improving the workflow itself is a non-trivial, but fruitful, pursuit

Recent Advances in Graph Partitioning

We survey recent trends in practical algorithms for balanced graph partitioning together with applications and future research directions

Town of Brownville Maine Ordinances

Ordinances Cover: Dog Control; Fireworks; Floodplain; Parking; Shoreland Zonin