Dynamics and interactions of active rotors

We consider a simple model of an internally driven self-rotating object; a rotor, confined to two dimensions by a thin film of low Reynolds number fluid. We undertake a detailed study of the hydrodynamic interactions between a pair of rotors and find that their effect on the resulting dynamics is a combination of fast and slow motions. We analyse the slow dynamics using an averaging procedure to take account of the fast degrees of freedom. Analytical results are compared with numerical simulations. Hydrodynamic interactions mean that while isolated rotors do not translate, bringing together a pair of rotors leads to motion of their centres. Two rotors spinning in the same sense rotate with an approximately constant angular velocity around each other, while two rotors of opposite sense, both translate with the same constant velocity, which depends on the separation of the pair. As a result a pair of counter-rotating rotors are a promising model for controlled self-propulsion.Comment: 6 pages, 6 figure

Violation and persistence of the K-quantum number in warm rotating nuclei

The validity of the K-quantum number in rapidly rotating warm nuclei is investigated as a function of thermal excitation energy U and angular momentum I, for the rare-earth nucleus 163Er. The quantal eigenstates are described with a shell model which combines a cranked Nilsson mean-field and a residual two-body interaction, together with a term which takes into account the angular momentum carried by the K-quantum number in an approximate way. K-mixing is produced by the interplay of the Coriolis interaction and the residual interaction; it is weak in the region of the discrete rotational bands (U \lesim 1MeV), but it gradually increases until the limit of complete violation of the K-quantum number is approached around U \sim 2 - 2.5 MeV. The calculated matrix elements between bands having different K-quantum numbers decrease exponentially as a function of $\Delta K$, in qualitative agreement with recent data.Comment: 29 pages, 7 figure

Improving the optimization in model predictive controllers:Scheduling large groups of electric vehicles

In parking lots with large groups of electric vehicles (EVs), charging has to happen in a coordinated manner, among others, due to the high load per vehicle and the limited capacity of the electricity grid. To achieve such coordination, model predictive control can be applied, thereby repeatedly solving an optimization problem. Due to its repetitive nature and its dependency on the time granularity, optimization has to be(computationally) efficient.The work presented here focuses on that optimization sub-routine, its computational efficiency and how to speed up the optimization for large groups of EVs. In particular, we adapt FOCS, an algorithm that can solve the underlying optimization problem, to better suit the repetitive set-up of model predictive control by adding a pre-mature stop feature. Based on real-world data, we empirically show that the added feature speeds up the median computation time for 1-minute granularity by up to 44%.Furthermore, since FOCS is an algorithm that uses maximum flow methods as a subroutine, the impact of choosing various maximum flow methods on the runtime is investigated. Finally, we compare FOCS to a commercially available solver, concluding that FOCS outperforms the state-of-the-art when making a full-day schedule for large groups of EVs

Relating Electric Vehicle Charging to Speed Scaling with Job-Specific Speed Limits

Due to the ongoing electrification of transport in combination with limited power grid capacities, efficient ways to schedule the charging of electric vehicles (EVs) are needed for the operation of, for example, large parking lots. Common approaches such as model predictive control repeatedly solve a corresponding offline problem. In this work, we first present and analyze the Flow-based Offline Charging Scheduler (FOCS), an offline algorithm to derive an optimal EV charging schedule for a fleet of EVs that minimizes an increasing, convex and differentiable function of the corresponding aggregated power profile. To this end, we relate EV charging to processor speed scaling models with job-specific speed limits. We prove our algorithm to be optimal and derive necessary and sufficient conditions for any EV charging profile to be optimal. Furthermore, we discuss two online algorithms and their competitive ratios for a specific class objective functions. In particular, we show that if those algorithms are applied and adapted to the presented EV scheduling problem, the competitive ratios for Average Rate and Optimal Available match those of the classical speed scaling problem. Finally, we present numerical results using real-world EV charging data to put the theoretical competitive ratios into a practical perspective

Long-Term Safety of Anti-TNF Adalimumab in HBc Antibody-Positive Psoriatic Arthritis Patients: A Retrospective Case Series of 8 Patients

Immunosuppressive drugs commonly used in the treatment of psoriatic arthritis make patients more susceptible to viral, bacterial, and fungal infections because of their mechanism of action. They not only increase the risk of new infections but also act altering the natural course of preexisting infections. While numerous data regarding the reactivation of tuberculosis infection are available in the literature, poor information about the risk of reactivation or exacerbation of hepatitis viruses B and C infections during treatment with biologics has been reported. Furthermore, reported series with biological therapy included short periods of followup, and therefore, they are not adequate to verify the risk of reactivation in the long-term treatment. Our study evaluated patients with a history of hepatitis B and psoriatic arthritis treated with adalimumab and monitored up to six years. During the observation period, treatment was effective and well tolerated in all patients, and liver function tests and viral load levels remained unchanged

Study of the optimal conditions for NV- center formation in type 1b diamond, using photoluminescence and positron annihilation spectroscopies

We studied the parameters to optimize the production of negatively-charged nitrogen-vacancy color centers (NV-) in type~1b single crystal diamond using proton irradiation followed by thermal annealing under vacuum. Several samples were treated under different irradiation and annealing conditions and characterized by slow positron beam Doppler-broadening and photoluminescence (PL) spectroscopies. At high proton fluences another complex vacancy defect appears limiting the formation of NV-. Concentrations as high as 2.3 x 10^18 cm^-3 of NV- have been estimated from PL measurements. Furthermore, we inferred the trapping coefficient of positrons by NV-. This study brings insight into the production of a high concentration of NV- in diamond, which is of utmost importance in ultra-sensitive magnetometry and quantum hybrid systems applications

Rotational Damping and Compound Formation in Warm Rotating Nuclei

The rotational damping width \Gamma_{rot} and the compound damping width \Gamma_{comp} are two fundamental quantities that characterize rapidly rotating compound nuclei having finite thermal excitation energy. A two-component structure in the strength function of consecutive E2 transitions reflects the two widths, and it causes characteristic features in the double and triple gamma-ray spectra. We discuss a new method to extract experimentally values of \Gamma_{rot} and \Gamma_{comp}. The first preliminary result of this method is presented.Comment: PDF, 8 pages, invited talk at the Conference on Frontiers of Nuclear Structure (FNS2002), August 2002, Berkele

Discovering Business Area Effects to Process Mining Analysis Using Clustering and Influence Analysis

A common challenge for improving business processes in large organizations is that business people in charge of the operations are lacking a fact-based understanding of the execution details, process variants, and exceptions taking place in business operations. While existing process mining methodologies can discover these details based on event logs, it is challenging to communicate the process mining findings to business people. In this paper, we present a novel methodology for discovering business areas that have a significant effect on the process execution details. Our method uses clustering to group similar cases based on process flow characteristics and then influence analysis for detecting those business areas that correlate most with the discovered clusters. Our analysis serves as a bridge between BPM people and business, people facilitating the knowledge sharing between these groups. We also present an example analysis based on publicly available real-life purchase order process data.Comment: 12 pages. Paper accepted in 23rd International Conference on Business Information Systems (BIS 2020) to be published in a proceedings edition of the Lecture Notes in Business Information Processin

Measuring close proximity interactions in summer camps during the COVID-19 pandemic

Policy makers have implemented multiple non-pharmaceutical strategies to mitigate the COVID-19 worldwide crisis. Interventions had the aim of reducing close proximity interactions, which drive the spread of the disease. A deeper knowledge of human physical interactions has revealed necessary, especially in all settings involving children, whose education and gathering activities should be preserved. Despite their relevance, almost no data are available on close proximity contacts among children in schools or other educational settings during the pandemic. Contact data are usually gathered via Bluetooth, which nonetheless offers a low temporal and spatial resolution. Recently, ultra-wideband (UWB) radios emerged as a more accurate alternative that nonetheless exhibits a significantly higher energy consumption, limiting in-field studies. In this paper, we leverage a novel approach, embodied by the Janus system that combines these radios by exploiting their complementary benefits. The very accurate proximity data gathered in-field by Janus, once augmented with several metadata, unlocks unprecedented levels of information, enabling the development of novel multi-level risk analyses. By means of this technology, we have collected real contact data of children and educators in three summer camps during summer 2020 in the province of Trento, Italy. The wide variety of performed daily activities induced multiple individual behaviors, allowing a rich investigation of social environments from the contagion risk perspective. We consider risk based on duration and proximity of contacts and classify interactions according to different risk levels. We can then evaluate the summer camps’ organization, observe the effect of partition in small groups, or social bubbles, and identify the organized activities that mitigate the riskier behaviors. Overall, we offer an insight into the educator-child and child-child social interactions during the pandemic, thus providing a valuable tool for schools, summer camps, and policy makers to (re)structure educational activities safely