Designed metallopeptides as tools in Chemical Biology

A series of Ru(II) metallopeptides featuring the dipyrido[3,2-a:2',3'-c]phenazine ligand (dppz) have been synthesized through solid-phase peptide synthesis (SPPS) methods. Spectroscopic studies indicate that functionalization with an oligoarginine basic tail causes a drastic change in the binding mode and a great increase of the affinity of these metallopeptides for well-matched B-DNA oligonucleotides over mismacthed DNAs. In addition, the spectroscopic data suggest that both the oligoarginine functionalization and the nature of the ancillary ligands do not cause an increase of the selectivity of these metallopeptides for a particular type of mismatched DNA oligonucleotide. Finally, fluorescence microscopy studies indicate that the oligoarginine functionalization causes the efficient internalization of the metallopeptides into Vero cells, causing apoptotic cell death after a few hours

Geometrical aspects in the analysis of microcanonical phase-transitions

In the present work, we discuss how the functional form of thermodynamic observables can be deduced from the geometric properties of subsets of phase space. The geometric quantities taken into account are mainly extrinsic curvatures of the energy level sets of the Hamiltonian of a system under investigation. In particular, it turns out that peculiar behaviours of thermodynamic observables at a phase transition point are rooted in more fundamental changes of the geometry of the energy level sets in phase space. More specifically, we discuss how microcanonical and geometrical descriptions of phase-transitions are shaped in the special case of $\phi^4$ models with either nearest-neighbours and mean-field interactions

The Berezinskii Kosterlitz Thouless phase transition is of second-order in the microcanonical ensemble

A paradigmatic example of a phase transition taking place in the absence of symmetry-breaking is provided by the Berezinkii-Kosterlitz-Thouless (BKT) transition in the two-dimensional XY model. In the framework of canonical ensemble, this phase transition is defined as an infinite-order one. To the contrary, by tackling the transitional behavior of the two dimensional XY model in the microcanonical ensemble, we show that the BKT phase transition is of second order. This provides a new example of statistical ensemble inequivalence that could apply to a broad class of systems undergoing BKT phase transitions

BurnoutEnsemble: Augmented Intelligence to Detect Indications for Burnout in Clinical Psychology

Burnout, a state of emotional, physical, and mental exhaustion caused by excessive and prolonged stress, is a growing concern. It is known to occur when an individual feels overwhelmed, emotionally exhausted, and unable to meet the constant demands imposed upon them. Detecting burnout is not an easy task, in large part because symptoms can overlap with those of other illnesses or syndromes. The use of natural language processing (NLP) methods has the potential to mitigate the limitations of typical burnout detection via inventories. In this article, the performance of NLP methods on anonymized free text data samples collected from the online forum/social media platform Reddit was analyzed. A dataset consisting of 13,568 samples describing first-hand experiences, of which 352 are related to burnout and 979 to depression, was compiled. This work demonstrates the effectiveness of NLP and machine learning methods in detecting indicators for burnout. Finally, it improves upon standard baseline classifiers by building and training an ensemble classifier using two methods (subreddit and random batching). The best ensemble models attain a balanced accuracy of 0.93, test F1 score of 0.43, and test recall of 0.93. Both the subreddit and random batching ensembles outperform the single classifier baselines in the experimental setup

Entanglement distance for an arbitrary state of M qubits

We propose a measure of entanglement that can be computed for any pure state of an $M$-qubit system. The entanglement measure has the form of a distance that we derive from an adapted application of the Fubini-Study metric. This measure is invariant under local unitary transformations and defined as trace of a suitable metric that we derive, the entanglement metric $\tilde{g}$. Furthermore, the analysis of the eigenvalues of $\tilde{g}$ gives information about the robustness of entanglement.Comment: 6 pages, 5 figure

Entanglement and quantum correlation measures for quantum multipartite mixed states

Entanglement, and quantum correlation, are precious resources for quantum technologies implementation based on quantum information science, such as quantum communication, quantum computing, and quantum interferometry. Nevertheless, to our best knowledge, a directly or numerically computable measure for the entanglement of multipartite mixed states is still lacking. In this work, (i) we derive a measure of the degree of quantum correlation for mixed multipartite states. The latter possesses a closed-form expression valid in the general case unlike, to our best knowledge, all other known measures of quantum correlation. (ii) We further propose an entanglement measure, derived from this quantum correlation measure using a novel regularization procedure for the density matrix. Therefore, a comparison of the proposed measures, of quantum correlation and entanglement, allows one to distinguish between quantum correlation detached from entanglement and the one induced by entanglement and, hence, to identify separable but non-classical states. We have tested our quantum correlation and entanglement measures, on states well-known in literature: a general Bell diagonal state and the Werner states, which are easily tractable with our regularization procedure, and we have verifed the accordance between our measures and the expected results for these states. Finally, we validate the two measures in two cases of multipartite states. The frst is a generalization to three qubits of the Werner state, the second is a one-parameter three qubits mixed state interpolating between a bi-separable state and a genuine multipartite state, passing through a fully separable state

Modélisation numérique des écoulements convectifs de nanofluides en régimes laminaire et turbulent

Les transferts de chaleur par convection jouent un rôle important dans divers secteurs industriels tels que la climatisation, le transport, la production chimique, la microélectronique et la production d’électricité. Les fluides caloporteurs conventionnels tels que l’eau, l’éthylène glycol et l’huile sont caractérisés par des propriétés thermiques relativement limitées, ce qui réduit l’efficacité des systémes thermiques mis en jeu. L’avancée récente dans le domaine des nanotechnologies a donné naissance à un nouveau type de particules métalliques, et non métalliques, de tailles nanométriques, caractérisées par une conductivité thermique trés élevée. Ces particules, appelées nanoparticules, sont généralement dispersées dans un fluide de base et le mélange résultant constitue une nouvelle classe de fluides caloporteurs nommés nanofluides. Le domaine des nanofluides est un champ de recherche très vivant et leur application dans les processus industriels devient de plus en plus répandue pour leurs remarquables propriétés optiques, magnétiques, diélectriques ou électromagnétiques. Dans le présent projet, seules les performances thermiques des nanofluides seront abordées. Les nanofluides ont montré leur capacité à modifier les propriétés de transport et de transfert de chaleur du fluide de base, ce qui constitue un grand potentiel d’amélioration pour les processus de transfert de chaleur. Cependant, bien que l’ajout de nanoparticules solides aux fluides de base augmente leur conductivité thermique, cela s’accompagne d’une diminution de leur capacité calorifique et d’une augmentation de leur viscosité. Ceci entraine une augmentation de la puissance de pompage requise. Les coûts de production des nanoparticules et la difficulté à préparer des nanofluides stables dans le temps rendent, pour l’instant, l’application des nanofluides dans l’industrie encore limitée. Dans ce contexte, l’objectif principal de ce projet de recherche est d’évaluer en détail les caractéristiques d’écoulements de nanofluides et les paramètres clés affectant leur performance dans le processus de transfert de chaleur. Pour ce faire, des modèles numériques ont été développés puis validés soigneusement avec des données issues de la littérature pour des écoulements convectifs en régimes laminaire et turbulent. Bien que les configurations choisies soient relativement canoniques, elles permettent d’évaluer les possibles avantages des nanofluides dans des systèmes thermiques industriels et d’étudier l’influence des principaux paramètres de contrôle, comme le débit d’entrée et la fraction en nanoparticules entre autres.Abstract: Convective heat transfer plays an important role in various industrial sectors such as airconditioning, transportation, chemical production, microelectronics or power generation. Conventional heat transfer fluids such as water, ethylene glycol or oil exhibit relatively limited heat transfer properties, which hinders the efficiency of thermal systems. The recent advances in the field of nanotechnology gave rise to a new class of nanometeric metallic and non-metallic particles characterized by their substantially higher thermal conductivities. These particles, referred as nanoparticles, are dispersed into a conventional fluid, creating a new class of heat transfer fluids named nanofluids. The study of nanofluids is a viable research field and their application in various industrial processes becomes more widespread due to their thermal, optical, magnetic, and electromagnetic properties. In the present study, only the thermal efficiency of nanofluids will be investigated. Nanofluids have shown their ability to enhance the heat transfer performances of the host fluid, which constitutes a great potential to increase the energetic efficiency of thermal systems. However, adding solid nanoparticles to a base fluid would not only increase its thermal conductivity but, it is also accompanied with a decrease of its heat capacity and an increase of its dynamic viscosity, which may lead to an increased required pumping power. The two main drawbacks of nanofluids, which limit their use in industrial systems remain the prohibitive cost to produce nanoparticles and the difficulty to prepare and stabilize nanofluids over a wide life cycle. In this context the main objective of this research project is to study in detail the nanofluid flow characteristics and the key parameters affecting their performance in heat transfer process. To this end, Computational Fluid Dynamics techniques are used to propose a numerical model able to simulate nanofluid flows taking into account several phenomena due to the presence of the nanoparticles into a base fluid and then evaluate the benefits from their using in industrial applications

Entanglement and Quantum Correlation Measures from a Minimum Distance Principle

Entanglement, and quantum correlation, are precious resources for quantum technologies implementation based on quantum information science, such as, for instance, quantum communication, quantum computing, and quantum interferometry. Nevertheless, to our best knowledge, a directly computable measure for the entanglement of multipartite mixed-states is still lacking. In this work, {\it i)} we derive from a minimum distance principle, an explicit measure able to quantify the degree of quantum correlation for pure or mixed multipartite states; {\it ii)} through a regularization process of the density matrix, we derive an entanglement measure from such quantum correlation measure; {\it iii)} we prove that our entanglement measure is \textit{faithful} in the sense that it vanishes only on the set of separable states. Then, a comparison of the proposed measures, of quantum correlation and entanglement, allows one to distinguish between quantum correlation detached from entanglement and the one induced by entanglement, hence to define the set of separable but non-classical states. Since all the relevant quantities in our approach, descend from the geometry structure of the projective Hilbert space, the proposed method is of general application. Finally, we apply the derived measures as an example to a general Bell diagonal state and to the Werner states, for which our regularization procedure is easily tractable.Comment: 7 Pages, 3 Figure

BFH-AMI at eRisk@ CLEF 2023

Mental health problems are a rising problem of today’s society. Methods of machine learning and natural language processing provide interesting new possibilities for psychology and psychiatry. In particular, eating disorders (ED) are widespread and can be life-threatening if untreated. This paper describes the approach to Task 3 of the eRisk 2023 challenge of the BFH-AMI team. The task concerned the prediction of patients’ answers to the Eating Disorder Examination Questionnaire (EDE-Q) based on their social media writing history. In our approach, we used a logistic regression model that was fed with a combination of user and question embeddings from the GPT-2 Large model