A Study of Slipper and Rail Wear Interaction at Low Speed

The wear research presented in the work consists of results gathered from adapting a FEM based on a Holloman High Speed Test Track (HHSTT) mission executed in January 2008. The FEM consists of a VascoMax slipper sliding on a stationary AISI-1080 steel rail. The slipper is slid along the rail at speeds of 20 m/s and 40 m/s with complementary vertical velocities of -0.079 m/s and -0.059 m/s, respectively. The surface roughness caused by features such as asperities and valleys of the materials, is simulated in this model by five asperities, 1 micron to 5 microns on the rail and a slipper with a leading edge radius of 6 microns. The free space between the surfaces, caused by the interaction of the asperities and valleys, is approximated by three gap spacings of 0.5 microns, 1.0 micron, and 1.5 microns. This study also explores three different Mesh refinements in the wearing portion of slipper to uncover any mesh dependence affecting the amount of wear. Additional simulations were completed to highlight the effects of using more accurate material definitions, tables of specific heat capacities were used instead of static values and more applicable, lower strain rate Johnson Cook plasticity parameters were. In a final three simulations, asperities with a smooth root fillet are used to highlight the model behavioral differences due to this simple change

Basic fibroblast growth factor mediates carotid plaque instability through metalloproteinase-2 and –9 expression

OBJECTIVE(S): We hypothesized that basic fibroblast growth factor (bFGF) may exert a role in carotid plaque instability by regulating the expression of matrix metalloproteinases (MMP). METHODS: Plaques obtained from 40 consecutive patients undergoing carotid endarterectomy were preoperatively classified as soft or hard. Serum bFGF was pre- and postoperatively measured. The release of MMP-2 and MMP-9 in the blood serum, and the activity, production and expression in the carotid specimens was analyzed. Specific anti-bFGF inhibition tests were performed in vitro on human umbilical artery smooth muscle cells (HUASMC) to evaluate the role of bFGF in the activity, production and expression of MMP-2 and -9. RESULTS: Twenty-one (53%) patients had a soft carotid plaque and 19 (48%) a hard plaque. Preoperative bFGF serum levels were higher in patients with soft plaques [soft=34 (28-39) pg/mL and hard=20 (17-22) pg/mL-p<0.001] and postoperatively returned to normal values (when compared to 10 healthy volunteers). The serum levels of MMP-2 in patients' with soft plaques were higher than those in patients' with hard plaques [soft=1222 (1190-1252) ng/mL and hard=748 (656-793)ng/mL-p<0.0001]. MMP-9 serum values were 26 (22-29) ng/mL for soft plaques and 18 (15-21) ng/mL for hard plaques (p<0.0001). We found increased activity, production and expression of MMP-2 and -9 in soft plaques compared to hard plaques (p<0.001). In vitro inhibition tests on HUASMC showed the direct influence of bFGF on the activity, production and expression of MMP-2 and -9 (p<0.001). CONCLUSIONS: bFGF seems to exert a key role in carotid plaque instability regulating the activity, production and expression of MMP thus altering the physiologic homeostasis of the carotid plaque

Use of Narbonne vetch grain (Vicia narbonensis) in Charolaise bull feeding: effect on in vitam and post mortem performances and on quality of meat

The addition of Narbonne vetch grain in the diet of 20 Charolaise bulls (420 ± 44 BW), during the finishing period (100 days), was investigated to study the effects on in vitam and post mortem performances and on quality of meat. No significant differences for ADWG ("Vetch" 1.55 kg/d vs. "Control" 1.40 kg/d; P = 0.34), FCR ("Vetch" 7.10 kg/kg vs. "Control" 7.49 kg/kg; P = 0.93) and yield ("Vetch" 60.97% vs. "Control" 62.13%: P = 0.33) were observed. The nutritional and physical characteristics of Longissimus thoracis et lumborum muscle showed no significant differences for Crude Protein ("Vetch" 22.47 vs. "Control" 22.36; P = 0.80), Fat ("Vetch" 2.56 vs "Control" 2.49; P = 0.87), Energy ("Vetch" 1130 vs. "Control" 1119; P = 0.79), TBARs ("Vetch" 0.063 vs. "Control" 0.069; P = 0.72), Luminosity ("Vetch" 46.47 vs. "Control" 43.43; P = 0.11), Hue ("Vetch" 0.6 vs. "Control" 0.6; P = 0.16), Chrome ("Vetch" 27.64 vs. "Control" 25.5; P = 0.56), Cooking loss ("Vetch" 26.77% vs. "Control" 28.62%; P = 0.33) and WBS ("Vetch" 3.4 kg f/cm2 vs. "Control" 3.3 kg f/cm2; P = 0.76). These preliminary results show interest towards the vetch grain as a protein source in the finishing period of bulls

Achieving interoperability through semantics-based technologies: the instant messaging case

The success of pervasive computing depends on the ability to compose a multitude of networked applications dynamically in order to achieve user goals. However, applications from different providers are not able to interoperate due to incompatible interaction protocols or disparate data models. Instant messaging is a representative example of the current situation, where various competing applications keep emerging. To enforce interoperability at runtime and in a non-intrusive manner, mediators are used to perform the necessary translations and coordination between the heterogeneous applications. Nevertheless, the design of mediators requires considerable knowledge about each application as well as a substantial development effort. In this paper we present an approach based on ontology reasoning and model checking in order to generate correct-by-construction mediators automatically. We demonstrate the feasibility of our approach through a prototype tool and show that it synthesises mediators that achieve efficient interoperation of instant messaging applications

ConflictNET: End-to-End Learning for Speech-Based Conflict Intensity Estimation

Computational paralinguistics aims to infer human emotions, personality traits and behavioural patterns from speech signals. In particular, verbal conflict is an important example of human-interaction behaviour, whose detection would enable monitoring and feedback in a variety of applications. The majority of methods for detection and intensity estimation of verbal conflict apply off-the-shelf classifiers/regressors to generic hand-crafted acoustic features. Generating conflict-specific features requires refinement steps and the availability of metadata, such as the number of speakers and their speech overlap duration. Moreover, most techniques treat feature extraction and regression as independent modules, which require separate training and parameter tuning. To address these limitations, we propose the first end-to-end convolutional-recurrent neural network architecture that learns conflict-specific features directly from raw speech waveforms, without using explicit domain knowledge or metadata. Additionally, to selectively focus the model on portions of speech containing verbal conflict instances, we include a global attention interface that learns the alignment between layers of the recurrent network. Experimental results on the SSPNet Conflict Corpus show that our end-to-end architecture achieves state-of-the-art performance in terms of Pearson Correlation Coefficient