A Double Resonance Method for Studying Collision Rates in an Intense Laser Field

Abstract

We propose a method for studying the modification of atomic collision rates in the presence of an intense laser field. This method employs a three-level lambda-system driven by two laser pulses: one strong and the other weak. The weak laser is used to populate a particular dressed state produced by the intense laser at some time during the pulse history. We show that this point can be selected by tuning the frequency of the weak laser. The spectrum of the emission produced by the dressed state is then recorded. Measuring this spectrum, both in the presence and in the absence of collisions, enables one to deduce the dependence of the collision rates on laser intensity. The method has other advantages discussed in the text.     

Anelastic reorganisation of fibre-reinforced biological tissues

In this work, we contribute to the study of the structural reorganisation of biological tissues in response to mechanical stimuli. We specialise our investigation to a class of hydrated soft tissues, whose internal structure features reinforcing fibres. These are oriented statistically within the tissue, and their pattern of orientation is such that, at each material point, the tissue is anisotropic. From its natural, stress-free state, the tissue can be distorted anelastically into a global reference configuration, and then deformed under the action of external mechanical loads. The anelastic distortions are responsible for changing irreversibly the internal structure of the tissue, which, in the present context, occurs through both the rearrangement of the bonds among the tissue cells and the deformation-driven reorientation of the fibres. The anelastic strains, in addition, are assumed to model the onset and evolution of microcracks in the tissue, which may be triggered by the mechanical loads applied to the tissue in the case of traumatic events, or diseases. For our purposes, we formulate an anisotropic model of remodelling and we consider a fully isotropic model of structural reorganisation for comparison, with the aim to study if, how, and to what extent the evolution of anelastic distortions is influenced by the tissue’s anisotropy.

     

Double Mutant Cycles as a Tool to Address Folding,Binding, and Allostery

Quantitative measurement of intramolecular and intermolecular interactions in protein structure is an elusive task, not easy to address experimentally. The phenomenon denoted ‘energetic coupling’ describes short- and long-range interactions between two residues in a protein system. A powerful method to identify and quantitatively characterize long-range interactions and allosteric networks in proteins or protein–ligand complexes is called double-mutant cycles analysis. In this review we describe the thermodynamic principles and basic equations that underlie the double mutant cycle methodology, its fields of application and latest employments, and caveats and pitfalls that the experimentalists must consider. In particular, we show how double mutant cycles can be a powerful tool to investigate allosteric mechanisms in protein binding reactions as well as elusive states in protein folding pathways.     

Potential to retrofit existing small-scale gasifiers through steam gasification of biomass residues for hydrogen and biofuels production

This work investigates the opportunity of retrofitting existing small-scale gasifiers shifting from combined heat and power (CHP) to hydrogen and biofuels production, using steam and biomass residues (woodchips, vineyard pruning and bark). The experiments were carried out in a batch reactor at 700 °C and 800 °C and at different steam flow (SF) rates (0.04 g/min and 0.20 g/min). The composition of the producer gas is in the range of 46–70 % H₂, 9–29 % CO, 12–27 % CO₂, and 2–6 % CH₄. A producer gas specific production factor of approx. 10 NL_pg/g_char can be achieved when the lower SFs are used, which allows to provide 80 % of the hydrogen concentration required for biomethanation and MeOH synthesis. As for FT synthesis, an optimal H₂/CO ratio of approx. 2 can be achieved. The results of this work provide further evidence towards the feasibility of hydrogen and biofuels generation from residual biomass through steam gasification.     

Dependency solving: A separate concern in component evolution management

Maintenance of component-based software platforms often has to face rapid evolution of software components. Component dependencies, conflicts, and package managers with dependency solving capabilities are the key ingredients of prevalent software maintenance technologies that have been proposed to keep software installations synchronized with evolving component repositories. We review state-of-the-art package managers and their ability to keep up with evolution at the current growth rate of popular component-based platforms, and conclude that their dependency solving abilities are not up to the task.We show that the complexity of the underlying upgrade planning problem is NP-complete even for seemingly simple component models, and argue that the principal source of complexity lies in multiple available versions of components. We then discuss the need of expressive languages for user preferences, which makes the problem even more challenging.We propose to establish dependency solving as a separate concern from other upgrade aspects, and present CUDF as a formalism to describe upgrade scenarios. By analyzing the result of an international dependency solving competition, we provide evidence that the proposed approach is viable.     

Advanced Modeling of Food Convective Drying: A Comparison Between Artificial Neural Networks and Hybrid Approaches

In the present paper, three different approaches are proposed to model the convective drying of food. The performance of thin-layer, pure neural network and hybrid neural model is compared in a wide range of operating conditions, with two different vegetables, available either as cylinders or as slabs with different characteristic dimensions. It was found that the thin-layer model was adequate to describe food drying behavior, but it could be applied only as a fitting procedure. Pure neural models gave accurate predictions in some situations, but exhibited poor performance when tested outside the range of operating conditions exploited during their development. Finally, it was shown that hybrid neural models, formulated as a combination of both theoretical and neural network models, are capable of offering the most accurate predictions of system behavior with average relative errors never exceeding 10%, even in operating conditions unexploited during the definition of the neural part of the model. The results obtained proved that the hybrid neural paradigm is a novel and efficient modeling technique that could be used successfully in food processing, thus allowing drying process optimization to be achieved, and efficient and fast on-line controllers to be implemented.     

Controlled suppression of the photoluminescence superlinear dependence on excitation density in quantum dots

We have shown that it is possible to tune, up to complete suppression, the photoluminescence superlinear dependence on the excitation density in quantum dot samples at high temperatures by annealing treatments. The effect has been attributed to the reduction of the defectivity of the material induced by annealing.