Friction on a single MoS2 nanotube

Friction was measured on a single molybdenum disulfide (MoS₂) nanotube and on a single MoS₂ nano-onion for the first time. We used atomic force microscopy (AFM) operating in ultra-high vacuum at room temperature. The average coefficient of friction between the AFM tip and MoS₂ nanotubes was found considerably below the corresponding values obtained from an air-cleaved MoS₂ single crystal or graphite. We revealed a nontrivial dependency of friction on interaction strength between the nanotube and the underlying substrate. Friction on detached or weakly supported nanotubes by the substrate was several times smaller (0.023 ± 0.005) than that on well-supported nanotubes (0.08 ± 0.02). We propose an explanation of a quarter of a century old phenomena of higher friction found for intracrystalline (0.06) than for intercrystalline slip (0.025) in MoS₂. Friction test on a single MoS₂ nano-onion revealed a combined gliding-rolling process.PACS, 62.20, 61.46.Fg, 68.37 Ps     

Output-associative RVM regression for dimensional and continuous emotion prediction

Many problems in machine learning and computer vision consist of predicting multi-dimensional output vectors given a specific set of input features. In many of these problems, there exist inherent temporal and spatial dependencies between the output vectors, as well as repeating output patterns and input–output associations, that can provide more robust and accurate predictors when modeled properly. With this intrinsic motivation, we propose a novel Output-Associative Relevance Vector Machine (OA-RVM) regression framework that augments the traditional RVM regression by being able to learn non-linear input and output dependencies. Instead of depending solely on the input patterns, OA-RVM models output covariances within a predefined temporal window, thus capturing past, current and future context. As a result, output patterns manifested in the training data are captured within a formal probabilistic framework, and subsequently used during inference. As a proof of concept, we target the highly challenging problem of dimensional and continuous prediction of emotions, and evaluate the proposed framework by focusing on the case of multiple nonverbal cues, namely facial expressions, shoulder movements and audio cues. We demonstrate the advantages of the proposed OA-RVM regression by performing subject-independent evaluation using the SAL database that constitutes naturalistic conversational interactions. The experimental results show that OA-RVM regression outperforms the traditional RVM and SVM regression approaches in terms of accuracy of the prediction (evaluated using the Root Mean Squared Error) and structure of the prediction (evaluated using the correlation coefficient), generating more accurate and robust prediction models.     

Evaluation of the quality of collaboration between the client and the therapist in phobia treatments

A growing number of empirical studies evaluate the influence of Mental Health (MH) technology on the clinical effectiveness, the therapeutic relationship (i.e., therapeutic alliance), and usability issues. However, to the authors’ knowledge, no studies have yet been performed regarding the influence of technology on the therapeutic process in terms of collaboration. This study evaluates the quality of collaboration between the client and therapist in Augmented Reality Exposure Therapy (ARET) context and the traditional, In Vivo Exposure Therapy (IVET) context with the Therapeutic Collaborative Scale (TCS). Twenty participants received an intensive session of cognitive behavioral therapy in either a technology-mediated therapeutic context or in a traditional therapeutic context. The results indicate that both therapeutic conditions show high collaboration scores. However, the asymmetry of roles between the therapist and the client under both conditions were detected. Also, a greater level of distraction was observed for therapists in ARET, which affected the quality of the therapists’ involvement in the therapeutic session. The implications of these results are discussed.     

Towards the automatic detection of spontaneous agreement and disagreement based on nonverbal behaviour: A survey of related cues,databases, and tools

While detecting and interpreting temporal patterns of nonverbal behavioural cues in a given context is a natural and often unconscious process for humans, it remains a rather difficult task for computer systems. Nevertheless, it is an important one to achieve if the goal is to realise a naturalistic communication between humans and machines. Machines that are able to sense social attitudes like agreement and disagreement and respond to them in a meaningful way are likely to be welcomed by users due to the more natural, efficient and human‐centred interaction they are bound to experience. This paper surveys the nonverbal behavioural cues that could be present during displays of agreement and disagreement; discusses a number of methods that could be used or adapted to detect these suggested cues; lists some publicly available databases these tools could be trained on for the analysis of spontaneous, audiovisual instances of agreement and disagreement, it examines the few existing attempts at agreement and disagreement classification, and finally discusses the challenges in automatically detecting agreement and disagreement.     

Intelligent forest fire monitoring system

This paper presents iForestFire, an Environmental Monitoring Information System for forest fire protection. The system is composed of several components, each having a particular function. Automatic fire detection is a crucial component of the system. It is based on various complex image processing algorithms. Complexity of the system also emerges from integration, based on multi agent technology, of different environment information. The presented system contributes to the environment protection and is in use in Croatia for several years.     

Shear bond strength to dentine of dental adhesives containing hydroxyapatite nano-fillers

The aim of this study was to compare the effect of hydroxyapatite (HAP) nano-rods (HAP_rods) and HAP nano-sticks (HAP_sticks) added to commercial adhesives on the macro-shear bond strength (SBS) to dentine and morphology of the adhesive–dentine interface. HAP was added to Single Bond Universal (SBU, 3M ESPE) and Te-Econom Bond (TeE; Ivoclar Vivadent), in the form of water suspensions to avoid agglomeration of nano-particles and to achieve HAP concentrations of 0.5, 1.0 and 1.5 wt%. Following a ‘total-etch’ or a ‘self-etch’ protocol, the adhesives were applied to flat dentine surface of 162 intact human, third molars (N = 6/group). Composite (Z250, 3M ESPE) was built-up using a split stainless steel mould, 3 mm in diameter. SBS was tested using a universal testing machine at 1 mm/min until fracture. Data were statistically analysed using two-way and one-way analysis of variance with Tukey’s post-test (α = 0.05). HAP_rods had no significant effect on SBS of the tested adhesives while HAP_sticks improved bond strength to dentine only of adhesives applied following the ‘total-etch’ total-etch protocol. SBS values of SBU containing 1% HAP_sticks (15.10 ± 2.96 MPa) and TeE containing 0.5% HAP_sticks applied following the total-etch protocol (12.96 ± 4.48 MPa) were higher than those of their respective control groups (10.36 ± 2.68 and 7.97 ± 3.64 MPa). Samples with higher SBS showed more ‘mixed’ failures. HAP nano-fillers may improve bond strength of total-etch adhesives without an adverse effect on adhesive dispersion on dentine and its ability to infiltrate dentinal tubules.     

Relative importance of macrophyte community versus water quality variables for predicting fish assemblages in coastal wetlands of the Laurentian Great Lakes

Fish have been shown to be sensitive indicators of environmental quality in Great Lakes coastal wetlands. Fish composition also reflects aquatic macrophyte communities, which provide them with critical habitat. Although investigators have shown that the relationship between water quality and fish community structure can be used to indicate wetland health, we speculate that this relationship is a result of the stronger, more direct relationship between water quality and macrophytes, together with the ensuing interconnection between macrophyte and fish assemblages. In this study, we use data collected from 115 Great Lakes coastal marshes to test the hypothesis that plants are better predictors of fish species composition than is water quality. First we use canonical correspondence analysis (CCA) to conduct an ordination of the fish community constrained by water quality parameters. We then use co-correspondence analysis (COCA) to conduct a direct ordination of the fish community with the plant community data. By comparing the statistic ‘percent fit,’ which refers to the cumulative percentage variance of the species data, we show that plants are consistently better predictors of the fish community than are water quality variables in three separate trials: all wetlands in the Great Lakes basin (whole: 21.2% vs 14.0%; n = 60), all wetlands in Lakes Huron and Superior (Upper: 20.3% vs 18.8%; n =  32), and all wetlands in Georgian Bay and the North Channel (Georgian Bay: 18% vs 17%; n =  70). This is the largest study to directly examine plant–fish interactions in wetlands of the Great Lakes basin.     

Characterisation of flavour compounds in Biska – a herbal spirit produced with mistletoe

Mistletoe (Viscum album) is mostly used in its herbal form owing to its content of diverse biologically active substances. In Croatia's Adriatic region, it is used for production of Biska, a strong alcoholic beverage produced by maceration of mistletoe in spirit or brandy, with or without the addition of honey. In this work, Biska was characterised by analysis of aroma compounds and physicochemical parameters and is the first study of the aroma profile of a strong alcoholic beverage produced with mistletoe. The aroma of 14 samples of Biska – five commercial and nine homemade – was analysed using GC/MS with solid‐phase microextraction. A total of 166 aroma compounds were detected. Major components were ethyl esters (medium and long chain fatty acids), fatty alcohols, isopropyl myristate, aldehyde decanal and some terpenes. The distribution of aroma compounds was evaluated by principal component analysis. Some significant differences in physicochemical properties were observed including ethanol concentration (28–44% v/v) and pH (4.4–6.5). Although the colour of Biska was light brown with greenish reflections, there were differences in chromatic parameters. The commercial samples were darker and were less transparent than homemade Biska, perhaps reflecting more extensive maceration/extraction or the addition of caramel. © 2018 The Institute of Brewing & Distilling     

Post Audit Analysis of a Groundwater Level Prediction Model in Developed Semiconfined Aquifer System

This paper assesses groundwater recharge under conditions of long-term groundwater pumping at the Ravnik pumping site in Croatia and analyses the groundwater level prediction model used in prior aquifer modelling. The results of model calibration revealed a very low net infiltration rate at the start of the pumping site’s operation. As the operation continued, the net infiltration rate slowly increased, while the percentage of infiltrated rainfall scaled up with increasing pumping rates. The predicted recharge of the covering aquitard amounts approximately 14–15 % of the mean annual precipitation. The aquifer recharge takes place from aquitard by seepage. A subsequent simulation of the pumping site’s operation was performed for the 9 years period on the assumption that the pumping rates and the groundwater recharge would be the same as those recorded during the final calibration years. Results show that the post audit measured levels correspond relatively well to the predicted levels and that increasing of the pumping rate causes changes in the water budget in advantage of net groundwater recharge as a consequence of spreading recharge area outside of previous model boundaries.