Microfibrillated-cellulose-modified urea-formaldehyde adhesives with different F/U molar ratios for wood-based composites

The relationship between urea–formaldehyde (UF) adhesives (E0 and E1 classes) and microfibrillated-cellulose (MFC) and its effect on the mechanical properties of laminated veneer lumbers (LVLs) were investigated. Bending strength, modulus of elasticity in bending, and tensile shear strength of the LVLs were determined. The morphology, gel time, viscosity, and acidity of the modified UF adhesives were also investigated. The tensile shear strength of the LVLs significantly increased (2.89 to 3.35 N/mm²) as the MFC suspension was increased to 3.75 g in the 7.50 g E0 class UF adhesive, while it slightly increased (3.10 to 3.16 N/mm²) as the MFC suspension was increased to 2.5 g in the 8.75 g E1 class UF adhesive. The results showed that the bond performance of the E0 class adhesive with the MFC was better than that of the E1 class adhesive with the MFC. The MFC was found to be valuable nanoscale reinforcing filler for the improvement of bond performance of UF adhesive, in particular, E0 class adhesive, in the production of wood-based composites.     

Determination of CaCO<Subscript>3</Subscript> and SiO<Subscript>2</Subscript> content in the binders of historic lime mortars

The binders of historic mortars composed of small grain sized silica (SiO₂) and carbonated lime (CaCO₃) are considered as the main part that give hydraulic character and high strength to the mortar. In this study, FTIR, SEM–EDS, LIBS and XRD spectroscopy were used to find out the weight ratios of CaCO₃ to SiO₂ in the binders of historic lime mortars. For this purpose, a series of pure calcium carbonate and silica mixture were prepared in ten combinations in varying ratios from 0.5 to 5. Calibration curve was prepared for each analysis by plotting the peak area or intensity ratios of CaCO₃ to SiO₂ versus the weight ratios of CaCO₃ to SiO₂. A good linear correlation coefficient was obtained for each analysis respectively. The analyses were then tested on the binder of the Roman mortar samples. The results indicated that FTIR, SEM–EDS and LIBS spectroscopy are convenient tools to determine the weight ratios of CaCO₃ to SiO₂ in the binders of mortars. But XRD spectroscopy is not convenient for quantitative analysis of binders due to the presence of varied amounts of amorphous or poor crystalline silica in their compositions.     

Waste pine cones as a source of reinforcing fillers for thermoplastic composites

In this study, we evaluated some physical and mechanical properties of polypropylene (PP) composites reinforced with pine‐cone flour and wood flour. Five types of wood–plastic composites (WPCs) were prepared from mixtures of cone flour, wood flour, PP, and a coupling agent. The water resistance and flexural properties of the composites were negatively affected by an increase in cone‐flour content. Extractives in the cone flour had a significant effect on the flexural properties of the WPCs. However, the flexural properties and water resistance of the WPC samples were not significantly affected by the addition of 10 wt % of the cone flour when compared to the WPC samples made from wood flour. © 2010 Wiley Periodicals, Inc. J Appl Polym Sci, 2010     

Thermal Decomposition and Ignition of PBXN‐110 Plastic‐Bonded Explosive

Due to safety requirements, insensitive behavior in slow or fast thermal heating (cook‐off) conditions is a desired behavior for today’s munitions. The ignition time of munitions under slow or fast cook‐off conditions is an important parameter in the design of insensitive munitions. The critical temperature, which mainly depends on the chemical, physical, and the geometrical properties of the energetic material, is the determining factor whether the material will end up with thermal initiation or not, when it is exposed to an external heat source. In this study a slow cook‐off test setup is designed and developed and the tests for a generic munition containing PBXN‐110 plastic‐bonded explosive are performed in order to obtain temperature distribution in the test item, ignition time, ignition temperature, and ignition location. In this paper the development procedure and the experimental results of the slow cook‐off tests are explained. Moreover, the kinetic parameters such as activation energy and pre‐exponential factor for the plastic‐bonded explosive obtained from the TGA tests are presented.     

Unified spray, foam and air bubbles for particle-based fluids

We present a new model for diffuse material, i.e. water–air mixtures, that can be combined with particle-based fluids. Diffuse material is uniformly represented with particles which are classified into spray, foam and air bubbles. Physically motivated rules are employed to generate, advect and dissipate diffuse material. The approach is realized as a post-processing step which enables efficient processing and versatile handling. As interparticle forces and the influence of diffuse material onto the fluid are neglected, large numbers of diffuse particles are efficiently processed to realize highly detailed small-scale effects. The presented results show that our approach can significantly improve the visual realism of large-scale fluid simulations.     

The Politics of the City Image: The Resurrection of the Kul-Sharif Mosque in the Kazan Kremlin (1995-2005)

This article studies the resurrection of the Kul-Sharif Mosque in the Kazan Kremlin in the context of the political transformations that Russia and the Republic of Tatarstan went through in the 1990s. The original structure was destroyed in 1552 when Ivan the Terrible conquered the capital of then Kazan Khanate. In 1990 the Republic of Tatarstan declared its sovereign status. The mosque was cited as a symbol of the heroic resistance to the Russian invaders, restoration of the Tatarstani statehood and cultural revival of the Tatar nation. The mosque became an essential part of the new image of Kazan, the capital city of Tatarstan. The article explores the mechanisms that brought the project into being and attempts to conceptualize the links between the image of the city and political and economic forces of the society.     

Intelligent sensor system for discrimination of material type using neural networks

Neural network based classification of material type even with the variation in the sensor parameter is investigated in this paper. The sensor is developed by means of a lightweight plunger probe and an optical mouse sensor. An experimental prototype was developed which involves bouncing or hopping of the plunger based impact probe freely on the plain surface of an object under test. The experiment is conducted to obtain the bouncing signals for plain surface of an objects kept at different distances from the probe. During the bouncing of the probe, time varying signals are generated from optical mouse that are recorded in data files on PC. Some dominant unique features are then extracted using signal processing tools to optimize neural network based classifier. The time and features of bouncing signal are related to the material type, and each material has a unique set of such properties. It is found that the sensor system is intelligent due to its ability to classify the material type even with the variation in the sensor parameter (distance between the sensor probe and plain objects). The classifiers are developed using two neural networks configurations, namely a well-known Multi-layer Perceptron Neural Networks (MLP NN), and Radial Basis Function Neural Networks (RBF NN). MLP NN and RBF NN models are designed to maximize accuracy under the constraints of minimum network dimension.The optimal parameters of MLP NN and RBF NN models based on various performance measures that include percentage classification accuracy (PCLA) on the testing data, and area under Receiver Operating Characteristics (ROC), and are determined. For the sensor data set, the PCLA of both the classifiers are found reasonable consistently in respect of rigorous testing using different data partitions. The areas under the ROC curves are close to unity. Performances of the two classifiers have been compared. It has been found that the RBF NN is more robust to noise, and epochs required for training are very less as compared to that for MLP NN.     

Costs and benefits of behavioral specialization

In this work, we study behavioral specialization in a swarm of autonomous robots. In the studied swarm, robots have to carry out tasks of different types that appear stochastically in time and space in a given environment. We consider a setting in which a robot working repeatedly on tasks of the same type improves its performance on them due to learning. Robots can exploit learning by adapting their task selection behavior, that is, by selecting with higher probability tasks of the type on which they have improved their performance. This adaptation of behavior is called behavioral specialization. We employ a simple task allocation strategy that allows a swarm of robots to behaviorally specialize. We study the influence of different environmental parameters on the performance of the swarm and show that the swarm can exploit learning successfully. However, there is a trade-off between the benefits and the costs of specialization. We study this trade-off in multiple experiments using different swarm sizes. Our experimental results indicate that spatiality has a major influence on the costs and benefits of specialization.     

Web video classification with visual and contextual semantics

On the social Web, the amount of video content either originated from wireless devices or previously received from media servers has increased enormously in the recent years. The astounding growth of Web videos has stimulated researchers to propose new strategies to organize them into their respective categories. Because of complex ontology and large variation in content and quality of Web videos, it is difficult to get sufficient, precisely labeled training data, which causes hindrance in automatic video classification. In this paper, we propose a novel content‐ and context‐based Web video classification framework by rendering external support through category discriminative terms (CDTs) and semantic relatedness measure (SRM). Mainly, a three‐step framework is proposed. Firstly, content‐based video classification is proposed, where twofold use of high‐level concept detectors is leveraged to classify Web videos. Initially, category classifiers induced from VIREO‐374 detectors are trained to classify Web videos, and then concept detectors with high confidence for each video are mapped to CDT through SRM‐assisted semantic content fusion function to further boost the category classifiers, which intuitively provide a more robust measure for Web video classification. Secondly, a context‐based video classification is proposed, where twofold use of contextual information is also harnessed. Initially, cosine similarity and then semantic similarity are measured between text features of each video and CDT through vector space model (VSM)‐ and SRM‐assisted semantic context fusion function, respectively. Finally, classification results from content and context are fused to compensate for the shortcomings of each other, which enhance the video classification performance. Experiments on large‐scale video dataset validate the effectiveness of the proposed solution.