Self-diffusion and Cooperative Diffusion in Semidilute Polymer Solutions as measured by Fluorescence Correlation Spectroscopy

We present a comprehensive investigation of polymer diffusion in the semidilute regime by fluorescence correlation spectroscopy (FCS) and dynamic light scattering (DLS). Using single-labeled polystyrene chains, FCS leads to the self-diffusion coefficient while DLS gives the cooperative diffusion coefficient for exactly the same molecular weights and concentrations. Using FCS we observe a new fast mode in the semidilute entangled concentration regime beyond the slower mode which is due to self-diffusion. Comparison of FCS data with data obtained by DLS on the same polymers shows that the second mode observed in FCS is identical to the cooperative diffusion coefficient measured with DLS. An in-depth analysis and a comparison with current theoretical models demonstrates that the new cooperative mode observed in FCS is due to the effective long-range interaction of the chains through the transient entanglement network

Solving Practical Railway Crew Scheduling Problems with Attendance Rates

Arising from a practical problem in German rail passenger transport, a prototype for a multi-period railway crew scheduling problem with attendance rates for conductors is developed and evaluated in this paper. The consideration of attendance rates is of increasing importance in regional transport networks and requires decision support. For this purpose business analytics is applied in order to offer an approach to transform real-world data to concrete operational decision support (action). The focus here is on the analysis step using a new set covering model with several essential restrictions integrated for the first time. A hybrid column generation approach is applied, which solves the pricing problem by means of a genetic algorithm. The artifact is evaluated with the help of a case study of three real-world transport networks. It is shown that the hybrid solution approach is able to solve the problem more effectively and efficiently compared to conventional approaches used in practice

Motivational interdependence in couple relationships

This article presents an integrative conceptual model of motivational interdependence in couples, the MIC model. Based on theoretical tenets in motivation psychology, personality psychology, and research on interpersonal perception, the MIC model postulates that two partners' motive dispositions fundamentally interact in shaping their individual motivation and behavior. On a functional level, a partner's motivated behavior is conceptualized as an environmental cue that can contribute to an actor's motive expression and satisfaction. However, the partner's motivated behavior is considered to gain this motivational relevance only via the actor's subjective perception. Multilevel analyses of an extensive experience sampling study on partner-related communal motivation ( N = up to 60,803 surveys from 508 individuals nested in 258 couples) supported the MIC model. Participants, particularly those with strong communal motive dispositions, behaved more communally at moments when they perceived their partners to behave more communally. In addition, participants experienced momentary boosts in satisfaction when they behaved more communally and, at the same time, perceived their partners' behavior as similarly communal. Broader implications of the MIC model for research on romantic relationships are discussed

What constitutes a machine-learning-driven business model? A taxonomy of B2B start-ups with machine learning at their core

Artificial intelligence, specifically machine learning (ML), technologies are powerfully driving business model innovation in organizations against the backdrop of increasing digitalization. The resulting novel business models are profoundly shaped by ML, a technology that brings about unique opportunities and challenges. However, to date, little research examines what exactly constitutes these business models that use ML at their core and how they can be distinguished. Therefore, this study aims to contribute to an increased understanding of the anatomy of ML-driven business models in the business-to-business segment. To this end, we develop a taxonomy that allows researchers and practitioners to differentiate these ML-driven business models according to their characteristics along ten dimensions. Additionally, we derive archetypes of ML-driven business models through a cluster analysis based on the characteristics of 102 start-ups from the database Crunchbase. Our results are cross-industry, providing fertile soil for expansion through future investigations

Truth and Wishful Thinking: How Interindividual Differences in Communal Motives Manifest in Momentary Partner Perceptions

Although rooted in reality, partner perceptions often reflect wishful thinking due to perceivers' needs. Dispositional needs, or motives, can differ between persons; however, little is known about their differential associations with everyday partner perception. The present study used data from a 4‐week experience sampling study ( N = up to 60942 surveys from 510 individuals nested in 259 couples) to examine the effects of perceivers' partner‐related implicit and explicit communal motives on the perception of (i) global communal partner behaviour and (ii) specific communal and uncommunal partner behaviours. The results of truth and bias models of judgement and quasi‐signal detection analyses indicate that strong implicit communal approach motives and strong explicit communal motives are associated with the tendency to overestimate the partner's communal behaviour. Additionally, strong implicit communal approach motives were associated with the tendency to avoid perceptions of uncommunal partner behaviour. Neither implicit nor explicit communal motives had an effect on accuracy in the perception of particularly communal partner behaviour. The results highlight the relevance of both implicit and explicit communal motives for momentary partner perceptions and emphasise the benefits of dyadic microlongitudinal designs for a better understanding of the mechanisms through which individual differences manifest in couples' everyday lives. © 2019 The Authors. European Journal of Personality published by John Wiley & Sons Ltd on behalf of European Association of Personality Psycholog

Measuring motivational relationship processes in experience sampling: A reliability model for moments, days, and persons nested in couples

The investigation of within-person process models, often done in experience sampling designs, requires a reliable assessment of within-person change. In this paper, we focus on dyadic intensive longitudinal designs where both partners of a couple are assessed multiple times each day across several days. We introduce a statistical model for variance decomposition based on generalizability theory (extending P. E. Shrout & S. P. Lane, 2012), which can estimate the relative proportion of variability on four hierarchical levels: moments within a day, days, persons, and couples. Based on these variance estimates, four reliability coefficients are derived: between-couples, between-persons, within-persons/between-days, and within-persons/between-moments. We apply the model to two dyadic intensive experience sampling studies (n1 = 130 persons, 5 surveys each day for 14 days, ≥ 7508 unique surveys; n2 = 508 persons, 5 surveys each day for 28 days, ≥ 47764 unique surveys). Five different scales in the domain of motivational processes and relationship quality were assessed with 2 to 5 items: State relationship satisfaction, communal motivation, and agentic motivation; the latter consists of two subscales, namely power and independence motivation. Largest variance components were on the level of persons, moments, couples, and days, where within-day variance was generally larger than between-day variance. Reliabilities ranged from .32 to .76 (couple level), .93 to .98 (person level), .61 to .88 (day level), and .28 to .72 (moment level). Scale intercorrelations reveal differential structures between and within persons, which has consequences for theory building and statistical modeling

Bugs That Can Resist Antibiotics but Not Men: Gender-Specific Differences in Notified Infections and Colonisations in Germany, 2010–2019

Data from surveillance networks show that men have a higher incidence rate of infections with anti-microbial-resistant (AMR) pathogens than women. We systematically analysed data of infections and colonisations with AMR pathogens under mandatory surveillance in Germany to quantify gender-specific differences. We calculated incidence-rates (IR) per 100,000 person–years for invasive infections with Methicillin-resistant Staphylococcus aureus (MRSA), and for infections or colonisations with carbapenem-non-susceptible Acinetobacter spp. (CRA), and Enterobacterales (CRE), using the entire German population as a denominator. We limited the study periods to years with complete notification data (MRSA: 2010–2019, CRA/CRE: 2017–2019). We used Poisson regression to adjust for gender, age group, federal state, and year of notification. In the study periods, IR for all notifications were 4.2 for MRSA, 0.90 for CRA, and 4.8 for CRE per 100,000 person-–years. The adjusted IR ratio for infections of men compared to women was 2.3 (95% confidence interval [CI]: 2.2–2.3) for MRSA, 2.2 (95%CI: 1.9–2.7) for CRA, and 1.7 (95%CI: 1.6–1.8) for CRE. Men in Germany show about double the risk for infection with AMR pathogens than women. This was also true for colonisations, where data were available. Screening procedures and associated hygiene measures may profit from a gender-stratified approach.Peer Reviewe

A practical approach to the sensitivity analysis for kinetic Monte Carlo simulation of heterogeneous catalysis

Lattice kinetic Monte Carlo simulations have become a vital tool for predictive quality atomistic understanding of complex surface chemical reaction kinetics over a wide range of reaction conditions. In order to expand their practical value in terms of giving guidelines for the atomic level design of catalytic systems, it is very desirable to readily evaluate a sensitivity analysis for a given model. The result of such a sensitivity analysis quantitatively expresses the dependency of the turnover frequency, being the main output variable, on the rate constants entering the model. In the past, the application of sensitivity analysis, such as degree of rate control, has been hampered by its exuberant computational effort required to accurately sample numerical derivatives of a property that is obtained from a stochastic simulation method. In this study, we present an efficient and robust three-stage approach that is capable of reliably evaluating the sensitivity measures for stiff microkinetic models as we demonstrate using the CO oxidation on RuO2(110) as a prototypical reaction. In the first step, we utilize the Fisher information matrix for filtering out elementary processes which only yield negligible sensitivity. Then we employ an estimator based on the linear response theory for calculating the sensitivity measure for non- critical conditions which covers the majority of cases. Finally, we adapt a method for sampling coupled finite differences for evaluating the sensitivity measure for lattice based models. This allows for an efficient evaluation even in critical regions near a second order phase transition that are hitherto difficult to control. The combined approach leads to significant computational savings over straightforward numerical derivatives and should aid in accelerating the nano-scale design of heterogeneous catalysts