Highly Deformable, Ultrathin Large-Area Poly(methyl methacrylate) Films.

Poly(methyl methacrylate) (PMMA) is a glassy engineering polymer that finds extensive use in a number of applications. Over the past decade, thin films of PMMA were combined with graphene or other two-dimensional materials for applications in the area of nanotechnology. However, the effect of size upon the mechanical behavior of this thermoplastic polymer has not been fully examined. In this work, we adopted a homemade nanomechanical device to assess the yielding and fracture characteristics of freestanding, ultrathin (180-280 nm) PMMA films of a loaded area as large as 0.3 mm2. The measured values of Young's modulus and yield strength were found to be broadly similar to those measured in the bulk, but in contrast, all specimens exhibited a quite surprisingly high strain at failure (>20%). Detailed optical examination of the specimens during tensile loading showed clear evidence of craze development which however did not lead to premature fracture. This work may pave the way for the development of glassy thermoplastic films with high ductility at ambient temperatures

Portfolio selection problems in practice: a comparison between linear and quadratic optimization models

Several portfolio selection models take into account practical limitations on the number of assets to include and on their weights in the portfolio. We present here a study of the Limited Asset Markowitz (LAM), of the Limited Asset Mean Absolute Deviation (LAMAD) and of the Limited Asset Conditional Value-at-Risk (LACVaR) models, where the assets are limited with the introduction of quantity and cardinality constraints. We propose a completely new approach for solving the LAM model, based on reformulation as a Standard Quadratic Program and on some recent theoretical results. With this approach we obtain optimal solutions both for some well-known financial data sets used by several other authors, and for some unsolved large size portfolio problems. We also test our method on five new data sets involving real-world capital market indices from major stock markets. Our computational experience shows that, rather unexpectedly, it is easier to solve the quadratic LAM model with our algorithm, than to solve the linear LACVaR and LAMAD models with CPLEX, one of the best commercial codes for mixed integer linear programming (MILP) problems. Finally, on the new data sets we have also compared, using out-of-sample analysis, the performance of the portfolios obtained by the Limited Asset models with the performance provided by the unconstrained models and with that of the official capital market indices

Resource dedication problem in a multi-project environment

There can be different approaches to the management of resources within the context of multi-project scheduling problems. In general, approaches to multiproject scheduling problems consider the resources as a pool shared by all projects. On the other hand, when projects are distributed geographically or sharing resources between projects is not preferred, then this resource sharing policy may not be feasible. In such cases, the resources must be dedicated to individual projects throughout the project durations. This multi-project problem environment is defined here as the resource dedication problem (RDP). RDP is defined as the optimal dedication of resource capacities to different projects within the overall limits of the resources and with the objective of minimizing a predetermined objective function. The projects involved are multi-mode resource constrained project scheduling problems with finish to start zero time lag and non-preemptive activities and limited renewable and nonrenewable resources. Here, the characterization of RDP, its mathematical formulation and two different solution methodologies are presented. The first solution approach is a genetic algorithm employing a new improvement move called combinatorial auction for RDP, which is based on preferences of projects for resources. Two different methods for calculating the projects’ preferences based on linear and Lagrangian relaxation are proposed. The second solution approach is a Lagrangian relaxation based heuristic employing subgradient optimization. Numerical studies demonstrate that the proposed approaches are powerful methods for solving this problem

Local Search Algorithms for Portfolio Selection: Search Space and Correlation Analysis

Modern Portfolio Theory dates back from the fifties, and quantitative approaches to solve optimization problems stemming from this field have been proposed ever since. We propose a metaheuristic approach for the Portfolio Selection Problem that combines local search and Quadratic Programming, and we compare our approach with an exact solver. Search space and correlation analysis are performed to analyse the algorithm's performance, showing that metaheuristics can be efficiently used to determine optimal portfolio allocation

Factorial validity of the Toronto Alexithymia Scale (TAS-20) in clinical samples: A critical examination of the literature and a psychometric study in anorexia nervosa

There is extensive use of the 20-item Toronto Alexithymia Scale (TAS-20) in research and clinical practice in anorexia nervosa (AN), though it is not empirically established in this population. This study aims to examine the factorial validity of the TAS-20 in a Portuguese AN sample (N = 125), testing four different models (ranging from 1 to 4 factors) that were identified in critical examination of existing factor analytic studies. Results of confirmatory factor analysis (CFA) suggested that the three-factor solution, measuring difficulty identifying (DIF) and describing feelings (DDF), and externally oriented thinking (EOT), was the best fitting model. The quality of measurement improves if two EOT items (16 and 18) are eliminated. Internal consistency of EOT was low and decreased with age. The results provide support for the factorial validity of the TAS-20 in AN. Nevertheless, the measurement of EOT requires some caution and may be problematic in AN adolescents.Center for Psychology at the University of Porto, Portuguese Science Foundation (FCT UID/PSI/00050/2013) and EU FEDER through COMPETE 2020 program (POCI-01-0145-FEDER-007294info:eu-repo/semantics/acceptedVersio

Determinants of recovery from post-COVID-19 dyspnoea: analysis of UK prospective cohorts of hospitalised COVID-19 patients and community-based controls

Background The risk factors for recovery from COVID-19 dyspnoea are poorly understood. We investigated determinants of recovery from dyspnoea in adults with COVID-19 and compared these to determinants of recovery from non-COVID-19 dyspnoea. Methods We used data from two prospective cohort studies: PHOSP-COVID (patients hospitalised between March 2020 and April 2021 with COVID-19) and COVIDENCE UK (community cohort studied over the same time period). PHOSP-COVID data were collected during hospitalisation and at 5-month and 1-year follow-up visits. COVIDENCE UK data were obtained through baseline and monthly online questionnaires. Dyspnoea was measured in both cohorts with the Medical Research Council Dyspnoea Scale. We used multivariable logistic regression to identify determinants associated with a reduction in dyspnoea between 5-month and 1-year follow-up. Findings We included 990 PHOSP-COVID and 3309 COVIDENCE UK participants. We observed higher odds of improvement between 5-month and 1-year follow-up among PHOSP-COVID participants who were younger (odds ratio 1.02 per year, 95% CI 1.01–1.03), male (1.54, 1.16–2.04), neither obese nor severely obese (1.82, 1.06–3.13 and 4.19, 2.14–8.19, respectively), had no pre-existing anxiety or depression (1.56, 1.09–2.22) or cardiovascular disease (1.33, 1.00–1.79), and shorter hospital admission (1.01 per day, 1.00–1.02). Similar associations were found in those recovering from non-COVID-19 dyspnoea, excluding age (and length of hospital admission). Interpretation Factors associated with dyspnoea recovery at 1-year post-discharge among patients hospitalised with COVID-19 were similar to those among community controls without COVID-19. Funding PHOSP-COVID is supported by a grant from the MRC-UK Research and Innovation and the Department of Health and Social Care through the National Institute for Health Research (NIHR) rapid response panel to tackle COVID-19. The views expressed in the publication are those of the author(s) and not necessarily those of the National Health Service (NHS), the NIHR or the Department of Health and Social Care. COVIDENCE UK is supported by the UK Research and Innovation, the National Institute for Health Research, and Barts Charity. The views expressed are those of the authors and not necessarily those of the funders

Vehicle routing problems over time: a survey

In vehicle routing problems (VRPs) the decisions to be taken concern the assignment of customers to vehicles and the sequencing of the customers assigned to each vehicle. Additional decisions may need to be jointly taken, depending on the specific problem setting. In this paper, after discussing the different kinds of decisions taken in different classes of VRPs, the class where the decision about when the routes start from the depot has to be taken is considered and the related literature is reviewed. This class of problems, that we call VRPs over time, includes the periodic routing problems, the inventory routing problems, the vehicle routing problems with release dates, and the multi-trip vehicle routing problems

An approximation result for a duo-processor task scheduling problem

We consider the problem of scheduling tasks on a set of dedicated processors, where each task requires a subset of two processors be simultaneously available for a given processing time. The objective is to determine a nonpreemptive schedule with minimum completion time. By means of a graph theoretical formulation, we show that instances with up to 4 processors can be solved in polynomial time. With m = 2s + 1 processors (for s = 2, 3,...) and a minimum of m task types, we prove that the problem is NP-hard. Moreover, for this class of NP-hard instances, a simple O(m) approximation algorithm, whose performance ratio is bounded by 3s/(2s + 1), is given. The bound is shown to be tight