Students’ expectations from graduate studies in heritage (tourism) management

This article provides certain key aspects that might help to further develop academic heritage education. These have been discussed within brainstorming and round-table sessions carried out by World Heritage students at BTU Cottbus-Senftenberg. Important aspects regarding expectations, current issues and prospective career opportunities were selected after these sessions. With the idea of our International Master Programme in mind, the results of this work have been summarized to identify what makes a good focus to Heritage Studies and further strategies to improve this discipline. It was concluded that international and interdisciplinary approaches should serve as the basis to facilitate personal research interests, critical thinking, a sense of student community and how all these can be applied to a future professional career. External students, faculty members and heritage professionals are invited to join this debate

An Energy-conscious Transport Protocol for Multi-hop Wireless Networks

We present a transport protocol whose goal is to reduce power consumption without compromising delivery requirements of applications. To meet its goal of energy efficiency, our transport protocol (1) contains mechanisms to balance end-to-end vs. local retransmissions; (2) minimizes acknowledgment traffic using receiver regulated rate-based flow control combined with selected acknowledgements and in-network caching of packets; and (3) aggressively seeks to avoid any congestion-based packet loss. Within a recently developed ultra low-power multi-hop wireless network system, extensive simulations and experimental results demonstrate that our transport protocol meets its goal of preserving the energy efficiency of the underlying network.Defense Advanced Research Projects Agency (NBCHC050053

A Two-step Statistical Approach for Inferring Network Traffic Demands (Revises Technical Report BUCS-2003-003)

Accurate knowledge of traffic demands in a communication network enables or enhances a variety of traffic engineering and network management tasks of paramount importance for operational networks. Directly measuring a complete set of these demands is prohibitively expensive because of the huge amounts of data that must be collected and the performance impact that such measurements would impose on the regular behavior of the network. As a consequence, we must rely on statistical techniques to produce estimates of actual traffic demands from partial information. The performance of such techniques is however limited due to their reliance on limited information and the high amount of computations they incur, which limits their convergence behavior. In this paper we study a two-step approach for inferring network traffic demands. First we elaborate and evaluate a modeling approach for generating good starting points to be fed to iterative statistical inference techniques. We call these starting points informed priors since they are obtained using actual network information such as packet traces and SNMP link counts. Second we provide a very fast variant of the EM algorithm which extends its computation range, increasing its accuracy and decreasing its dependence on the quality of the starting point. Finally, we evaluate and compare alternative mechanisms for generating starting points and the convergence characteristics of our EM algorithm against a recently proposed Weighted Least Squares approach.National Science Foundation (ANI-0095988, EIA-0202067, ITR ANI-0205294

The digital twin synchronization problem: Framework, formulations, and analysis

As the adoption of digital twins increases steadily, it is necessary to determine how to operate them most effectively and efficiently. In this article, the digital twin synchronization problem is introduced and defined formally. Frequent synchronizations would increase cost and data traffic congestion, whereas infrequent synchronizations would increase the bias of the predictions and yield wrong decisions. This work defines the synchronization problem variants in different contexts. To discuss the problem and its solution, the problem of determining when to synchronize an unreliable production system with its digital twin to minimize the average synchronization and bias costs is formulated and analyzed analytically. The state-independent, state-dependent, and full-information solutions have been determined by using a stochastic model of the system. Solving the synchronization problem using simulation is discussed, and an approximate policy is proposed. Our results show that the performance of the state-dependent policy is close to the optimal solution that can be obtained with full information and significantly better than the performance of the state-independent policy. Furthermore, the approximate periodic state-dependent policy yields near-optimal results. To operate digital twins more effectively, the digital twin synchronization problem must be considered and solved to determine the optimal synchronization policy

Generation of mathematical programming representations for discrete event simulation models of timed petri nets

This work proposes a mathematical programming (MP) representation of discrete event simulation of timed Petri nets (TPN). Currently, mathematical programming techniques are not widely applied to optimize discrete event systems due to the difficulty of formulating models capable to correctly represent the system dynamics. This work connects the two fruitful research fields, i.e., mathematical programming and Timed Petri Nets. In the MP formalism, the decision variables of the model correspond to the transition firing times and the markings of the TPN, whereas the constraints represent the state transition logic and temporal sequences among events. The MP model and a simulation run of the TPN are then totally equivalent, and this equivalence has been validated through an application in the queuing network field. Using a TPN model as input, the MP model can be routinely generated and used as a white box for further tasks such as sensitivity analysis, cut generation in optimization procedures, and proof of formal properties

Analytical evaluation of the output variability in production systems with general Markovian structure

Performance evaluation models are used by companies to design, adapt, manage and control their production systems. In the literature, most of the effort has been dedicated to the development of efficient methodologies to estimate the first moment performance measures of production systems, such as the expected production rate, the buffer levels and the mean completion time. However, there is industrial evidence that the variability of the production output may drastically impact on the capability of managing the system operations, causing the observed system performance to be highly different from what expected. This paper presents a general methodology to analyze the variability of the output of unreliable single machines and small-scale multi-stage production systems modeled as General Markovian structure. The generality of the approach allows modeling and studying performance measures such as the variance of the cumulated output and the variance of the inter-departure time under many system configurations within a unique framework. The proposed method is based on the characterization of the autocorrelation structure of the system output. The impact of different system parameters on the output variability is investigated and characterized. Moreover, managerial actions that allow reducing the output variability are identified. The computational complexity of the method is studied on an extensive set of computer experiments. Finally, the limits of this approach while studying long multi-stage production lines are highlighted. © 2013 Springer-Verlag Berlin Heidelberg

Reinforcement learning for energy-efficient control of multi-stage production lines with parallel machine workstations

An effective approach to enhancing the sustainability of production systems is to use energy-efficient control (EEC) policies for optimal balancing of production rate and energy demand. Reinforcement learning (RL) algorithms can be employed to successfully control production systems, even when there is a lack of prior knowledge about system parameters. Furthermore, recent research demonstrated that RL can be also applied for the optimal EEC of a single manufacturing workstation with parallel machines. The purpose of this study is to apply an RL for EEC approach to more workstations belonging to the same industrial production system from the automotive sector, without relying on full knowledge of system dynamics. This work aims to show how the RL for EEC of more workstations affects the overall production system in terms of throughput and energy consumption. Numerical results demonstrate the benefits of the proposed model

Electrostatic potentials and average electron densities of bioisosteres in methylsquarate and acetic acid

© 2016 Future Science Ltd. Background: The bioisosterism in -CO2H and -C4HO3 is exploited using the quantum theory of atoms in molecules and molecular electrostatic potentials (ESP). Results & discussion: Bioisosteres in methylsquarate and acetic acid, in the neutral/anionic forms, have average electron densities that differ by less than 2% (i.e., ∼0.01 atomic units) while irrespective of the capping group. The topography of the ESP reveals similarities in the case of the neutral species but not in the anionic forms. Conclusion: The nonclassical bioisosteres in methylsquarate and acetic acid have average electron densities that are similar and relatively insensitive to the ionization state (neutral or anionic) or its studied capping group (H, CH3, Cl or phenyl). The ESP reveals similarities in the topography of neutral molecules

Adenine-thymine tautomerization under the influence of strong homogeneous electric fields

© the Owner Societies 2018. The effect of strong electric fields on the tautomerization of the adenine-thymine (AT) base pair in DNA is explored using density functional theory. It was found that the AT base pair is not likely to undergo a double proton transfer reaction even in the presence of electric fields ranging from 5.14 × 108 to 5.14 × 109 V m-1. This conclusion holds true in Gibbs energies computed at 25 °C or 37 °C. Energy correction terms to the electronic energy, such as total internal energy, Gibbs energy, zero-point energy, enthalpy and entropy corrections, were investigated in detail to pinpoint the major contributors to the low probability of the AT tautomerization. It was found that the entropy corrections are the least significant, while zero-point energy and Gibbs energy corrections can be large enough to thermodynamically inhibit the DPT in AT