A thermodynamically consistent Markovian master equation beyond the secular approximation

Markovian master equations provide a versatile tool for describing open quantum systems when memory effects of the environment may be neglected. As these equations are of an approximate nature, they often do not respect the laws of thermodynamics when no secular approximation is performed in their derivation. Here we introduce a Markovian master equation that is thermodynamically consistent and provides an accurate description whenever memory effects can be neglected. The thermodynamic consistency is obtained through a rescaled Hamiltonian for the thermodynamic bookkeeping, exploiting the fact that a Markovian description implies a limited resolution for heat. Our results enable a thermodynamically consistent description of a variety of systems where the secular approximation breaks down

Violating the thermodynamic uncertainty relation in the three-level maser

Nanoscale heat engines are subject to large fluctuations which affect their precision. The thermodynamic uncertainty relation (TUR) provides a trade-off between output power, fluctuations, and entropic cost. This trade-off may be overcome by systems exhibiting quantum coherence. This Letter provides a study of the TUR in a prototypical quantum heat engine, the Scovil–Schulz-DuBois maser. Comparison with a classical reference system allows us to determine the effect of quantum coherence on the performance of the heat engine. We identify analytically regions where coherence suppresses fluctuations, implying a quantum advantage, as well as regions where fluctuations are enhanced by coherence. This quantum effect cannot be anticipated from the off-diagonal elements of the density matrix. Because the fluctuations are not encoded in the steady state alone, TUR violations are a consequence of coherence that goes beyond steady-state coherence. While the system violates the conventional TUR, it adheres to a recent formulation of a quantum TUR. We further show that parameters where the engine operates close to the conventional limit are prevalent and TUR violations in the quantum model are not uncommon

Microscopic co-existence of superconductivity and magnetism in Ba1-xKxFe2As2

It is widely believed that, in contrast to its electron doped counterparts, the hole doped compound Ba1-xKxFe2As2 exhibits a mesoscopic phase separation of magnetism and superconductivity in the underdoped region of the phase diagram. Here, we report a combined high-resolution x-ray powder diffraction and volume sensitive muon spin rotation study of underdoped Ba1-xKxFe2As2 (0 \leq x \leq 0.25) showing that this paradigm is wrong. Instead we find a microscopic coexistence of the two forms of order. A competition of magnetism and superconductivity is evident from a significant reduction of the magnetic moment and a concomitant decrease of the magneto-elastically coupled orthorhombic lattice distortion below the superconducting phase transition.Comment: 4 pages, 4 figure

Computationally motivated synthesis and enzyme kinetic evaluation of N-(β-d-glucopyranosyl)-1,2,4-triazolecarboxamides as glycogen phosphorylase inhibitors

Following our recent study of N-(β-D-glucopyranosyl)-oxadiazole-carboxamides (Polyák et al., Biorg. Med. Chem. 2013, 21, 5738) revealed as moderate inhibitors of glycogen phosphorylase (GP), in silico docking calculations using Glide have been performed on N-(β-D-glucopyranosyl)-1,2,4-triazolecarboxamides with different aryl substituents predicting more favorable binding at GP. The ligands were subsequently synthesized in moderate yields using N-(2,3,4,6-terta-O-acetyl-β-D-glucopyranosyl)-tetrazole-5-carboxamide as starting material. Kinetics experiments against rabbit muscle glycogen phosphorylase b (RMGPb) revealed the ligands to be low µM GP inhibitors; the phenyl analogue (Ki = 1 µM) is one of the most potent N-(β-D-glucopyranosyl)-heteroaryl-carboxamide-type inhibitors of the GP catalytic site discovered to date. Based on QM and QM/MM calculations, the potency of the ligands is predicted to arise from favorable intra- and intermolecular hydrogen bonds formed by the most stable solution phase tautomeric (t2) state of the 1,2,4-triazole in a conformationally dynamic system. ADMET property predictions revealed the compounds to have promising pharmacokinetic properties without any toxicity. This study highlights the benefits of a computationally lead approach to GP inhibitor design

BIMAM—a tool for imputing variables missing across datasets using a Bayesian imputation and analysis model

Motivation Combination of multiple datasets is routine in modern epidemiology. However, studies may have measured different sets of variables; this is often inefficiently dealt with by excluding studies or dropping variables. Multilevel multiple imputation methods to impute these ‘systematically’ missing data (as opposed to ‘sporadically’ missing data within a study) are available, but problems may arise when many random effects are needed to allow for heterogeneity across studies. We show that the Bayesian IMputation and Analysis Model (BIMAM) implemented in our tool works well in this situation. General features BIMAM performs imputation and analysis simultaneously. It imputes both binary and continuous systematically and sporadically missing data, and analyses binary and continuous outcomes. BIMAM is a user-friendly, freely available tool that does not require knowledge of Bayesian methods. BIMAM is an R Shiny application. It is downloadable to a local machine and it automatically installs the required freely available packages (R packages, including R2MultiBUGS and MultiBUGS). Availability BIMAM is available at [www.alecstudy.org/bimam]

Chronic environmental perturbation influences microbial community assembly patterns

Acknowledgements Next-generation sequencing and library construction was performed by NCIMB Ltd., Aberdeen and CGEBM, Aberdeen. The authors would like to acknowledge the support of the Maxwell computer cluster funded by the University of Aberdeen. Dr Axel Aigle is acknowledged for assistance in molecular analysis. This work was supported by the Natural Environment Research Council [NE/L00982X/1] with financial support from BP UK Ltd and Intertek Group PLC. CGR was supported by a University Research Fellowship from the Royal Society [UF150571]Peer reviewedPostprin

A mental health and wellbeing chatbot: user event log analysis

Background: Conversational user interfaces, or chatbots, are becoming more popular in the realm of digital health and well-being. While many studies focus on measuring the cause or effect of a digital intervention on people’s health and well-being (outcomes), there is a need to understand how users really engage and use a digital intervention in the real world. Objective: In this study, we examine the user logs of a mental well-being chatbot called ChatPal, which is based on the concept of positive psychology. The aim of this research is to analyze the log data from the chatbot to provide insight into usage patterns, the different types of users using clustering, and associations between the usage of the app’s features. Methods: Log data from ChatPal was analyzed to explore usage. A number of user characteristics including user tenure, unique days, mood logs recorded, conversations accessed, and total number of interactions were used with k-means clustering to identify user archetypes. Association rule mining was used to explore links between conversations. Results: ChatPal log data revealed 579 individuals older than 18 years used the app with most users being female (n=387, 67%). User interactions peaked around breakfast, lunchtime, and early evening. Clustering revealed 3 groups including “abandoning users” (n=473), “sporadic users” (n=93), and “frequent transient users” (n=13). Each cluster had distinct usage characteristics, and the features were significantly different (P&lt;.001) across each group. While all conversations within the chatbot were accessed at least once by users, the “treat yourself like a friend” conversation was the most popular, which was accessed by 29% (n=168) of users. However, only 11.7% (n=68) of users repeated this exercise more than once. Analysis of transitions between conversations revealed strong links between “treat yourself like a friend,” “soothing touch,” and “thoughts diary” among others. Association rule mining confirmed these 3 conversations as having the strongest linkages and suggested other associations between the co-use of chatbot features. Conclusions: This study has provided insight into the types of people using the ChatPal chatbot, patterns of use, and associations between the usage of the app’s features, which can be used to further develop the app by considering the features most accessed by users.Validerad;2023;Nivå 2;2023-08-14 (joosat);Funder: Interreg Northern Periphery and Arctic Programme (grant number 345)Licens fulltext: CC BY License</p