Management of corn stalk waste as reinforcement for polypropylene injection moulded composites

The main objective of this study was the management of corn stalk waste as reinforcement for polypropylene (PP) injection moulded composites as an alternative to wood flour and fibers. In the first step, corn stalk waste was subjected to various treatments, and four different corn stalk derivatives (flour and fibers) able to be used as reinforcement of composite materials were prepared and characterized. These derivatives are corn stalk flour, thermo-mechanical, semi-chemical, and chemical fibers. They were characterized in terms of their yield, lignin content, Kappa number, fiber length/diameter ratio, fines, coarseness, viscosity, and the length at the break of a standard sheet of paper. Results showed that the corn stalk derivatives have different physico-chemical properties. In the second step, the prepared flour and fibers were explored as a reinforcing element for PP composites. Coupled and non-coupled PP composites were prepared and tested for tensile properties. For overall trend, with the addition of a coupling agent, tensile properties of composites significantly improved, as compared with non-coupled samples. In addition, a morphological study revealed the positive effect of the coupling agent on the interfacial bonding. The composites prepared with semichemical fiber gave better results in comparison with the rest of the corn stalk derivatives due to its chemical characteristics

Research on the suitability of organosolv semi-chemical triticale fibers as reinforcement for recycled hdpe composites

The main objective of this research was to study the feasibility of incorporating organosolv semi-chemical triticale fibers as the reinforcing element in recycled high density polyethylene (HDPE). In the first step, triticale fibers were characterized in terms of chemical composition and compared with other biomass species (wheat, rye, softwood, and hardwood). Then, organosolv semi-chemical triticale fibers were prepared by the ethanolamine process. These fibers were characterized in terms of its yield, kappa number, fiber length/diameter ratio, fines, and viscosity; the obtained results were compared with those of eucalypt kraft pulp. In the second step, the prepared fibers were examined as a reinforcing element for recycled HDPE composites. Coupled and non-coupled HDPE composites were prepared and tested for tensile properties. Results showed that with the addition of the coupling agent maleated polyethylene (MAPE), the tensile properties of composites were significantly improved, as compared to non-coupled samples and the plain matrix. Furthermore, the influence of MAPE on the interfacial shear strength (IFSS) was studied. The contributions of both fibers and matrix to the composite strength were also studied. This was possible by the use of a numerical iterative method based on the Bowyer-Bader and Kelly-Tyson equations

Hybrid coordination of reinforcement learning-based behaviors for AUV control

This paper proposes a hybrid coordination method for behavior-based control architectures. The hybrid method takes advantages of the robustness and modularity in competitive approaches as well as optimized trajectories in cooperative ones. This paper shows the feasibility of applying this hybrid method with a 3D-navigation to an autonomous underwater vehicle (AUV). The behaviors are learnt online by means of reinforcement learning. A continuous Q-learning implemented with a feed-forward neural network is employed. Realistic simulations were carried out. The results obtained show the good performance of the hybrid method on behavior coordination as well as the convergence of the behaviors

A behavior-based scheme using reinforcement learning for autonomous underwater vehicles

This paper presents a hybrid behavior-based scheme using reinforcement learning for high-level control of autonomous underwater vehicles (AUVs). Two main features of the presented approach are hybrid behavior coordination and semi on-line neural-Q_learning (SONQL). Hybrid behavior coordination takes advantages of robustness and modularity in the competitive approach as well as efficient trajectories in the cooperative approach. SONQL, a new continuous approach of the Q_learning algorithm with a multilayer neural network is used to learn behavior state/action mapping online. Experimental results show the feasibility of the presented approach for AUVs

Policy gradient based Reinforcement Learning for real autonomous underwater cable tracking

This paper proposes a field application of a high-level reinforcement learning (RL) control system for solving the action selection problem of an autonomous robot in cable tracking task. The learning system is characterized by using a direct policy search method for learning the internal state/action mapping. Policy only algorithms may suffer from long convergence times when dealing with real robotics. In order to speed up the process, the learning phase has been carried out in a simulated environment and, in a second step, the policy has been transferred and tested successfully on a real robot. Future steps plan to continue the learning process on-line while on the real robot while performing the mentioned task. We demonstrate its feasibility with real experiments on the underwater robot ICTINEU AUV

Autonomous underwater vehicle control using reinforcement learning policy search methods

Autonomous underwater vehicles (AUV) represent a challenging control problem with complex, noisy, dynamics. Nowadays, not only the continuous scientific advances in underwater robotics but the increasing number of subsea missions and its complexity ask for an automatization of submarine processes. This paper proposes a high-level control system for solving the action selection problem of an autonomous robot. The system is characterized by the use of reinforcement learning direct policy search methods (RLDPS) for learning the internal state/action mapping of some behaviors. We demonstrate its feasibility with simulated experiments using the model of our underwater robot URIS in a target following task

Towards Direct Policy Search Reinforcement Learning for Robot Control

This paper proposes a high-level reinforcement learning (RL) control system for solving the action selection problem of an autonomous robot. Although the dominant approach, when using RL, has been to apply value function based algorithms, the system here detailed is characterized by the use of direct policy search methods. Rather than approximating a value function, these methodologies approximate a policy using an independent function approximator with its own parameters, trying to maximize the future expected reward. The policy based algorithm presented in this paper is used for learning the internal state/action mapping of a behavior. In this preliminary work, we demonstrate its feasibility with simulated experiments using the underwater robot GARBI in a target reaching task

A Monte Carlo-Based Fiber Tracking Algorithm using Diffusion Tensor MRI

Diffusion tensor magnetic resonance imaging, which measures directional information of water diffusion in the brain, has emerged as a powerful tool for human brain studies. In this paper, we introduce a new Monte Carlo-based fiber tracking approach to estimate brain connectivity. One of the main characteristics of this approach is that all parameters of the algorithm are automatically determined at each point using the entropy of the eigenvalues of the diffusion tensor. Experimental results show the good performance of the proposed approach

Universality class of fiber bundles with strong heterogeneities

We study the effect of strong heterogeneities on the fracture of disordered materials using a fiber bundle model. The bundle is composed of two subsets of fibers, i.e. a fraction 0 ≤ α ≤ 1 of fibers is unbreakable, while the remaining 1 - α fraction is characterized by a distribution of breaking thresholds. Assuming global load sharing, we show analytically that there exists a critical fraction of the components αc which separates two qualitatively diferent regimes of the system: below αc the burst size distribution is a power law with the usual exponent Ƭ= 5/2, while above αc the exponent switches to a lower value Ƭ = 9/4 and a cutoff function occurs with a diverging characteristic size. Analyzing the macroscopic response of the system we demonstrate that the transition is conditioned to disorder distributions where the constitutive curve has a single maximum and an inflexion point defining a novel universality class of breakdown phenomena

Avalanche dynamics of fiber bundle models

We present a detailed analytical and numerical study of the avalanche distributions of the continuous damage fiber bundle model CDFBM . Linearly elastic fibers undergo a series of partial failure events which give rise to a gradual degradation of their stiffness. We show that the model reproduces a wide range of mechanical behaviors. We find that macroscopic hardening and plastic responses are characterized by avalanche distributions, which exhibit an algebraic decay with exponents between 5/2 and 2 different from those observed in mean-field fiber bundle models. We also derive analytically the phase diagram of a family of CDFBM which covers a large variety of potential avalanche size distributions. Our results provide a unified view of the statistics of breaking avalanches in fiber bundle models

Mean intrinsic tensile properties of stone groundwood fibers from softwood

Stone groundwood (SGW) is a fibrous matter commonly prepared in a high yield process, and mainly used for papermaking applications. In this work, the use of SGW fibers is explored as reinforcing element of polypropylene (PP) composites. Due to its chemical and superficial features, the use of coupling agents is needed for a good adhesion and stress transfer across the fiber-matrix interface. The intrinsic strength of the reinforcement is a key parameter to predict the mechanical properties of the composite and to perform an interface analysis. The main objective of the present work was the determination of the intrinsic tensile strength of stone groundwood fibers. Coupled and non-coupled PP composites from stone groundwood fibers were prepared. The influence of the surface morphology and the quality at interface on the final properties of the composite was analyzed and compared to that of fiberglass PP composites. The intrinsic tensile properties of stone groundwood fibers, as well as the fiber orientation factor and the interfacial shear strength of the current composites were determined

Impairment of mossy fiber long-term potentiation and associative learning in pituitary adenylate cyclase activating polypeptide type I receptor-deficient Mice

The pituitary adenylate cyclase activating polypeptide (PACAP) type I receptor (PAC1) is a G-protein-coupled receptor binding the strongly conserved neuropeptide PACAP with 1000-fold higher affinity than the related peptide vasoactive intestinal peptide. PAC1-mediated signaling has been implicated in neuronal differentiation and synaptic plasticity. To gain further insight into the biological significance of PAC1-mediated signaling in vivo, we generated two different mutant mouse strains, harboring either a complete or a forebrain-specific inactivation of PAC1. Mutants from both strains show a deficit in contextual fear conditioning, a hippocampus-dependent associative learning paradigm. In sharp contrast, amygdala-dependent cued fear conditioning remains intact. Interestingly, no deficits in other hippocampus-dependent tasks modeling declarative learning such as the Morris water maze or the social transmission of food preference are observed. At the cellular level, the deficit in hippocampus-dependent associative learning is accompanied by an impairment of mossy fiber long-term potentiation (LTP). Because the hippocampal expression of PAC1 is restricted to mossy fiber terminals, we conclude that presynaptic PAC1-mediated signaling at the mossy fiber synapse is involved in both LTP and hippocampus-dependent associative learning.

CB1 cannabinoid receptor modulates MDMA acute responses and reinforcement

Background: 3, 4-methylenedioxymethamphetamine (MDMA) is a popular recreational drug widely abused by young people. The endocannabinoid system is involved in the addictive processes induced by different drugs of abuse. However, the role of this system in the pharmacological effects of MDMA has not been yet clarified.Methods: Locomotion, body temperature and anxiogenic-like responses were evaluated after acute MDMA administration in CB1 knockout mice. Additionally, MDMA rewarding properties were investigated in the place conditioning and the intravenous self-administration paradigms. Extracellular levels of DA in the nucleus accumbens were also analyzed after a single administration of MDMA by in vivo microdialysis. Results: Acute MDMA administration increased locomotor activity, body temperature and anxiogenic-like responses in wild type mice, but these responses were lower or abolished in knockout animals. MDMA produced similar conditioned place preference and increased dopamine extracellular levels in the nucleus accumbens in both genotypes. Nevertheless, CB1 knockout mice failed to self-administer MDMA at any of the doses used. Conclusions: These results indicate that CB1 cannabinoid receptors play an important role in the acute prototypical effects of MDMA, and are essential in the acquisition of an operant behavior to self-administer this drug.

Prediction of subcutaneous fatty acid composition from ham of CLA fed boars and gilts using a NIRS fiber optic probe

The aim of this work was the use of NIR technology by direct application of a fiber optic probe on back fat to analyze the fatty acid composition of CLA fed boars and gilts. 265 animals were fed 3 different diets and the fatty acid profile of back fat from Gluteus medius was analyzed using gas chromatography and FT-NIR. Spectra were acquired using a Bruker Optics Matrix-F duplex spectrometer equipped with a fiber optic probe (IN-268-2). Oleic and stearic fatty acids were predicted accurately; myristic, vaccenic and linoleic fatty acids were predicted with lower accuracy, while palmitic and α-linolenic fatty acids were poorly predicted. The relative percentage of fatty acids and NIR spectra showed differences in fatty acid composition of back fat from pigs fed CLA which increased the relative percentage of SFA and PUFA while MUFA decreased. Results suggest that a NIR fiber optic probe can be used to predict total saturated and unsaturated fatty acid composition, as well as the percentage of stearic and oleic. NIR showed potential as a rapid and easily implemented method to discriminate carcasses from animals fed different diets.

Thermoplastic Starch-based Composites Reinforced with Rape Fibers: Water Uptake and Thermomechanical Properties

Fully biodegradable composite materials were obtained through reinforcement of a commercially available thermoplastic starch (TPS) matrix with rapeseed fibers (RSF). The influence of reinforcement content on the water sorption capacity, as well as thermal and thermo-mechanical properties of composites were evaluated. Even though the hydrophilic character of natural fibers tends to favor the absorption of water, results demonstrated that the incorporation of RSF did not have a significant effect on the water uptake of the composites. DSC experiments showed that fibers restricted the mobility of the starch macromolecules from the TPS matrix, hence reducing their capacity to crystallize. The viscoelastic behaviour of TPS was also affected, and reinforced materials presented lower viscous deformation and recovery capacity. In addition, the elasticity of materials was considerably diminished when increasing fiber content, as evidenced in the TMA and DMTA measurements