The Polaron: Ground State, Excited States, and Far from Equilibrium

We describe a variational approach for solving the Holstein polaron model with dynamical quantum phonons on an infinite lattice. The method is simple, fast, extremely accurate, and gives ground and excited state energies and wavefunctions at any momentum k. The method can also be used to calculate coherent quantum dynamics for inelastic tunneling and for strongly driven polarons far from equilibrium.     

Comparative Analysis of System Dynamics and Object-Oriented Bayesian Networks Modelling for Water Systems Management

This paper presents a comparative analysis of System Dynamics Modelling (SDM) and Object-Oriented Bayesian Networks (OOBN). Both techniques are extensively used for water resources modelling due to their flexibility, effectiveness in assessing different management options, ease of operation and suitability for encouraging stakeholder involvement. Conversely, both approaches have several important differences that make them complementary. For example, while SDM is more suitable for simulating the feedback dynamics of processes, OOBN modelling is a powerful tool for modelling systems with uncertain inputs (or outputs) characterised by probability distributions. This comparative analysis is applied to the Kairouan aquifer system, Tunisia, where the aquifer plays an essential role for socio-economic development in the region. Both models produced comparable results using baseline data, and show their complementarity through a suite of scenario tests. It is shown that reducing pumping of groundwater to coastal cities may prove the key to reducing the current aquifer deficit, though local demand reduction must be considered to preserve the agricultural economy. It is suggested that water management assessment should be tackled using both approaches to complement each other, adding depth and insight, and giving a more coherent picture of the problem being addressed, allowing for robust policy decisions to be made.     

On stability prediction for milling

Stability of 2-dof milling is investigated. Stability boundaries are predicted by the zeroth order approximation (ZOA) and the semi-discretization (SD) methods. While similar for high radial immersions, predictions of the two methods grow considerably different as radial immersion is decreased. The most prominent difference is an additional type of instability causing periodic chatter which is predicted only by the SD method. Experiments confirm predictions of the SD method, revealing three principal types of tool motion: periodic chatter-free, quasi-periodic chatter and periodic chatter, as well as some special chatter cases. Tool deflections recorded during each of these motion types are studied in detail.     

Neural network modelling of cold-cathode gauge parameters

This article describes modelling of the operating characteristics of a cold-cathode ionisation gauge (CCG). The gauge characteristics were measured on a gauge comparison UHV calibration system with a test chamber, an extractor gauge, a spinning rotor gauge, and a gas manifold with a precise leak valve. Discharge intensity was measured vs. anode voltage at different pressures selected in the range from 1×10⁻⁹ to 1×10⁻⁵ mbar, and vs. pressure at different operating voltages ranging from 1.2 to 9 kV. In all cases the magnetic flux density was the same and amounted to about 0.13 T. The CCG exhibits an extremely low thermal outgassing rate and a low measurement limit. Therefore, it is suitable for pressure measurements in the ultrahigh vacuum range; however, it has a significant disadvantage. The discharge current vs. the pressure characteristic is non-linear and, in some cases, even discontinuous.The measured CCG characteristics were used as an input for the artificial neural network, which was used to generate a non-linear CCG input-output function used for linearisation purposes. It is generally known and strictly proven that neural networks are capable of learning and building any kind of real and non-polynomial input-output function. Furthermore, it was also mathematically proven that the single hidden neural layer system can learn any function. Other authors have reported that the learned function characteristics are not always continuous.In our experimental work, no mapping discontinuities in the formed model were detected. Despite the fact that learning of the input-output characteristics can be obtained by the neural networks with only one hidden layer, we have used the multilayer neural networks that exhibit a faster convergent and smoother learning process. The neural networks were trained to perform the transfer function between the input gauge parameters and the pressure. The neural networks are a suitable solution for CCG characteristics modelling and thus offer the possibility to overcome the disadvantages of the CCG.     

With or without microcomputers : A dilemma

The emergence of microcomputers in the early 80s created a confused atmosphere in the information technology (IT) users' world. Especially, in developing countries, the decision on what type of hardware to acquire for day-to-day data processing needs of organizations was at the mercy of somewhat inadequate hardware vendor specifications. This being the case, a study was conducted in a composite organization of associated labour (COAL - a Yugoslav terminology for complex business organization - equivalent to corporation), to determine whether microcomputers (micros) could or could not be adapted to support data processing in conjunction with the existing mainframe. The research is based on samples gathered from a COAL that consists of nine different work organizations (WO), equivalent to middle size business organizations, which in turn consist of 27 firms or basic organizations of associated labour (BOAL). The study involved 350 employees whose day-to-day work assignment is linked with the existing data processing system, and those whose work assignment could have been supported by IT. The results are analyzed and implications outlined and discussed.     

Prediction of Chronic Inflammation for Inhaled Particles: the Impact of Material Cycling and Quarantining in the Lung Epithelium

On a daily basis, people are exposed to a multitude of health-hazardous airborne particulate matter with notable deposition in the fragile alveolar region of the lungs. Hence, there is a great need for identification and prediction of material-associated diseases, currently hindered due to the lack of in-depth understanding of causal relationships, in particular between acute exposures and chronic symptoms. By applying advanced microscopies and omics to in vitro and in vivo systems, together with in silico molecular modeling, it is determined herein that the long-lasting response to a single exposure can originate from the interplay between the newly discovered nanomaterial quarantining and nanomaterial cycling between different lung cell types. This new insight finally allows prediction of the spectrum of lung inflammation associated with materials of interest using only in vitro measurements and in silico modeling, potentially relating outcomes to material properties for a large number of materials, and thus boosting safe-by-design-based material development. Because of its profound implications for animal-free predictive toxicology, this work paves the way to a more efficient and hazard-free introduction of numerous new advanced materials into our lives.     

Volume Determination of Vacuum Vessels by Gas Expansion Method

This paper presents a practical procedure for the determination and precision calibration of the volume ratio and absolute value of the volume of vacuum chamber by using static expansion method. Such systems for calculations of volume ratio consist from two vessels connected via a valve called the expansion valve. For determination of absolute value of the volume we added another volume with simple shape (determined by dimensional measurements). In such systems, under the isothermal conditions, the gas pressure is reduced by the ratio of the small volume to the sum of the first volume (small volume) and second volume. The uncertainty of measurement of the volume ratio and absolute volume is calculated as well. Relative volume calibration is performed with uncertainties below 0.75 %.