The Effects of System Type and System Characteristics on Skills Acquisition in Upper Secondary Education and Training

This report examines the effects of upper secondary system types and characteristics on literacy and numeracy skills acquisition during the upper secondary phase of education and training. Whereas there is a substantial literature on system effects on skills during the primary and lower secondary phases of education, much less has been written about these effects in relation to the upper secondary phase. However, with the arrival of the OECD’s Survey of Adult Skills (SAS), which has now tested adults in over 40 countries and regions, it is now possible to explore how far education system characteristics explain the substantial variation across countries in changes in skills levels and inequalities during upper secondary phase. In this report we seek to build on earlier work and provide more robust evidence on system effects during the upper secondary phase in three ways. Firstly, we use data from the larger sample of countries in both waves 1 and 2 of SAS. Secondly, we test the effects of a considerably wider range of system indicators. Thirdly, we use a variety of statistical methods to analyse the relationships across countries between upper secondary system types and characteristics and changes in levels and distributions of skills between age 15 (in PISA) and the end of the upper secondary phase. Whereas our previous work analysed changes using quasi-cohort analysis of published data on skills from PISA (at age 15) and SAS (at age 25-29), thus allowing compounding effects from tertiary education and employment, here we use customised data from OECD on skills scores at age 18-20 to capture more precisely the skills at the beginning and end of upper secondary education and training. Following a review of the literature on system effects, we identify a range of factors deemed to influence skills acquisition in the upper secondary phase and six upper secondary system types based on common and distinctive characteristics. The subsequent sections provide descriptive statistics on system characteristics by country/region and by system type and a statistical analysis, using both OLS regressions and Difference-in-Difference methods to estimate the effects of different types and characteristics on relative changes in skills levels and inequalities during the upper secondary phase

Linear response within the projection-based renormalization method: Many-body corrections beyond the random phase approximation

The explicit evaluation of linear response coefficients for interacting many-particle systems still poses a considerable challenge to theoreticians. In this work we use a novel many-particle renormalization technique, the so-called projector-based renormalization method, to show how such coefficients can systematically be evaluated. To demonstrate the prospects and power of our approach we consider the dynamical wave-vector dependent spin susceptibility of the two-dimensional Hubbard model and also determine the subsequent magnetic phase diagram close to half-filling. We show that the superior treatment of (Coulomb) correlation and fluctuation effects within the projector-based renormalization method significantly improves the standard random phase approximation results.Comment: 17 pages, 7 figures, revised versio

Faxen relations in solids - a generalized approach to particle motion in elasticity and viscoelasticity

A movable inclusion in an elastic material oscillates as a rigid body with six degrees of freedom. Displacement/rotation and force/moment tensors which express the motion of the inclusion in terms of the displacement and force at arbitrary exterior points are introduced. Using reciprocity arguments two general identities are derived relating these tensors. Applications of the identities to spherical particles provide several new results, including simple expressions for the force and moment on the particle due to plane wave excitation.Comment: 11 pages, 4 figure

Tensor Networks for Dimensionality Reduction and Large-Scale Optimizations. Part 2 Applications and Future Perspectives

Part 2 of this monograph builds on the introduction to tensor networks and their operations presented in Part 1. It focuses on tensor network models for super-compressed higher-order representation of data/parameters and related cost functions, while providing an outline of their applications in machine learning and data analytics. A particular emphasis is on the tensor train (TT) and Hierarchical Tucker (HT) decompositions, and their physically meaningful interpretations which reflect the scalability of the tensor network approach. Through a graphical approach, we also elucidate how, by virtue of the underlying low-rank tensor approximations and sophisticated contractions of core tensors, tensor networks have the ability to perform distributed computations on otherwise prohibitively large volumes of data/parameters, thereby alleviating or even eliminating the curse of dimensionality. The usefulness of this concept is illustrated over a number of applied areas, including generalized regression and classification (support tensor machines, canonical correlation analysis, higher order partial least squares), generalized eigenvalue decomposition, Riemannian optimization, and in the optimization of deep neural networks. Part 1 and Part 2 of this work can be used either as stand-alone separate texts, or indeed as a conjoint comprehensive review of the exciting field of low-rank tensor networks and tensor decompositions.Comment: 232 page

Tensor Networks for Dimensionality Reduction and Large-Scale Optimizations. Part 2 Applications and Future Perspectives

Part 2 of this monograph builds on the introduction to tensor networks and their operations presented in Part 1. It focuses on tensor network models for super-compressed higher-order representation of data/parameters and related cost functions, while providing an outline of their applications in machine learning and data analytics. A particular emphasis is on the tensor train (TT) and Hierarchical Tucker (HT) decompositions, and their physically meaningful interpretations which reflect the scalability of the tensor network approach. Through a graphical approach, we also elucidate how, by virtue of the underlying low-rank tensor approximations and sophisticated contractions of core tensors, tensor networks have the ability to perform distributed computations on otherwise prohibitively large volumes of data/parameters, thereby alleviating or even eliminating the curse of dimensionality. The usefulness of this concept is illustrated over a number of applied areas, including generalized regression and classification (support tensor machines, canonical correlation analysis, higher order partial least squares), generalized eigenvalue decomposition, Riemannian optimization, and in the optimization of deep neural networks. Part 1 and Part 2 of this work can be used either as stand-alone separate texts, or indeed as a conjoint comprehensive review of the exciting field of low-rank tensor networks and tensor decompositions.Comment: 232 page

Catalytic pyrolysis of plastic waste using metal-incorporated activated carbons for monomer recovery and carbon nanotube synthesis

As the global plastic waste crisis intensifies, innovative and sustainable solutions are urgently needed. This study evaluated waste-derived metal-incorporated activated carbon (AC) catalysts for the pyrolysis of mixed plastic waste to generate value-added products, focusing on product yield distribution, composition, hydrogen, and carbon nanotube (CNT) formation. Pyrolysis-catalysis experiments were conducted using a two-stage fixed-bed reactor, wherein the temperature was maintained at 500 °C in first stage (pyrolysis) and varied (500, 600, and 700 °C) in the second stage (catalysis). The tested ACs were incorporated with nickel (Ni-AC), iron (Fe-AC), and zinc (Zn-AC) to assess the impact of metal particles distributed on the carbonaceous support in the second stage. The results from the ACs were compared to those obtained using zeolite (H-ZSM-5), Raw-AC, and non-catalytic runs. The Ni-AC and Fe-AC demonstrated superior catalytic activity, with Ni-AC being more efficient in producing hydrogen (4.24wt%) and CNTs (34.5wt%) with diameters of approximately 30nm, and Fe-AC leading to higher gas yields (68.8wt%) and CNTs (12.4wt%) of around 60nm. In contrast, Zn-AC and Raw-AC presented limited effectiveness, although Raw-AC moderately outperformed Zn-AC with enhanced gas yields and reduced oil/wax yields. The zeolite H-ZSM-5 exhibited the highest gas yields (78wt%), converting heavy fractions into lighter molecules, notably the monomers ethylene and propylene. These findings provide valuable insights into catalyst selection and optimization for plastic waste pyrolysis processes, with H-ZSM-5 being the most effective catalyst for monomer recovery, and Ni-AC and Fe-AC demonstrating promising results