This book deals with density, temperature, velocity and concentration fluctuations in fluids and fluid mixtures. The book first reviews thermal fluctuations in equilibrium fluids on the basis of ...fluctuating hydrodynamics. It then shows how the method of fluctuating hydrodynamics can be extended to deal with hydrodynamic fluctuations when the system is in a stationary nonequilibrium state. In contrast to equilibrium fluids where the fluctuations are generally short ranged unless the system is close to a critical point, fluctuations in nonequilibrium fluids are always long-ranged encompassing the entire system. The book provides the first comprehensive treatment of fluctuations in fluids and fluid mixtures brought out of equilibrium by the imposition of a temperature and concentration gradient but that are still in a macroscopically quiescent state. By incorporating appropriate boundary conditions in the case of fluid layers, it is shown how fluctuating hydrodynamics affects the fluctuations close to the onset of convection. Experimental techniques of light scattering and shadowgraphy for measuring nonequilibrium fluctuations are elucidated and the experimental results thus far reported in the literature are reviewed.· Systematic exposition of fluctuating hydrodynamics and its applications · First book on nonequilibrium fluctuations in fluids · Fluctuating Boussinesq equations and nonequilibrium fluids · Fluid layers and onset of convection · Rayleigh scattering and Brillouin scattering in fluids · Shadowgraph technique for measuring fluctuations · Fluctuations near hydrodynamic instabilities
Data-driven computational mechanics Kirchdoerfer, T.; Ortiz, M.
Computer methods in applied mechanics and engineering,
06/2016, Letnik:
304
Journal Article
Recenzirano
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We develop a new computing paradigm, which we refer to as data-driven computing, according to which calculations are carried out directly from experimental material data and pertinent constraints and ...conservation laws, such as compatibility and equilibrium, thus bypassing the empirical material modeling step of conventional computing altogether. Data-driven solvers seek to assign to each material point the state from a prespecified data set that is closest to satisfying the conservation laws. Equivalently, data-driven solvers aim to find the state satisfying the conservation laws that is closest to the data set. The resulting data-driven problem thus consists of the minimization of a distance function to the data set in phase space subject to constraints introduced by the conservation laws. We motivate the data-driven paradigm and investigate the performance of data-driven solvers by means of two examples of application, namely, the static equilibrium of nonlinear three-dimensional trusses and linear elasticity. In these tests, the data-driven solvers exhibit good convergence properties both with respect to the number of data points and with regard to local data assignment. The variational structure of the data-driven problem also renders it amenable to analysis. We show that, as the data set approximates increasingly closely a classical material law in phase space, the data-driven solutions converge to the classical solution. We also illustrate the robustness of data-driven solvers with respect to spatial discretization. In particular, we show that the data-driven solutions of finite-element discretizations of linear elasticity converge jointly with respect to mesh size and approximation by the data set.
Data‐driven computing in dynamics Kirchdoerfer, T.; Ortiz, M.
International journal for numerical methods in engineering,
16 March 2018, Letnik:
113, Številka:
11
Journal Article
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Summary
We formulate extensions to data‐riven computing for both distance‐minimizing and entropy‐maximizing schemes to incorporate time integration. Previous works focused on formulating both types ...of solvers in the presence of static equilibrium constraints. Here, formulations assign data points to a variable relevance depending on distance to the solution and on maximum‐entropy weighting, with distance‐minimizing schemes discussed as a special case. The resulting schemes consist of the minimization of a suitably defined free energy over phase space subject to compatibility and a time‐discretized momentum conservation constraint. We present selected numerical tests that establish the convergence properties of both types of data‐driven solvers and solutions.
Dysfunction of cell bioenergetics is a common feature of neurodegenerative diseases, the most common of which is Alzheimer's disease (AD). Disrupted energy utilization implicates mitochondria at its ...nexus. This review summarizes some of the evidence that points to faulty mitochondrial function in AD and highlights past and current therapeutic development efforts. Classical neuropathological hallmarks of disease (β‐amyloid and τ) and sporadic AD risk genes (APOE) may trigger mitochondrial disturbance, yet mitochondrial dysfunction may incite pathology. Preclinical and clinical efforts have overwhelmingly centred on the amyloid pathway, but clinical trials have yet to reveal clear‐cut benefits. AD therapies aimed at mitochondrial dysfunction are few and concentrate on reversing oxidative stress and cell death pathways. Novel research efforts aimed at boosting mitochondrial and bioenergetic function offer an alternative treatment strategy. Enhancing cell bioenergetics in preclinical models may yield widespread favourable effects that could benefit persons with AD.
Linked Articles
This article is part of a themed section on Therapeutics for Dementia and Alzheimer's Disease: New Directions for Precision Medicine. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v176.18/issuetoc
We formulate a Data Driven Computing paradigm, termed max-ent Data Driven Computing, that generalizes distance-minimizing Data Driven Computing and is robust with respect to outliers. Robustness is ...achieved by means of clustering analysis. Specifically, we assign data points a variable relevance depending on distance to the solution and on maximum-entropy estimation. The resulting scheme consists of the minimization of a suitably-defined free energy over phase space subject to compatibility and equilibrium constraints. Distance-minimizing Data Driven schemes are recovered in the limit of zero temperature. We present selected numerical tests that establish the convergence properties of the max-ent Data Driven solvers and solutions.
We leverage the phase segregated microstructure of polyurea to study its shock response using molecular dynamics (MD) simulation. The two phase segregated domains, the hard and the soft domains, are ...investigated separately. The shock response of the domains is studied using a multiscale shock-simulation approach that allows simulation of low pressure shocks. Both domains exhibit an unconventional behavior at low shock velocities that is typically associated with polymers. The shock response of the hard domain is marked by energy dissipation due to hydrogen bond breaking. Moreover, the radial distribution function suggests a severe distortion in the ring structure of aromatic moieties in the hard domain at high shock pressure. Finally, the shock Hugoniot of polyurea, obtained by combining the response of the two domains using a mixing rule, shows excellent match with experimental data.
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•Shock response of the phase segregated domains have been studied independently.•The response of polyurea is obtained by combining the responses of the two domains.•Computed shock Hugoniot of polyurea shows excellent agreement with experiments.•Hard domain mitigates shock by hydrogen bond breaking and aromatic ring distortion.
Summary
Background
Strict avoidance is the only accepted management for cow's milk (CM) allergy. CM oral immunotherapy (CM‐OIT) is under investigation.
Objectives
To evaluate long‐term safety of ...CM‐OIT. To identify clinical/immunological predictors of adverse events.
Methods
Prospective longitudinal epidemiological intervention study. CM‐allergic children aged 5–18 underwent a Spanish‐approved CM‐OIT protocol without premedication. Clinical data, skin prick test (SPT) and specific IgE (sIgE) at baseline and 1 year after OIT were registered. All dose‐related reactions, treatments needed and cofactors involved were recorded. Through survival analysis, we studied the cumulative probability of reactions resolution over time and clinical/immunological risk factors of reactions persistence.
Results
81 children were recruited. Mean follow‐up was 25 months. 95% of children suffered reactions, 91% of which affected a single organ. Reactions were heterogeneously distributed: (a) 60 children (75%) had occasional symptoms which ceased over time. 86% of them reached complete desensitization (200 mL). (b) 20 children (25%) suffered frequent (78% of total reactions), more severe and unpredictable reactions, which persisted during follow‐up or led to withdrawal (6 cases). Reactions persistence was associated with a higher frequency and severity. Kaplan–Meier estimate revealed a cumulative probability of reactions resolution of 25% at 3 months (95% CI: 1.9–4.1) and 50% (95% CI: 6.1–9.9) at 8 months based on all patients. Cox proportional hazards multivariate regression model identified 3 variables (CM‐sIgE ≥ 50 KU L−1, CM‐SPT ≥ 9 mm and Sampson's severity grades 2, 3 and 4 at baseline food challenge) as independent risk factors of reactions persistence. The combination of 2 or 3 of these factors involved hazard ratios to develop persistent reactions of 2.26 (95% CI: 1.14–4.46; P = 0.019) and 6.06 (95% CI: 2.7–13.7; P < 0.001), respectively.
Clinical implications
CM‐OIT was insufficiently safe in 25% of children. The above‐mentioned clinical and immunological parameters would help clinicians to identify highly reactive patients before CM‐OIT. In them, individualized schedules and premedication should be considered.
High-throughput sequencing is helping biologists to overcome the difficulties of inferring the phylogenies of recently diverged taxa. The present study analyzes the phylogenetic signal of genomic ...regions with different inheritance patterns using genome skimming and ddRAD-seq in a species-rich Andean genus (Diplostephium) and its allies.
We analyzed the complete nuclear ribosomal cistron, the complete chloroplast genome, a partial mitochondrial genome, and a nuclear-ddRAD matrix separately with phylogenetic methods. We applied several approaches to understand the causes of incongruence among datasets, including simulations and the detection of introgression using the D-statistic (ABBA-BABA test).
We found significant incongruence among the nuclear, chloroplast, and mitochondrial phylogenies. The strong signal of hybridization found by simulations and the D-statistic among genera and inside the main clades of Diplostephium indicate reticulate evolution as a main cause of phylogenetic incongruence.
Our results add evidence for a major role of reticulate evolution in events of rapid diversification. Hybridization and introgression confound chloroplast and mitochondrial phylogenies in relation to the species tree as a result of the uniparental inheritance of these genomic regions. Practical implications regarding the prevalence of hybridization are discussed in relation to the phylogenetic method.
The prospects and potential of Reverse Electrodialysis (RED) for energy harvesting from natural streams with salinity gradient demand more in-depth studies to understand and overcome the limitations ...posed by divalent ions. Power performance is greatly influenced by the ionic resistance displayed by the alternating cation and anion exchange membranes (CEMs and AEMs, respectively) housed in RED stacks, which in turn is determined by the type and concentration of ions and counter-ions in the water streams. The effects of divalent ions on power output have been experimentally approached in several works by using real or synthetic water. However, the development of comprehensive models including the effect of divalent ions on membrane resistance and power performance under different scenarios is still very scarce. Thus, this work investigates experimentally the effect of ion species on membrane resistance, providing for the first time mathematical correlations useful to predict power performance in RED stacks under a wide range of compositions of salinity gradient solutions. To this end, electrochemical impedance spectroscopy (EIS) measurements have been performed for CEM and AEM commercial membranes in contact with different concentration of NaCl solutions and including different mixtures of divalent ions (Ca2+, Mg2+, SO42−). These correlations have been implemented in a previously developed model to determine power outputs as function of ion mixture compositions. Scenarios of general interest for RED practical implementation have been addressed; specifically, solutions with a composition representative of seawater or high salinity brines have been studied as high concentration solutions (HCS) and, on the other hand, typical concentrations of wastewater treatment plant effluents, river water or brackish water from desalination plants were used as low concentration solutions (LCS).
•Modelling the effect of divalent ions on Reverse Electrodialysis performance.•Influence of divalent ions on cation and anion exchange membrane resistances.•Assessment of real water stream ion concentrations for SGE recovery.•Power reduction of 16.3% in real scenarios in comparison with pure NaCl solutions.
Model-Free Data-Driven inelasticity Eggersmann, R.; Kirchdoerfer, T.; Reese, S. ...
Computer methods in applied mechanics and engineering,
06/2019, Letnik:
350
Journal Article
Recenzirano
Odprti dostop
We extend the Data-Driven formulation of problems in elasticity of Kirchdoerfer and Ortiz (2016) to inelasticity. This extension differs fundamentally from Data-Driven problems in elasticity in that ...the material data set evolves in time as a consequence of the history dependence of the material. We investigate three representational paradigms for the evolving material data sets: (i) materials with memory, i. e., conditioning the material data set to the past history of deformation; (ii) differential materials, i. e., conditioning the material data set to short histories of stress and strain; and (iii) history variables, i. e., conditioning the material data set to ad hoc variables encoding partial information about the history of stress and strain. We also consider combinations of the three paradigms thereof and investigate their ability to represent the evolving data sets of different classes of inelastic materials, including viscoelasticity, viscoplasticity and plasticity. We present selected numerical examples that demonstrate the range and scope of Data-Driven inelasticity and the numerical performance of implementations thereof.
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