Cavitation Bubbles Generated by Vibrating Quartz Tuning Fork in Liquid $$^$$He Close to the $$\lambda $$λ-Transition

We report direct optical observation of cavitation bubbles in liquid helium, both in classical viscous He I and in superfluid He II, close to the \(\lambda \)-transition. Heterogenous cavitation due to the fast-flowing liquid over the rough surface of prongs of a quartz tuning fork oscillating at its fundamental resonant frequency of \(4\,\mathrm {kHz}\) occurs in the form of a cluster of small bubbles rapidly changing its size and position. In accord with previous investigators, we find the cavitation threshold lower in He I than in He II. In He I, the detached bubbles last longer than one camera frame (10 ms), while in He II the cavitation bubbles do not tear off from the surface of the fork up to the highest attainable drive.     

Quantitative fine-grained human evaluation of machine translation systems: a case study on English to Croatian

This paper presents a quantitative fine-grained manual evaluation approach to comparing the performance of different machine translation (MT) systems. We build upon the well-established multidimensional quality metrics (MQM) error taxonomy and implement a novel method that assesses whether the differences in performance for MQM error types between different MT systems are statistically significant. We conduct a case study for English-to-Croatian, a language direction that involves translating into a morphologically rich language, for which we compare three MT systems belonging to different paradigms: pure phrase-based, factored phrase-based and neural. First, we design an MQM-compliant error taxonomy tailored to the relevant linguistic phenomena of Slavic languages, which made the annotation process feasible and accurate. Errors in MT outputs were then annotated by two annotators following this taxonomy. Subsequently, we carried out a statistical analysis which showed that the best-performing system (neural) reduces the errors produced by the worst system (pure phrase-based) by more than half (54%). Moreover, we conducted an additional analysis of agreement errors in which we distinguished between short (phrase-level) and long distance (sentence-level) errors. We discovered that phrase-based MT approaches are of limited use for long distance agreement phenomena, for which neural MT was found to be especially effective.     

A Bayesian approach to forecasting daily air-pollutant levels

Forecasting air-pollutant levels is an important issue, due to their adverse effects on public health, and often a legislative necessity. The advantage of Bayesian methods is their ability to provide density predictions which can easily be transformed into ordinal or binary predictions given a set of thresholds. We develop a Bayesian approach to forecasting PM\(_{10}\) and O\(_3\) levels that efficiently deals with extensive amounts of input parameters, and test whether it outperforms classical models and experts. The new approach is used to fit models for PM\(_{10}\) and O\(_3\) level forecasting that can be used in daily practice. We also introduce a novel approach for comparing models to experts based on estimated cost matrices. The results for diverse air quality monitoring sites across Slovenia show that Bayesian models outperform classical models in both PM\(_{10}\) and O\(_3\) predictions. The proposed models perform better than experts in PM\(_{10}\) and are on par with experts in O\(_3\) predictions—where experts already base their predictions on predictions from a statistical model. A Bayesian approach—especially using Gaussian processes—offers several advantages: superior performance, robustness to overfitting, more information, and the ability to efficiently adapt to different cost matrices.     

Simulation of liquid micro-jet in free expanding high-speed co-flowing gas streams

We present the development of an experimentally validated computational fluid dynamics model for liquid micro jets. Such jets are produced by focusing hydrodynamic momentum from a co-flowing sheath of gas on a liquid stream in a nozzle. The numerical model based on laminar two-phase, Newtonian, compressible Navier–Stokes equations is solved with finite volume method, where the phase interface is treated by the volume of fluid approach. A mixture model of the two-phase system is solved in axisymmetry using?~?300,000 finite volumes, while ensuring mesh independence with the finite volumes of the size 0.25 µm in the vicinity of the jet and drops. The numerical model is evaluated by comparing jet diameters and jet lengths obtained experimentally and from scaling analysis. They are not affected by the strong temperature and viscosity changes in the focusing gas while expanding at nozzle outlet. A range of gas and liquid-operating parameters is investigated numerically to understand their influence on the jet performance. The study is performed for gas and liquid Reynolds numbers in the range 17–1222 and 110–215, and Weber numbers in the range 3–320, respectively. A reasonably good agreement between experimental and scaling results is found for the range of operating parameters never tackled before. This study provides a basis for further computational designs as well as adjustments of the operating conditions for specific liquids and gases.     

Updated Taxonomy for the Network and Service Management Research Field

Network and service management is an established research field within the general area of computer networks. A few years ago, an initial taxonomy, organizing a comprehensive list of terms and topics, was established through interviews with experts from both industry and academia. This taxonomy has since been used to better partition standardization efforts, identify classes of managed objects and improve the assignment of reviewers to papers submitted in the field. Because the field of network and service management is rapidly evolving, a biyearly update of the taxonomy was proposed. In this paper, a large-scale questionnaire is presented which was answered by experts in the field, evaluating the relevance of each individual topic for the next five years. Missing topics, which are likely to become relevant over the next few years, are identified as well. Furthermore, an analysis is performed of the records of papers submitted to major conferences in the area. Based on the obtained results, an updated version of the taxonomy is proposed.     

Encoding Information on the Excited State of a Molecular Spin Chain

The quantum states of nano-objects can drive electrical transport properties across lateral and local-probe junctions. This raises the prospect, in a solid-state device, of electrically encoding information at the quantum level using spin-flip excitations between electron spins. However, this electronic state has no defined magnetic orientation and is short-lived. Using a novel vertical nanojunction process, these limitations are overcome and this steady-state capability is experimentally demonstrated in solid-state spintronic devices. The excited quantum state of a spin chain formed by Co phthalocyanine molecules coupled to a ferromagnetic electrode constitutes a distinct magnetic unit endowed with a coercive field. This generates a specific steady-state magnetoresistance trace that is tied to the spin-flip conductance channel, and is opposite in sign to the ground state magnetoresistance term, as expected from spin excitation transition rules. The experimental 5.9 meV thermal energy barrier between the ground and excited spin states is confirmed by density functional theory, in line with macrospin phenomenological modeling of magnetotransport results. This low-voltage control over a spin chain's quantum state and spintronic contribution lay a path for transmitting spin wave-encoded information across molecular layers in devices. It should also stimulate quantum prospects for the antiferromagnetic spintronics and oxides electronics communities.     

Fault diagnosis of rolling bearing based on back propagation neural network optimized by cuckoo search algorithm

In order to improve the accuracy of rolling bearing fault diagnosis in mechanical equipment, a new fault diagnosis method based on back propagation neural network optimized by cuckoo search algorithm is proposed. This method use the global search ability of the cuckoo search algorithm to constantly search for the best weights and thresholds, and then give it to the back propagation neural network. In this paper, wavelet packet decomposition is used for feature extraction of vibration signals. The energy values of different frequency bands are obtained through wavelet packet decomposition, and they are input as feature vectors into optimized back propagation neural network to identify different fault types of rolling bearings. Through the three sets of simulation comparison experiments of Matlab, the experimental results show that, Under the same conditions, compared with the other five models, the proposed back propagation neural network optimized by cuckoo search algorithm has the least number of training iterations and the highest diagnostic accuracy rate. And in the complex classification experiment with the same fault location but different bearing diameters, the fault recognition correct rate of the back propagation neural network optimized by cuckoo search algorithm is 96.25%.

     

Solid state NMR study of segmented polymer networks: fine-tuning of phase morphology via their molecular design

Segmented polymer networks (SPNs) based on thermo-sensitive poly(N-isopropyl acryl amide) (PNIPAA) and poly(tetrahydrofuran) (PTHF) have been synthesized by free radical copolymerization of PTHF bis-macromonomers with N-isopropyl acrylamide. The nature of the polymerizable end group on the bis-macromonomer has been varied, respectively from acrylate to acrylamide end groups. The multiphase behaviour of the corresponding SPNs has been examined as a function of the nature of the end group by making use of solid-state ¹³C CP/MAS NMR relaxometry, ¹H wideline NMR relaxometry and dynamic mechanical analysis (DMA). When PTHF with acrylate end groups was used during the SPN formation, analysis of proton spin-lattice relaxation times (T_1H) and proton spin-lattice relaxation times in the rotating frame (T_1ρH) revealed phase separation with domain sizes larger than 5 nm when the PTHF fraction exceeds 10 wt%. Only for lower PTHF-amounts, the SPNs were homogeneous on the nanometer scale. On the other hand, when PTHF with acrylamide end groups was used as macromolecular cross-linker, the NMR results showed the absence of any domain formation for SPNs with PTHF fractions up to 50 wt%. The major impact of the molecular design on the ultimate phase morphology of bicomponent polymer networks has been confirmed in all cases by DMA-analysis.     

Prediction of Speech Quality Based on Resilient Backpropagation Artificial Neural Network

The paper presents a system for monitoring and assessment the speech quality in the IP telephony infrastructures using modular probes. The probes are placed at key nodes in the network where aggregating packet loss data. The system dynamically measures speech quality and results are collected on a central server. For data analysis we applied four-state Markov model for modeling the impact of network impairments on speech quality, afterwards, the resilient back propagation (Rprop) algorithm was used to train a neural network. Information about the speech quality are displayed in the form of automatically generated graphs and tables. The proposed solution has been tested with selected codecs and further generalizes the already presented concepts of the speech quality estimation in the IP environment.

     

Cadmium and zinc uptake by volunteer willow species and elder rooting in polluted dredged sediment disposal sites

Salix species and Sambucus nigra L. (elder) naturally invade dredged sediment landfills and are commonly encountered on substrates contaminated with heavy metals. Foliar concentrations of Cd and Zn in four Salix species and elder were explored in the field. Metal contents in dredged sediment derived soils were elevated compared to baseline concentration levels reported for Flanders. To evaluate foliar concentrations, reference data were compiled from observations in nurseries, young plantations and unpolluted sites with volunteer willow vegetation. Willows grown on polluted dredged sediment landfills showed elevated foliar Cd and Zn concentrations (>6.6 mg Cd/kg DW and >700 mg Zn/kg DW). This was not the case for elder. For willow, a significant relation was found between soil total Zn or Cd and foliar Zn or Cd, regardless of age, species, or clone. Willows proved to be useful bioindicators. Results indicated a possible threat in long-term habitat development of willow brushwood from transfer of Cd and Zn to the food web.