Showing papers by "University of Luxembourg published in 2022"

VQE method: a short survey and recent developments

Abstract: Abstract The variational quantum eigensolver (VQE) is a method that uses a hybrid quantum-classical computational approach to find eigenvalues of a Hamiltonian. VQE has been proposed as an alternative to fully quantum algorithms such as quantum phase estimation (QPE) because fully quantum algorithms require quantum hardware that will not be accessible in the near future. VQE has been successfully applied to solve the electronic Schrödinger equation for a variety of small molecules. However, the scalability of this method is limited by two factors: the complexity of the quantum circuits and the complexity of the classical optimization problem. Both of these factors are affected by the choice of the variational ansatz used to represent the trial wave function. Hence, the construction of an efficient ansatz is an active area of research. Put another way, modern quantum computers are not capable of executing deep quantum circuits produced by using currently available ansatzes for problems that map onto more than several qubits. In this review, we present recent developments in the field of designing efficient ansatzes that fall into two categories—chemistry–inspired and hardware–efficient—that produce quantum circuits that are easier to run on modern hardware. We discuss the shortfalls of ansatzes originally formulated for VQE simulations, how they are addressed in more sophisticated methods, and the potential ways for further improvements.

Fair power allocation in cooperative cognitive systems under NOMA transmission for future IoT networks

Abstract: To support the massive connectivity in Internet of Things (IoT), several promising techniques like cognitive radio (CR) and non-orthogonal multiple access (NOMA) enables the user to share spectrum resources. This work aims to achieve fairness among secondary users (SUs) in IoT cooperative NOMA-based CR transmission. We design a power allocation algorithm, an independent battery constraint at each node is considered, and power gap among transmissions of two NOMA users is applied for successive interference cancellation. The simulation results show that the proposed framework provides excellent performance and for sufficient available transmission power perfect fairness is achieved.

Neurodegeneration and neuroinflammation are linked, but independent of alpha‐synuclein inclusions, in a seeding/spreading mouse model of Parkinson's disease

Abstract: A key pathological process in Parkinson's disease (PD) is the transneuronal spreading of α-synuclein. Alpha-synuclein (α-syn) is a presynaptic protein that, in PD, forms pathological inclusions. Other hallmarks of PD include neurodegeneration and microgliosis in susceptible brain regions. Whether it is primarily transneuronal spreading of α-syn particles, inclusion formation, or other mechanisms, such as inflammation, that cause neurodegeneration in PD is unclear. We used a model of spreading of α-syn induced by striatal injection of α-syn preformed fibrils into the mouse striatum to address this question. We performed quantitative analysis for α-syn inclusions, neurodegeneration, and microgliosis in different brain regions, and generated gene expression profiles of the ventral midbrain, at two different timepoints after disease induction. We observed significant neurodegeneration and microgliosis in brain regions not only with, but also without α-syn inclusions. We also observed prominent microgliosis in injured brain regions that did not correlate with neurodegeneration nor with inclusion load. Using longitudinal gene expression profiling, we observed early gene expression changes, linked to neuroinflammation, that preceded neurodegeneration, indicating an active role of microglia in this process. Altered gene pathways overlapped with those typical of PD. Our observations indicate that α-syn inclusion formation is not the major driver in the early phases of PD-like neurodegeneration, but that microglia, activated by diffusible, oligomeric α-syn, may play a key role in this process. Our findings uncover new features of α-syn induced pathologies, in particular microgliosis, and point to the necessity for a broader view of the process of α-syn spreading.

Does green public procurement trigger environmental innovations?

Abstract: Green public procurement has gained high political priority and is argued to be an effective demand-side policy to trigger environmental innovations. However, the empirical evidence on its innovation impact is limited. We construct a novel firm-level dataset to investigate the effect of winning public procurement tenders with additional environmental award criteria on firms’ introduction of environmental innovations. Employing cross-sectional difference-in-differences methods, we find that winning public procurement awards with environmental selection criteria increases a firm's probability of introducing more environmentally friendly products on average by 20 percentage points. We show that this effect is driven by small and medium-sized firms and is not statistically significant for larger firms. Furthremore, there is no statistically significant effect on the introduction of more environmentally friendly processes.

Genomic and metabolic adaptations of biofilms to ecological windows of opportunity in glacier-fed streams

Abstract: Abstract In glacier-fed streams, ecological windows of opportunity allow complex microbial biofilms to develop and transiently form the basis of the food web, thereby controlling key ecosystem processes. Using metagenome-assembled genomes, we unravel strategies that allow biofilms to seize this opportunity in an ecosystem otherwise characterized by harsh environmental conditions. We observe a diverse microbiome spanning the entire tree of life including a rich virome. Various co-existing energy acquisition pathways point to diverse niches and the exploitation of available resources, likely fostering the establishment of complex biofilms during windows of opportunity. The wide occurrence of rhodopsins, besides chlorophyll, highlights the role of solar energy capture in these biofilms while internal carbon and nutrient cycling between photoautotrophs and heterotrophs may help overcome constraints imposed by oligotrophy in these habitats. Mechanisms potentially protecting bacteria against low temperatures and high UV-radiation are also revealed and the selective pressure of this environment is further highlighted by a phylogenomic analysis differentiating important components of the glacier-fed stream microbiome from other ecosystems. Our findings reveal key genomic underpinnings of adaptive traits contributing to the success of complex biofilms to exploit environmental opportunities in glacier-fed streams, which are now rapidly changing owing to global warming.

BIGDML—Towards accurate quantum machine learning force fields for materials

Abstract: Machine-learning force fields (MLFF) should be accurate, computationally and data efficient, and applicable to molecules, materials, and interfaces thereof. Currently, MLFFs often introduce tradeoffs that restrict their practical applicability to small subsets of chemical space or require exhaustive datasets for training. Here, we introduce the Bravais-Inspired Gradient-Domain Machine Learning (BIGDML) approach and demonstrate its ability to construct reliable force fields using a training set with just 10-200 geometries for materials including pristine and defect-containing 2D and 3D semiconductors and metals, as well as chemisorbed and physisorbed atomic and molecular adsorbates on surfaces. The BIGDML model employs the full relevant symmetry group for a given material, does not assume artificial atom types or localization of atomic interactions and exhibits high data efficiency and state-of-the-art energy accuracies (errors substantially below 1 meV per atom) for an extended set of materials. Extensive path-integral molecular dynamics carried out with BIGDML models demonstrate the counterintuitive localization of benzene--graphene dynamics induced by nuclear quantum effects and allow to rationalize the Arrhenius behavior of hydrogen diffusion coefficient in a Pd crystal for a wide range of temperatures.

DeepPhysics: A physics aware deep learning framework for real‐time simulation

Abstract: Real-time simulation of elastic structures is essential in many applications, from computer-guided surgical interventions to interactive design in mechanical engineering. The finite element method is often used as the numerical method of reference for solving the partial differential equations associated with these problems. Deep learning methods have recently shown that they could represent an alternative strategy to solve physics-based problems. In this article, we propose a solution to simulate hyper-elastic materials using a data-driven approach, where a neural network is trained to learn the nonlinear relationship between boundary conditions and the resulting displacement field. We also introduce a method to guarantee the validity of the solution. In total, we present three contributions: an optimized data set generation algorithm based on modal analysis, a physics-informed loss function, and a hybrid Newton–Raphson algorithm. The method is applied to two benchmarks: a cantilever beam and a propeller. The results show that our network architecture trained with a limited amount of data can predict the displacement field in less than a millisecond. The predictions on various geometries, topologies, mesh resolutions, and boundary conditions are accurate to a few micrometers for nonlinear deformations of several centimeters of amplitude.

Cortex tissue relaxation and slow to medium load rates dependency can be captured by a two-phase flow poroelastic model

Abstract: This paper investigates the complex time-dependent behavior of cortex tissue, under adiabatic condition, using a two-phase flow poroelastic model. Motivated by experiments and Biot's consolidation theory, we tackle time-dependent uniaxial loading, confined and unconfined, with various geometries and loading rates from 1μm/s to 100μm/s. The cortex tissue is modeled as the porous solid saturated by two immiscible fluids, with dynamic viscosities separated by four orders, resulting in two different characteristic times. These are respectively associated to interstitial fluid and glial cells. The partial differential equations system is discretized in space by the finite element method and in time by Euler-implicit scheme. The solution is computed using a monolithic scheme within the open-source computational framework FEniCS. The parameters calibration is based on Sobol sensitivity analysis, which divides them into two groups: the tissue specific group, whose parameters represent general properties, and sample specific group, whose parameters have greater variations. Our results show that the experimental curves can be reproduced without the need to resort to viscous solid effects, by adding an additional fluid phase. Through this process, we aim to present multiphase poromechanics as a promising way to a unified brain tissue modeling framework in a variety of settings.

Inverse deformation analysis: an experimental and numerical assessment using the FEniCS Project

Abstract: Abstract In this paper we develop a framework for solving inverse deformation problems using the FEniCS Project finite-element software. We validate our approach with experimental imaging data acquired from a soft silicone beam under gravity. In contrast with inverse iterative algorithms that require multiple solutions of a standard elasticity problem, the proposed method can compute the undeformed configuration by solving only one modified elasticity problem. This modified problem has a complexity comparable to the standard one. The framework is implemented within an open-source pipeline enabling the direct and inverse deformation simulation directly from imaging data. We use the high-level unified form language (UFL) of the FEniCS Project to express the finite-element model in variational form and to automatically derive the consistent Jacobian. Consequently, the design of the pipeline is flexible: for example, it allows the modification of the constitutive models by changing a single line of code. We include a complete working example showing the inverse deformation of a beam deformed by gravity as supplementary material.

Performance of early warning signals for disease re-emergence: A case study on COVID-19 data

Abstract: Developing measures for rapid and early detection of disease re-emergence is important to perform science-based risk assessment of epidemic threats. In the past few years, several early warning signals (EWS) from complex systems theory have been introduced to detect impending critical transitions and extend the set of indicators. However, it is still debated whether they are generically applicable or potentially sensitive to some dynamical characteristics such as system noise and rates of approach to critical parameter values. Moreover, testing on empirical data has, so far, been limited. Hence, verifying EWS performance remains a challenge. In this study, we tackle this question by analyzing the performance of common EWS, such as increasing variance and autocorrelation, in detecting the emergence of COVID-19 outbreaks in various countries. Our work illustrates that these EWS might be successful in detecting disease emergence when some basic assumptions are satisfied: a slow forcing through the transitions and not-fat-tailed noise. In uncertain cases, we observe that noise properties or commensurable time scales may obscure the expected early warning signals. Overall, our results suggest that EWS can be useful for active monitoring of epidemic dynamics, but that their performance is sensitive to certain features of the underlying dynamics. Our findings thus pave a connection between theoretical and empirical studies, constituting a further step towards the application of EWS indicators for informing public health policies.

Emergent second law for non-equilibrium steady states

Abstract: The Gibbs distribution universally characterizes states of thermal equilibrium. In order to extend the Gibbs distribution to non-equilibrium steady states, one must relate the self-information $\mathcal{I}(x) = -\log(P_\text{ss}(x))$ of microstate $x$ to measurable physical quantities. This is a central problem in non-equilibrium statistical physics. By considering open systems described by stochastic dynamics which become deterministic in the macroscopic limit, we show that changes $\Delta \mathcal{I} = \mathcal{I}(x_t) - \mathcal{I}(x_0)$ in steady state self-information along deterministic trajectories can be bounded by the macroscopic entropy production $\Sigma$. This bound takes the form of an emergent second law $\Sigma + k_b \Delta \mathcal{I}\geq 0$, which contains the usual second law $\Sigma \geq 0$ as a corollary, and is saturated in the linear regime close to equilibrium. We thus obtain a tighter version of the second law of thermodynamics that provides a link between the deterministic relaxation of a system and the non-equilibrium fluctuations at steady state. In addition to its fundamental value, our result leads to novel methods for computing non-equilibrium distributions, providing a deterministic alternative to Gillespie simulations or spectral methods.

Four reasons why data centers matter, five implications of their social spatial distribution, one graphic to visualize them

Abstract: Data centers constitute a new kind of telecommunications infrastructure that demands attention for four reasons. Data centers are under-examined in the social sciences literature, urban studies, in particular. Data centers present an under explored geography of cyberworlds. Large digital corporations such as Amazon or Google are expanding their role in urban infrastructural development (such as data centers), and it is necessary to research and explain this phenomenon. Data centers present challenges of urban governance. The graphic provided here visualizes the social spatial distribution of data centers in the Washington Metropolitan Area. There are five implications of their social spatial distribution. Data centers are concentrated in metropolitan areas. Data centers have a high demand for energy and water, competing with local residents for these resources. The data center industry is a state-led niche economy. The uneven distribution of data centers can invoke inter-county competition for tax revenue, in addition to access to the water, power, and land resources that data centers require. The scale of the problem is unknown because the input needs of many data centers are not publicly available.

Strengthening and toughening of Ti(C,N)-based cermets: (Ti,W)C additive design and the mechanism

Abstract: (Ti0.88W0.12)C solid-solution is characterized by the lowest formation energy and hence it can significantly affect the microstructure and properties of Ti(C,N)-based cermets. This research aims to explore the design principle for (Ti0.88W0.12)C addition amount, and the related strengthening mechanism. 28TiC0.7N0.3–35(Ti0.88W0.12)C–4.5TaC–4.5NbC–8Mo2C–10Ni–10Co (A) and 48TiC0.7N0.3–15(Ti0.88W0.12)C–4.5TaC–4.5NbC–8Mo2C–10Ni–10Co (B) were prepared. On considering the application market expansion, the effect of TiC0.7N0.3/(Ti0.88W0.12)C ratio on the microstructure, mechanical properties and corrosion resistance was investigated. The results show that the hard phase in the two cermets exhibits a preferred orientation of (111) plane. Compared with cermet A, cermet B is characterized by a more uniform microstructure, finer hard phase grains, and 1.23 times higher transverse rupture strength (TRS). Hardness, Palmqvist toughness and TRS of cermet B are 90.5HRA, 12.1 MPa m1/2 and 2616 MPa, respectively. Hardness and toughness of cermet A are 0.4 HRA and 1.06 times higher than that of cermet B, respectively. The results of electrochemical corrosion experiments in H2SO4 (pH = 1) and NaOH (pH = 13) solutions show that cermet B exhibits better corrosion resistance. Both the cermets exhibit excellent resistance to NaOH solution corrosion. The strengthening mechanism driven by TiC0.7N0.3/(Ti0.88W0.12)C ratio is discussed.

Overexpression of PFKFB3 promotes cell glycolysis and proliferation in renal cell carcinoma

Abstract: Cancer cells prefer utilizing aerobic glycolysis in order to exacerbate tumor mass and maintain un-regulated proliferative rates. As a key glycolytic activator, phosphofructo-2-kinase/fructose-2,6-bisphosphatase 3 (PFKFB3) has been implicated in multiple tumor type progression. However, the specific function and clinical significance of PFKFB3 in renal cell carcinoma (RCC) are yet not clarified. This investigation assessed PFKFB3 roles in RCC.PFKFB3 expression levels were analyzed in clear cell renal cell carcinoma (ccRCC) tissues, together with its relationship with clinical characteristics of ccRCC. Real-time PCR and Western blot assays were employed for determining PFKFB3 expression in different RCC cell lines. Furthermore, we determined the glycolytic activity by glucose uptake, lactate secretion assay and ECAR analysis. CCK-8 assay, clone formation, flow cytometry and EdU assessments were performed for monitoring tumor proliferative capacity and cell-cycle distribution. Furthermore, a murine xenograft model was employed for investigating the effect of PFKFB3 on tumor growth in vivo.PFKFB3 was significantly up-regulated in RCC specimens and cell lines in comparison to normal control. Overexpression of PFKFB3 was directly correlated to later TNM stages, thus becoming a robust prognostic biomarker for ccRCC cases. Furthermore, PFKFB3 knockdown suppressed cell glycolysis, proliferative rate and cell-cycle G1/S conversion in RCC cells. Importantly, in vivo experiments confirmed that PFKFB3 knockdown delayed tumor growth derived from the ACHN cell line.Such results suggest that PFKFB3 is a key molecular player in RCC progression via mediating glycolysis / proliferation and provides a potential therapeutic target against RCC.

Origin of Interface Limitation in Zn(O,S)/CuInS₂-Based Solar Cells

Abstract: Copper indium disulfide (CuInS2) grown under Cu-rich conditions exhibits high optical quality but suffers predominantly from charge carrier interface recombination, resulting in poor solar cell performance. An unfavorable "cliff"-like conduction band alignment at the buffer/CuInS2 interface could be a possible cause of enhanced interface recombination in the device. In this work, we exploit direct and inverse photoelectron spectroscopy together with electrical characterization to investigate the cause of interface recombination in chemical bath-deposited Zn(O,S)/co-evaporated CuInS2-based devices. Temperature-dependent current-voltage analyses indeed reveal an activation energy of the dominant charge carrier recombination path, considerably smaller than the absorber bulk band gap, confirming the dominant recombination channel to be present at the Zn(O,S)/CuInS2 interface. However, photoelectron spectroscopy measurements indicate a small (0.1 eV) "spike"-like conduction band offset at the Zn(O,S)/CuInS2 interface, excluding an unfavorable energy-level alignment to be the prominent cause for strong interface recombination. The observed band bending upon interface formation also suggests Fermi-level pinning not to be the main reason, leaving near-interface defects (as recently observed in Cu-rich CuInSe2) as the likely reason for the performance-limiting interface recombination.

The impact of parental migration on psychological well-being of children in Ghana

Abstract: This study is the first to employ panel data to examine the time-varying effects of internal and international parental migration on the psychological well-being of children who stay behind in an African context. The analysis employs data collected in 2013, 2014 and 2015 from school going children aged 12–21 in two urban areas with high out-migration rates in Ghana – Kumasi and Sunyani. Using children’s self-reports, an analysis was conducted separately for boys (N = 781) and girls (N = 705). Results indicate that girls and boys with the mother away internally or internationally are equally or more likely to have higher levels of psychological well-being when compared to boys and girls of non-migrant parents. A higher level of well-being is observed amongst girls when parents migrate and divorce. However, parental migration and divorce are more likely to increase the psychological vulnerability of boys. In Ghana, the psychological well-being of children is nuanced by which parent has migrated, marital status of migrant parent and the gender of the child.

Quantum collisional thermostats

Abstract: Abstract Collisional reservoirs are becoming a major tool for modelling open quantum systems. In their simplest implementation, an external agent switches on, for a given time, the interaction between the system and a specimen from the reservoir. Generically, in this operation the external agent performs work onto the system, preventing thermalization when the reservoir is at equilibrium. One can recover thermalization by considering an autonomous global setup where the reservoir particles colliding with the system possess a kinetic degree of freedom. The drawback is that the corresponding scattering problem is rather involved. Here, we present a formal solution of the problem in one dimension and for flat interaction potentials. The solution is based on the transfer matrix formalism and allows one to explore the symmetries of the resulting scattering map. One of these symmetries is micro-reversibility, which is a condition for thermalization. We then introduce two approximations of the scattering map that preserve these symmetries and, consequently, thermalize the system. These relatively simple approximate solutions constitute models of quantum thermostats and are useful tools to study quantum systems in contact with thermal baths. We illustrate their accuracy in a specific example, showing that both are good approximations of the exact scattering problem even in situations far from equilibrium. Moreover, one of the models consists of the removal of certain coherences plus a very specific randomization of the interaction time. These two features allow one to identify as heat the energy transfer due to switching on and off the interaction. Our results prompt the fundamental question of how to distinguish between heat and work from the statistical properties of the exchange of energy between a system and its surroundings.

Complex, but in a good way? How to represent encryption to non-experts through text and visuals – Evidence from expert co-creation and a vignette experiment

Abstract: An ongoing discussion in the field of usable privacy and security debates whether security mechanisms should be visible to end-users during interactions with technology, or hidden away. This paper addresses this question using a mixed-methods approach, focusing on encryption as a mechanism for confidentiality during data transmission on a smartphone application. In study 1, we conducted a qualitative co-creation study with security and Human-Computer Interaction (HCI) experts (N = 9) to create appropriate textual and visual representations of the security mechanism encryption in data transmission. We investigated this question in two contexts: online banking and e-voting. In study 2, we put these ideas to the test by presenting these visual and textual representations to non-expert users in an online vignette experiment (N = 2180). We found a statistically significant and positive effect of the textual representation of encryption on perceived security and understanding, but not on user experience (UX). More complex text describing encryption resulted in higher perceived security and more accurate understanding. The visual representation of encryption had no statistically significant effect on perceived security, UX or understanding. Our study contributes to the larger discussion regarding visible instances of security and their impact on user perceptions.

Molecular Interactions Induced by a Static Electric Field in Quantum Mechanics and Quantum Electrodynamics

Abstract: By means of quantum mechanics and quantum electrodynamics applied to coupled harmonic Drude oscillators, we study the interaction between two neutral atoms or molecules subject to a uniform static electric field. Our focus is to understand the interplay between leading contributions to field-induced electrostatics/polarization and dispersion interactions, as considered within the employed Drude model for both non-retarded and retarded regimes. For the first case, we present an exact solution for two coupled oscillators obtained by diagonalizing the corresponding quantum-mechanical Hamiltonian and demonstrate that the external field can control the strength of different intermolecular interactions and relative orientations of the molecules. In the retarded regime described by quantum electrodynamics, our analysis shows that field-induced electrostatic and polarization energies remain unchanged (in isotropic and homogeneous vacuum) compared to the non-retarded case. For interacting species modeled by quantum Drude oscillators, the developed framework based on quantum mechanics and quantum electrodynamics yields the leading contributions to molecular interactions under the combined action of external and vacuum fields.

Mobile robots path planning and mobile multirobots control: A review

Abstract: Abstract Mobile robots and multimobile robotic system usage for task achievement have been an emerging research area since the last decades. This article presents a review about mobile robot navigation problem and multimobile robotic systems control. The main focus is made on path planning strategies and algorithms in static and dynamic environments. A classification on mobile robots path planning has been defined in the literature and divided to classical and heuristic approaches. Each of them has its own advantages and drawbacks. On the other hand, the control of multimobile robots is presented and the control approaches for a fleet of robots are presented. Scientists found that using more than one robot as opposed to a single one presents many advantages when considering redundant task, dangerous tasks, or a task that scales up or down in time or that requires flexibility. They have defined three main approaches of multiple robots control: behavior-based approach, leader–follower approach, and virtual structure approach. This article addresses these approaches and provides examples from the literature.