A New Estimation Study of the Stress-Strength Reliability for the Topp–Leone Distribution Using Advanced Sampling Methods

In this manuscript, we investigate the estimation of the unknown reliability measure R = P [Y < X], in the case where Y and X are two independent random variables with Topp–Leone distributions. As the main contribution, various advanced sampling strategies are studied. The suggested strategies are simple random, ranked set, and median ranked set samplings. Firstly, based on the maximum likelihood, we give an efficient estimator of R when the observations of the two random variables are selected from the same simple random sample. Secondly, such an estimator is addressed when the observations of the two random variables are selected from the ranked set sampling method. Then, based on median ranked set sampling, the maximum likelihood estimator of R is addressed in all the four cases. When the observations from the two random variables are selected from the same set size, two cases are considered, while the other two cases are considered at different set sizes. A simulation research is developed to evaluate the behavior of the obtained estimates based on standard and median ranked set samplings with their simple random sampling equivalents. The ratio of mean square error is used to assess the effectiveness of these estimates.

Advanced Sampling Methods for Multiscale Simulation of Disordered Proteins and Dynamic Interactions

Intrinsically disordered proteins (IDPs) are highly prevalent and play important roles in biology and human diseases. It is now also recognized that many IDPs remain dynamic even in specific complexes and functional assemblies. Computer simulations are essential for deriving a molecular description of the disordered protein ensembles and dynamic interactions for a mechanistic understanding of IDPs in biology, diseases, and therapeutics. Here, we provide an in-depth review of recent advances in the multi-scale simulation of disordered protein states, with a particular emphasis on the development and application of advanced sampling techniques for studying IDPs. These techniques are critical for adequate sampling of the manifold functionally relevant conformational spaces of IDPs. Together with dramatically improved protein force fields, these advanced simulation approaches have achieved substantial success and demonstrated significant promise towards the quantitative and predictive modeling of IDPs and their dynamic interactions. We will also discuss important challenges remaining in the atomistic simulation of larger systems and how various coarse-grained approaches may help to bridge the remaining gaps in the accessible time- and length-scales of IDP simulations.

Revisiting Wedge Sampling for Budgeted Maximum Inner Product Search (Extended Abstract)

Top-k maximum inner product search (MIPS) is a central task in many machine learning applications. This work extends top-k MIPS with a budgeted setting, that asks for the best approximate top-k MIPS given a limited budget of computational operations. We study recent advanced sampling methods, including wedge and diamond sampling, to solve budgeted top-k MIPS. First, we theoretically show that diamond sampling is essentially a combination of wedge sampling and basic sampling for top-k MIPS. Second, we propose dWedge, a simple deterministic variant of wedge sampling for budgeted top-k MIPS. Empirically, dWedge provides significantly higher accuracy than other budgeted top-k MIPS solvers while maintaining a similar speedup.

A Molecular Dynamics Simulation Scenario for Studying Solvent-mediated Interactions of Polymers and Application to Thermoresponse of Poly (N-isopropylacrylamide) in Water

Studying equilibrium properties of polymers in solution by atomistic simulations is a challenging task as the available computation time is often not sufficient to ensure representative sampling of the phase space. One approach to tackle this problem is to create a simulation scenario which is simple enough to enable adequate sampling of equilibrium states while it retains the essential parts of the physics of the polymer in solution. In this work, we present and test such a scenario, which is designed for studying whether a given polymer will aggregate or dissolve in a given solvent. Two periodic polymer molecules are simulated in the explicit solvent. The distance d between the polymer chains lends itself as an order parameter so that advanced sampling techniques, such as umbrella sampling, can be applied easily. A state corresponding to dissolved polymers (large d) and a state corresponding to aggregated polymers (small d) can be defined. The scenario misses the intramolecular collapse of the single chain, but it retains full atomistic detail regarding the polymer-solvent and the intermolecular polymer-polymer interactions. Thethermoresponsive behavior of PNIPAM in water is studied with the new scenario, and it is shown that quantitative predictions of the experimental equilibrium data can be obtained after adjusting a single state-independent parameter in the force field.

LCA Practices of Plastics and Their Recycling: A Critical Review

In a bid to help address the environmental footprints associated with products and services, life cycle assessment (LCA) applications have become increasingly popular throughout the years. This review summarizes some important methodological developments in recent years, such as the advent of dynamic LCA, as well as highlighting recent LCA applications in the context of plastics/recycling with a focus on their methodological choices. Furthermore, this review aims to offer a set of possible research lines to improve the gap between LCA and decision-making (policy). It was found that the majority of reviewed papers are mostly conservative in their methodological practice, employing mostly static analyses and making little use of other methods. In order to bridge the gap between LCA and policy, it is suggested to broaden system boundaries through the integration of dynamic modelling methods, incorporating interactions between fore- and background systems, and including behavioral components where relevant. In addition, advanced sampling routines to further explore and assess the policy space are recommended. This is of paramount importance when dealing with recycling processes as the molecules/polymers constituting the output of those processes have to be benchmarked in terms of costs and, crucially, their sustainability character against virgin ones.

Code interoperability extends the scope of quantum simulations

The functionality of many materials is critically dependent on the integration of dissimilar components and on the interfaces that arise between them. The description of such heterogeneous components requires the development and deployment of first principles methods, coupled to appropriate dynamical descriptions of matter and advanced sampling techniques, in order to capture all the relevant length and time scales of importance to the materials’ performance. It is thus essential to build simple, streamlined computational schemes for the prediction and design of multiple properties of broad classes of materials, by developing interoperable codes which can be efficiently coupled to each other to perform complex tasks. We discuss the use of interoperable codes to simulate the structural and spectroscopic characterization of materials, including chemical reactions for catalysis, the description of defects for quantum information science, and heat and charge transport.