The presence of alkali cations in electrolyte solutions is known to impact the rate of electrocatalytic reactions, though the mechanism of such impact is not conclusively determined. We use density ...functional theory (DFT) to examine the specific adsorption of alkali cations to fcc(111) electrode surfaces, as specific adsorption may block catalyst sites or otherwise impact surface catalytic chemistry. Solvation of the cation-metal surface structure was investigated using explicit water models. Computed equilibrium potentials for alkali cation adsorption suggest that alkali and alkaline earth cations will specifically adsorb onto Pt(111) and Pd(111) surfaces in the potential range of hydrogen oxidation and hydrogen evolution catalysis in alkaline solutions.
Two-particle angular correlations is a robust tool that allows to study different physical properties of the system created in the high-energy collisions of both protons and heavy ions. ALICE ...experiment enable correlation measurements of identified particles for different collision systems, including less common probes, like ϕ mesons,
mesons and deuterons. In this document selected ALICE correlation measurements with identified particles are reported.
In animal communication research, vocal labeling refers to incidents in which an animal consistently uses a specific acoustic signal when presented with a specific object or class of objects. ...Labeling with learned signals is a foundation of human language but is notably rare in nonhuman communication systems. In natural animal systems, labeling often occurs with signals that are not influenced by learning, such as in alarm and food calling. There is a suggestion, however, that some species use learned signals to label conspecific individuals in their own communication system when mimicking individually distinctive calls. Bottlenose dolphins (Tursiops truncatus) are a promising animal for exploration in this area because they are capable of vocal production learning and can learn to use arbitrary signals to report the presence or absence of objects. Bottlenose dolphins develop their own unique identity signal, the signature whistle. This whistle encodes individual identity independently of voice features. The copying of signature whistles may therefore allow animals to label or address one another. Here, we show that wild bottlenose dolphins respond to hearing a copy of their own signature whistle by calling back. Animals did not respond to whistles that were not their own signature. This study provides compelling evidence that a dolphin’s learned identity signal is used as a label when addressing conspecifics. Bottlenose dolphins therefore appear to be unique as nonhuman mammals to use learned signals as individually specific labels for different social companions in their own natural communication system.
Bottlenose dolphins (Tursiops truncatus) produce individually distinctive signature whistles that broadcast the identity of the caller. Unlike voice cues that affect all calls of an animal, signature ...whistles are distinct whistle types carrying identity information in their frequency modulation pattern. Signature whistle development is influenced by vocal production learning. Animals use a whistle from their environment as a model, but modify it, and thus invent a novel signal. Dolphins also copy signature whistles of others, effectively addressing the whistle owner. This copying occurs at low rates and the resulting copies are recognizable as such by parameter variations in the copy. Captive dolphins can learn to associate novel whistles with objects and use these whistles to report on the presence or absence of the object. If applied to signature whistles, this ability would make the signature whistle a rare example of a learned referential signal in animals. Here, we review the history of signature whistle research, covering definitions, acoustic features, information content, contextual use, developmental aspects, and species comparisons with mammals and birds. We show how these signals stand out amongst recognition calls in animals and how they contribute to our understanding of complexity in animal communication.
Vocal production learning is a rare communication skill and has only been found in selected avian and mammalian species 1–4. Although humans use learned formants and voiceless sounds to encode most ...lexical information 5, evidence for vocal learning in other animals tends to focus on the modulation pattern of the fundamental frequency 3, 4. Attempts to teach mammals to produce human speech sounds have largely been unsuccessful, most notably in extensive studies on great apes 5. The limited evidence for formant copying in mammals raises the question whether advanced learned control over formant production is uniquely human. We show that gray seals (Halichoerus grypus) have the ability to match modulations in peak frequency patterns of call sequences or melodies by modifying the formants in their own calls, moving outside of their normal repertoire’s distribution of frequencies and even copying human vowel sounds. Seals also demonstrated enhanced auditory memory for call sequences by accurately copying sequential changes in peak frequency and the number of calls played to them. Our results demonstrate that formants can be influenced by vocal production learning in non-human vocal learners, providing a mammalian substrate for the evolution of flexible information coding in formants as found in human language.
•Vocal learning is crucial for language acquisition but relatively rare in animals•We tested whether gray seals can copy melodies and human formants•Seals were versatile vocal learners copying vowels and peak frequency of melodies•Seals used the same supra-laryngeal structures as humans when copying model sounds
Speech information in voiced human sounds is encoded in resonances in the supra-laryngeal tract that emphasize the energy content of selected harmonics. Stansbury and Janik show that gray seals can learn to modify emphasized frequency bands called formants to copy human vowels and melodies, making them an ideal model of human speech development.
Machine learning is a class of statistical techniques which has proven to be a powerful tool for modelling the behaviour of complex systems, in which response quantities depend on assumed controls or ...predictors in a complicated way.
In this paper, as our first purpose, we propose the application of machine learning to reconstruct incomplete or irregularly sampled data of time series indoor radon (222Rn). The physical assumption underlying the modelling is that Rn concentration in the air is controlled by environmental variables such as air temperature and pressure. The algorithms “learn” from complete sections of multivariate series, derive a dependence model and apply it to sections where the controls are available, but not the response (Rn), and in this way complete the Rn series. Three machine learning techniques are applied in this study, namely random forest, its extension called the gradient boosting machine and deep learning. For a comparison, we apply the classical multiple regression in a generalized linear model version. Performance of the models is evaluated through different metrics. The performance of the gradient boosting machine is found to be superior to that of the other techniques.
By applying learning machines, we show, as our second purpose, that missing data or periods of Rn series data can be reconstructed and resampled on a regular grid reasonably, if data of appropriate physical controls are available. The techniques also identify to which degree the assumed controls contribute to imputing missing Rn values.
Our third purpose, though no less important from the viewpoint of physics, is identifying to which degree physical, in this case environmental variables, are relevant as Rn predictors, or in other words, which predictors explain most of the temporal variability of Rn. We show that variables which contribute most to the Rn series reconstruction, are temperature, relative humidity and day of the year. The first two are physical predictors, while “day of the year” is a statistical proxy or surrogate for missing or unknown predictors.
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•Reconstruction of missing values in incomplete radon time series•Application of machine learning methods•Gradient Boosting Machine provided most precise result•Day of the year and temperature were most important predictors
The work concerns the application of non-contact measurement 3D scanning techniques in conjunction with a study of the microstructure of a forging die (made of W360 steel) for the production of an ...engine valve (made of NCF 3015 steel) in a hot die forging process in order to analyse the changes in the working surface of the tools and identify the destructive mechanisms. The detailed analysis presented in this paper examines the possibility of using 3D reverse engineering techniques for a direct quality control and examination of the changes in the surface layer geometry of the forging dies, based on the measurement of the geometry changes for cyclically collected forgings. The selected area of the valve forgings cyclically retrieved from the forging process was scanned with the use of an intermediate scanning method - reverse 3D scanning. On this basis, an analysis of the progressive material growth on the selected surface of the forgings was made, which also meant a loss of material on the tools. The performed analyses showed a good agreement of the geometrical properties of the surfaces (of the selected forgings representing the proceeding wear of the tool) and the geometrical defect of the working impression of the tool, based on the direct measurements during the production process. The reverse 3D scanning method developed by the authors has been repeatedly verified by them, which is confirmed by numerous studies and applications. The obtained results combined with SEM analyses and microhardness measurements enable a fast analysis of the forging tool life with respect to the quality and quantity (of material defect), which, in consequence, leads to significant economical savings.
•Measurement of tool wear on the basis of the use reverse method of scanning 3D.•Identification of the destructive mechanisms and phenomena in different areas of die.•Use the combine results of SEM and microhardness measurements as well as FE modelling for a more complete analysis.•Practical knowledge for forging-engineers about the improvement of durability forging tools.
Bottlenose dolphins (Tursiops truncatus) produce many vocalisations, including whistles that are unique to the individual producing them. Such "signature whistles" play a role in individual ...recognition and maintaining group integrity. Previous work has shown that humans can successfully group the spectrographic representations of signature whistles according to the individual dolphins that produced them. However, attempts at using mathematical algorithms to perform a similar task have been less successful. A greater understanding of the encoding of identity information in signature whistles is important for assessing similarity of whistles and thus social influences on the development of these learned calls. We re-examined 400 signature whistles from 20 individual dolphins used in a previous study, and tested the performance of new mathematical algorithms. We compared the measure used in the original study (correlation matrix of evenly sampled frequency measurements) to one used in several previous studies (similarity matrix of time-warped whistles), and to a new algorithm based on the Parsons code, used in music retrieval databases. The Parsons code records the direction of frequency change at each time step, and is effective at capturing human perception of music. We analysed similarity matrices from each of these three techniques, as well as a random control, by unsupervised clustering using three separate techniques: k-means clustering, hierarchical clustering, and an adaptive resonance theory neural network. For each of the three clustering techniques, a seven-level Parsons algorithm provided better clustering than the correlation and dynamic time warping algorithms, and was closer to the near-perfect visual categorisations of human judges. Thus, the Parsons code captures much of the individual identity information present in signature whistles, and may prove useful in studies requiring quantification of whistle similarity.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Dolphin communication is suspected to be complex, on the basis of their call repertoires, cognitive abilities, and ability to modify signals through vocal learning. Because of the difficulties ...involved in observing and recording individual cetaceans, very little is known about how they use their calls. This report shows that wild, unrestrained bottlenose dolphins use their learned whistles in matching interactions, in which an individual responds to a whistle of a conspecific by emitting the same whistle type. Vocal matching occurred over distances of up to 580 meters and is indicative of animals addressing each other individually.
The bottlenose dolphin, Tursiops truncatus, is one of very few animals that, through vocal learning, can invent novel acoustic signals and copy whistles of conspecifics. Furthermore, receivers can ...extract identity information from the invented part of whistles. In captivity, dolphins use such signature whistles while separated from the rest of their group. However, little is known about how they use them at sea. If signature whistles are the main vehicle to transmit identity information, then dolphins should exchange these whistles in contexts where groups or individuals join. We used passive acoustic localization during focal boat follows to observe signature whistle use in the wild. We found that stereotypic whistle exchanges occurred primarily when groups of dolphins met and joined at sea. A sequence analysis verified that most of the whistles used during joins were signature whistles. Whistle matching or copying was not observed in any of the joins. The data show that signature whistle exchanges are a significant part of a greeting sequence that allows dolphins to identify conspecifics when encountering them in the wild.
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