Quantum transport and localization in biased periodic structures under bi- and polychromatic driving

We consider the dynamics of a quantum particle in a one-dimensional periodic potential (lattice) under the action of a static and time-periodic field. The analysis is based on a nearest-neighbor tight-binding model which allows a convenient closed form description of the transport properties in terms of generalized Bessel functions. The case of bichromatic driving is analyzed in detail and the intricate transport and localization phenomena depending on the communicability of the two excitation frequencies and the Bloch frequency are discussed. The case of polychromatic driving is also discussed, in particular for flipped static fields, i.e. rectangular pulses, which can support an almost dispersionless transport with a velocity independent of the field amplitude.Comment: 18 pages, 11 figur

Magnetotactic bacteria Magnetic navigation on the microscale

Magnetotactic bacteria are aquatic microorganisms with the ability to swim along the field lines of a magnetic field, which in their natural environment is provided by the magnetic field of the Earth. They do so with the help of specialized magnetic organelles called magnetosomes, vesicles containing magnetic crystals. Magnetosomes are aligned along cytoskeletal filaments to give linear structures that can function as intracellular compass needles. The predominant viewpoint is that the cells passively align with an external magnetic field, just like a macroscopic compass needle, but swim actively along the field lines, propelled by their flagella. In this minireview, we give an introduction to this intriguing bacterial behavior and discuss recent advances in understanding it, with a focus on the swimming directionality, which is not only affected by magnetic fields, but also by gradients of the oxygen concentration

Efficiency Measurement in Digitalized Work Systems of Transport Logistics

Digitalization is a major trend and challenge in most industries and sectors of societies. Still, quantitative insights regarding the impacts of digitalization are missing. This chapter is reporting a first approach using Data Envelopment Analysis (DEA) for measuring efficiency results of digitalization steps in a retail logistics context. Aspiring to quantify the performance of professional truck drivers during a digital turnover related to mobile devices, we evaluate truck loading processes. As inputs we use loading time and costs. Outputs are load factor of units, invoice charged to shops, and the value of the damages during truck loading. The findings indicate that a change in the level of digitalization entails a loss of the efficiency level in the first instance, which can be compensated and even surpassed later. When applying linear regression analysis, we prove a low statistical linear relationship of age and efficiency plus a strong statistical linear relationship of employer size and efficiency as well as period of employment and efficiency, always regarding the changing levels of digitalization in the working system of professional truck drivers. For practitioners in retail logistics, we derive the importance of employee retention programs for human resource management, along with a positive working environment provided for truck drivers to reduce fluctuation effects. Furthermore, we advise designing software for truck drivers as commonplace as possible and in the style of widespread smartphone software user interfaces

Smart and efficient: Learning curves in manual and human-robot order picking systems

Order picking has been identified as the most labour-intensive, as well as costly activity within warehouse logistics and is experiencing significant changes due to new technologies in the forms of artificial intelligence (AI) and automation. One fundamental question concerns the employees learning progress in human-robot picking systems compared to existing manual technologies. Therefore, this paper presents an empirical analysis of learning curves in manual pick-by-voice (n=30 pickers) and semiautomated (n=20 pickers) order picking. Aspiring to measure the individual learning progress without a priori assumptions, this publication is the first to apply Data Envelopment Analysis and examine order pickers learning curves in real application scenarios. The findings indicate that automating human work accelerates the individual learning progress in human-robot picking systems

Localized Cumulative Distributions and a Multivariate Generalization of the Cramér-von Mises Distance

This paper is concerned with distances for comparing multivariate random vectors with a special focus on the case that at least one of the random vectors is of discrete type, i.e., assumes values from a discrete set only. The first contribution is a new type of characterization of multivariate random quantities, the so called Localized Cumulative Distribution (LCD) that, in contrast to the conventional definition of a cumulative distribution, is unique and symmetric. Based on the LCDs of the random vectors under consideration, the second contribution is the definition of generalized distance measures that are suitable for the multivariate case. These distances are used for both analysis and synthesis purposes. Analysis is concerned with assessing whether a given sample stems from a given continuous distribution. Synthesis is concerned with both density estimation, i.e., calculating a suitable continuous approximation of a given sample, and density discretization, i.e., approximation of a given continuous random vector by a discrete one

Dirac Mixture Trees for Fast Suboptimal Multi-Dimensional Density Approximation

We consider the problem of approximating an arbitrary multi-dimensional probability density function by means of a Dirac mixture density. Instead of an optimal solution based on minimizing a global distance measure between the true density and its approximation, a fast suboptimal anytime procedure is proposed, which is based on sequentially partitioning the state space and component placement by local optimization. The proposed procedure adaptively covers the entire state space with a gradually increasing resolution. It can be efficiently implemented by means of a pre-allocated tree structure in a straightforward manner. The resulting computational complexity is linear in the number of components and linear in the number of dimensions. This allows a large number of components to be handled, which is especially useful in high-dimensional state spaces

Nonlinear Fusion of Multi-Dimensional Densities in Joint State Space

Nonlinear fusion of multi-dimensional densities is an important application in Bayesian state estimation. In the approach proposed here, a joint density over all considered densities is build, which is then approximated by means of a Dirac mixture density by partitioning the joint state space into regions that are represented by single Dirac components. This approximation procedure depends on the nonlinear fusion model and only areas relevant to this model are considered. The processing in joint state space has advantages, especially when fusing Dirac mixture densities. Within this approach, degeneration can be avoided and even densities without mutual support can be combined. Thus, this approach gives an alternative to multiplication of Dirac mixtures with a likelihood, as used in the particle filter. Furthermore, a nonlinear Bayesian estimator with filter and prediction step can be formulated, which is able to cope with both discrete and continuous densities

COVID-19 health policy evaluation: integrating health and economic perspectives with a data envelopment analysis approach

The COVID-19 pandemic is a global challenge to humankind. To improve the knowledge regarding relevant, efficient and effective COVID-19 measures in health policy, this paper applies a multi-criteria evaluation approach with population, health care, and economic datasets from 19 countries within the OECD. The comparative investigation was based on a Data Envelopment Analysis approach as an efficiency measurement method. Results indicate that on the one hand, factors like population size, population density, and country development stage, did not play a major role in successful pandemic management. On the other hand, pre-pandemic healthcare system policies were decisive. Healthcare systems with a primary care orientation and a high proportion of primary care doctors compared to specialists were found to be more efficient than systems with a medium level of resources that were partly financed through public funding and characterized by a high level of access regulation. Roughly two weeks after the introduction of ad hoc measures, e.g., lockdowns and quarantine policies, we did not observe a direct impact on country-level healthcare efficiency, while delayed lockdowns led to significantly lower efficiency levels during the first COVID-19 wave in 2020. From an economic perspective, strategies without general lockdowns were identified as a more efficient strategy than the full lockdown strategy. Additionally, governmental support of short-term work is promising. Improving the efficiency of COVID-19 countermeasures is crucial in saving as many lives as possible with limited resources