16,062 research outputs found

    Optimal Control of the Landau-de Gennes Model of Nematic Liquid Crystals

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    We present an analysis and numerical study of an optimal control problem for the Landau-de Gennes (LdG) model of nematic liquid crystals (LCs), which is a crucial component in modern technology. They exhibit long range orientational order in their nematic phase, which is represented by a tensor-valued (spatial) order parameter Q=Q(x)Q = Q(x). Equilibrium LC states correspond to QQ functions that (locally) minimize an LdG energy functional. Thus, we consider an L2L^2-gradient flow of the LdG energy that allows for finding local minimizers and leads to a semi-linear parabolic PDE, for which we develop an optimal control framework. We then derive several a priori estimates for the forward problem, including continuity in space-time, that allow us to prove existence of optimal boundary and external ``force'' controls and to derive optimality conditions through the use of an adjoint equation. Next, we present a simple finite element scheme for the LdG model and a straightforward optimization algorithm. We illustrate optimization of LC states through numerical experiments in two and three dimensions that seek to place LC defects (where Q(x)=0Q(x) = 0) in desired locations, which is desirable in applications.Comment: 26 pages, 9 figure

    Likelihood Asymptotics in Nonregular Settings: A Review with Emphasis on the Likelihood Ratio

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    This paper reviews the most common situations where one or more regularity conditions which underlie classical likelihood-based parametric inference fail. We identify three main classes of problems: boundary problems, indeterminate parameter problems -- which include non-identifiable parameters and singular information matrices -- and change-point problems. The review focuses on the large-sample properties of the likelihood ratio statistic. We emphasize analytical solutions and acknowledge software implementations where available. We furthermore give summary insight about the possible tools to derivate the key results. Other approaches to hypothesis testing and connections to estimation are listed in the annotated bibliography of the Supplementary Material

    A Design Science Research Approach to Smart and Collaborative Urban Supply Networks

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    Urban supply networks are facing increasing demands and challenges and thus constitute a relevant field for research and practical development. Supply chain management holds enormous potential and relevance for society and everyday life as the flow of goods and information are important economic functions. Being a heterogeneous field, the literature base of supply chain management research is difficult to manage and navigate. Disruptive digital technologies and the implementation of cross-network information analysis and sharing drive the need for new organisational and technological approaches. Practical issues are manifold and include mega trends such as digital transformation, urbanisation, and environmental awareness. A promising approach to solving these problems is the realisation of smart and collaborative supply networks. The growth of artificial intelligence applications in recent years has led to a wide range of applications in a variety of domains. However, the potential of artificial intelligence utilisation in supply chain management has not yet been fully exploited. Similarly, value creation increasingly takes place in networked value creation cycles that have become continuously more collaborative, complex, and dynamic as interactions in business processes involving information technologies have become more intense. Following a design science research approach this cumulative thesis comprises the development and discussion of four artefacts for the analysis and advancement of smart and collaborative urban supply networks. This thesis aims to highlight the potential of artificial intelligence-based supply networks, to advance data-driven inter-organisational collaboration, and to improve last mile supply network sustainability. Based on thorough machine learning and systematic literature reviews, reference and system dynamics modelling, simulation, and qualitative empirical research, the artefacts provide a valuable contribution to research and practice

    Subsidiary Entrepreneurial Alertness: Antecedents and Outcomes

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    This thesis brings together concepts from both international business and entrepreneurship to develop a framework of the facilitators of subsidiary innovation and performance. This study proposes that Subsidiary Entrepreneurial Alertness (SEA) facilitates the recognition of opportunities (the origin of subsidiary initiatives). First introduced by Kirzner (1979) in the context of the individual, entrepreneurial alertness (EA) is the ability to notice an opportunity without actively searching. Similarly, to entrepreneurial alertness at the individual level, this study argues that SEA enables the subsidiary to best select opportunities based on resources available. The research further develops our conceptualisation of SEA by drawing on work by Tang et al. (2012) identifying three distinct activities of EA: scanning and search (identifying opportunities unseen by others due to their awareness gaps), association and connection of information, and evaluation and judgement to interpret or anticipate future viability of opportunities. This study then hypothesises that SEA leads to opportunity recognition at the subsidiary level and further hypothesises innovation and performance as outcomes of opportunity recognition. This research brings these arguments together to develop and test a comprehensive theoretical model. The theoretical model is tested through a mail survey of the CEOs/MDs of foreign subsidiaries within the Republic of Ireland (an innovative hub for foreign subsidiaries). This method was selected as the best method to reach the targeted respondent, and due to the depth of knowledge the target respondent holds, the survey can answer the desired question more substantially. The results were examined using partial least squares structural equation modelling (PLS-SEM). The study’s findings confirm two critical aspects of subsidiary context, subsidiary brokerage and subsidiary credibility are positively related to SEA. The study establishes a positive link between SEA and both the generation of innovation and the subsidiary’s performance. This thesis makes three significant contributions to the subsidiary literature as it 1) introduces and develops the concept of SEA, 2) identifies the antecedents of SEA, and 3) demonstrates the impact of SEA on subsidiary opportunity recognition. Implications for subsidiaries, headquarters and policy makers are discussed along with the limitations of the study

    Countermeasures for the majority attack in blockchain distributed systems

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    La tecnología Blockchain es considerada como uno de los paradigmas informáticos más importantes posterior al Internet; en función a sus características únicas que la hacen ideal para registrar, verificar y administrar información de diferentes transacciones. A pesar de esto, Blockchain se enfrenta a diferentes problemas de seguridad, siendo el ataque del 51% o ataque mayoritario uno de los más importantes. Este consiste en que uno o más mineros tomen el control de al menos el 51% del Hash extraído o del cómputo en una red; de modo que un minero puede manipular y modificar arbitrariamente la información registrada en esta tecnología. Este trabajo se enfocó en diseñar e implementar estrategias de detección y mitigación de ataques mayoritarios (51% de ataque) en un sistema distribuido Blockchain, a partir de la caracterización del comportamiento de los mineros. Para lograr esto, se analizó y evaluó el Hash Rate / Share de los mineros de Bitcoin y Crypto Ethereum, seguido del diseño e implementación de un protocolo de consenso para controlar el poder de cómputo de los mineros. Posteriormente, se realizó la exploración y evaluación de modelos de Machine Learning para detectar software malicioso de tipo Cryptojacking.DoctoradoDoctor en Ingeniería de Sistemas y Computació

    Fast approximate Barnes interpolation: illustrated by Python-Numba implementation fast-barnes-py v1.0

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    Barnes interpolation is a method that is widely used in geospatial sciences like meteorology to remodel data values recorded at irregularly distributed points into a representative analytical field. When implemented naively, the effort to calculate Barnes interpolation depends on the product of the number of sample points N and the number of grid points W×H, resulting in a computational complexity of O(N⋅W⋅H). In the era of highly resolved grids and overwhelming numbers of sample points, which originate, e.g., from the Internet of Things or crowd-sourced data, this computation can be quite demanding, even on high-performance machines. This paper presents new approaches of how very good approximations of Barnes interpolation can be implemented using fast algorithms that have a computational complexity of O(N+W⋅H). Two use cases in particular are considered, namely (1) where the used grid is embedded in the Euclidean plane and (2) where the grid is located on the unit sphere.</p

    Multi-Attribute Utility Preference Robust Optimization: A Continuous Piecewise Linear Approximation Approach

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    In this paper, we consider a multi-attribute decision making problem where the decision maker's (DM's) objective is to maximize the expected utility of outcomes but the true utility function which captures the DM's risk preference is ambiguous. We propose a maximin multi-attribute utility preference robust optimization (UPRO) model where the optimal decision is based on the worst-case utility function in an ambiguity set of plausible utility functions constructed using partially available information such as the DM's specific preferences between some lotteries. Specifically, we consider a UPRO model with two attributes, where the DM's risk attitude is multivariate risk-averse and the ambiguity set is defined by a linear system of inequalities represented by the Lebesgue-Stieltjes (LS) integrals of the DM's utility functions. To solve the maximin problem, we propose an explicit piecewise linear approximation (EPLA) scheme to approximate the DM's true unknown utility so that the inner minimization problem reduces to a linear program, and we solve the approximate maximin problem by a derivative-free (Dfree) method. Moreover, by introducing binary variables to locate the position of the reward function in a family of simplices, we propose an implicit piecewise linear approximation (IPLA) representation of the approximate UPRO and solve it using the Dfree method. Such IPLA technique prompts us to reformulate the approximate UPRO as a single mixed-integer program (MIP) and extend the tractability of the approximate UPRO to the multi-attribute case. Furthermore, we extend the model to the expected utility maximization problem with expected utility constraints where the worst-case utility functions in the objective and constraints are considered simultaneously. Finally, we report the numerical results about performances of the proposed models.Comment: 50 pages,18 figure

    Equations discovery of organized cloud fields: Stochastic generator and dynamical insights

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    The emergence of organized multiscale patterns resulting from convection is ubiquitous, observed throughout different cloud types. The reproduction of such patterns by general circulation models remains a challenge due to the complex nature of clouds, characterized by processes interacting over a wide range of spatio-temporal scales. The new advances in data-driven modeling techniques have raised a lot of promises to discover dynamical equations from partial observations of complex systems. This study presents such a discovery from high-resolution satellite datasets of continental cloud fields. The model is made of stochastic differential equations able to simulate with high fidelity the spatio-temporal coherence and variability of the cloud patterns such as the characteristic lifetime of individual clouds or global organizational features governed by convective inertia gravity waves. This feat is achieved through the model's lagged effects associated with convection recirculation times, and hidden variables parameterizing the unobserved processes and variables.Comment: 11 pages, 9 figure

    Mechanism of Qihuang needle therapy in the management of tic disorders: a clinical trial protocol

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    BackgroundQihuang needle therapy is a newly developed acupuncture therapy to treat tic disorders in clinical practice. However, the mechanism to reduce tic severity remains unknown. Changes in intestinal flora and circulation metabolites are perhaps the potential pathogenesis of tic disorders. As a result, we present a protocol for a controlled clinical trial using multi-omics analysis to probe the mechanism of the Qihuang needle in managing tic disorders.MethodsThis is a matched-pairs design, controlled, clinical trial for patients with tic disorders. Participants will be allocated to either an experimental group or a healthy control group. The main acupoints are Baihui (GV20), Yintang (EX-HN3), and Jueyinshu (BL14). The experimental group will receive Qihuang needle therapy for a month, while the control group will receive no interventions.Expected outcomesThe change in the severity of the tic disorder is set as the main outcome. Secondary outcomes include gastrointestinal severity index and recurrence rate, which will be calculated after a 12-week follow-up. Gut microbiota, measured by 16S rRNA gene sequencing; serum metabolomics, assessed via LC/MS; and serum zonulin, assessed by enzyme-linked immunosorbent assay (ELISA), will be used as biological specimen analysis outcomes. The present study will investigate the possible interactions between intestinal flora and serum metabolites and the improvement of clinical profiles, which may elucidate the mechanism of Qihuang needle therapy for tic disorders.Trial registrationThis trial is registered at the Chinese Clinical Trial Registry (http://www.chictr.org.cn/). Registration number: ChiCTR2200057723, Date: 2022-04-14

    The cosmic waltz of Coma Berenices and Latyshev 2 (Group X). Membership, phase-space structure, mass, and energy distributions

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    Context. Open clusters (OCs) are fundamental benchmarks where theories of star formation and stellar evolution can be tested and validated. Coma Ber and Latyshev 2 (Group X) are the second and third OCs closest to the Sun, making them excellent targets to search for low-mass stars and ultra-cool dwarfs. In addition, this pair will experience a flyby in 10-16 Myr which makes it a benchmark to test OCs pair interactions. Aims. We aim at analysing the membership, luminosity, mass, phase-space (i.e., positions and velocities), and energy distributions for Coma Ber and Latyshev 2 and test the hypothesis of the mixing of their populations at the encounter time. Methods. We develop a new phase-space membership methodology and apply it to Gaia data. With the recovered members we infer the phase-space, luminosity and mass distributions using publicly available Bayesian inference codes. Then, with a publicly available orbit integration code and members' positions and velocities, we integrate their orbits 20 Myr into the future. Results. In Coma Ber, we identify 302 candidate members distributed in the core and tidal tails. The tails are dynamically cold and asymmetrically populated. The stellar system called Group X is made of two structures: the disrupted OC Latyshev 2 (186 candidate members) and a loose stellar association called Mecayotl 1 (146 candidate members), both of them will fly by Coma Ber in 11.3±0.511.3\pm0.5 Myr and 14.0±0.614.0\pm0.6 Myr, respectively, and each other in 8.1±1.38.1\pm1.3 Myr. Conclusions. We study the dynamical properties of the core and tails of Coma Ber and also confirm the existence of the OC Latyshev 2 and its neighbour stellar association Mecayotl 1. Although these three systems will experience encounters we find no evidence supporting the mixing of their populations.Comment: 25 pages, 19 figures, accepted for publication in Astronomy & Astrophysic
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