Molecular-level dispersion of graphene into epoxidized natural rubber: Morphology, interfacial interaction and mechanical reinforcement

The interfacial interaction of composites dominates the properties of polymeric/inorganic nanocomposites. Herein, epoxy and hydroxyl groups are introduced into the natural rubber (NR) molecular chains to anchor oxygenous functional groups on the surface of graphene oxide (GO) sheets and therefore enhance the interfacial interaction between GO and rubber. From the morphological observation and interaction analysis, it is found that epoxidized natural rubber (ENR) latex particles are assembled onto the surfaces of GO sheets by employing hydrogen bonding interaction as driving force. This self-assembly depresses restacking and agglomeration of GO sheets and leads to homogenous dispersion of GO within ENR matrix. The formation of hydrogen bonding interface between ENR and GO demonstrates a significant reinforcement for the ENR host. Compared with those of pure ENR, the composite with 0.7 wt% GO loading receives 87% increase in tensile strength and 8.7 fold increase in modulus at 200% elongation after static in-situ vulcanization.

New insights into the structures of diorganotellurium Oxides. The first polymeric diorganotelluroxane[(p-MeOC6H4)2TEO]n

The characterization of the previously reported diorganotellurium oxides R₂TeO (R = Ph (1) and p-MeOC₆H₄ (2)) was revisited by osmometric molecular weight determinations, ¹²⁵Te NMR spectroscopy, and electrospray spectrometry (ESMS) in solution and by 125Te MAS NMR spectroscopy in the solid state. The single-crystal X-ray structure of 2 revealed a polymeric arrangement that features a zigzag configured Te-O backbone without any secondary Te···O interactions. In solution 1 and 2 exist predominantly as monomers but appear to be in equilibrium with higher oligomers to a minor extent.

Reinforcement sensitivity theory (RST) and body change behaviour in males

According to the unrevised Reinforcement Sensitivity Theory two motivational systems shape personality: a behavioural approach system (BAS) that determines sensitivity to rewards, and a behavioural inhibition system (BIS) that determines sensitivity to punishments. The role of reinforcement sensitivity in body change behaviour in males was explored with a non-clinical sample of 120 men aged 18–40 years. Self-reported symptoms of unhealthy weight loss (weight preoccupation, fasting, bingeing/purging) and body development (muscle/size preoccupation, obligatory exercise, use of chemical supplements) were regressed on measures of BAS and BIS sensitivity. Significant relationships were observed between BAS sensitivity and body development, and between BIS sensitivity and weight loss. These relationships were mediated by internalization of the athletic/muscular ideal, body comparisons, the importance of achieving one’s ideal or ‘best possible’ body (in the case of BAS but not BIS), and body dissatisfaction (in the case of BIS but not BAS). These results support the proposition that body development in males is influenced by sensitivity to rewards associated with achieving a certain body shape, and that weight loss is influenced by sensitivity to punishments associated with possessing an unsatisfactory body shape.

Investigation of effective material properties in composites with internal defect or reinforcement particles

This paper is concerned with the investigation of the effective material properties of internally defective or particle-reinforced composites. An analysis was carried out with a novel method using the two-dimensional special finite element method mixing the concept of equivalent homogeneous materials. A formulation has been developed for a series of special finite elements containing an internal defect or reinforcement in order to assure the high accuracy especially in the vicinity of defects or reinforcements. The adoption of the special finite element can greatly simplify numerical modeling of particle-composites. The numerical result provides the effective material properties of particle-reinforced composite and explains that the size of particles has great influence on the material properties. Numerical examples also demonstrate the validity and versatility of the proposed method by comparing with existing results from literatures.

A reinforcement learning approach for robot control in an unknown environment

In this paper, a control approach based on reinforcement learning is present for a robot to complete a dynamic task in an unknown environment. First, a temporal difference-based reinforcement learning algorithm and its evaluation function are used to make the robot learn with its trials and errors as well as experiences. Second, the simulation are carried out to adjust the parameters of the learning algorithm and determine an optimal policy by using the models of a robot. Last, the effectiveness of the present approach is demonstrated by balancing an inverse pendulum in the unknown environment.

The application of a reinforcement learning agent to a multi-product manufacturing facility

An intelligent agent-based scheduling system, consisting of a reinforcement learning agent and a simulation model has been developed and tested on a classic scheduling problem. The production facility studied is a multiproduct serial line subject to stochastic failure. The agent goal is to minimise total production costs, through selection of job sequence and batch size. To explore state space the agent used reinforcement learning. By applying an independent inventory control policy for each product, the agent successfully identified optimal operating policies for a real production facility.

Construction of aggregation operators with noble reinforcement

This paper examines disjunctive aggregation operators used in various recommender systems. A specific requirement in these systems is the property of noble reinforcement: allowing a collection of high-valued arguments to reinforce each other while avoiding reinforcement of low-valued arguments. We present a new construction of Lipschitz-continuous aggregation operators with noble reinforcement property and its refinements.

Optimising discrete event simulation models using a reinforcement learning agent

A reinforcement learning agent has been developed to determine optimal operating policies in a multi-part serial line. The agent interacts with a discrete event simulation model of a stochastic production facility. This study identifies issues important to the simulation developer who wishes to optimise a complex simulation or develop a robust operating policy. Critical parameters pertinent to 'tuning' an agent quickly and enabling it to rapidly learn the system were investigated.

Characterization of the moisture absorption and thermal ageing behaviour of polymeric composite systems using Raman spectroscopy

Advanced polymeric materials and their respective composites are fast becoming one of the world's most frequently used engineering materials. They find application in the manufacture of e.g. boat hulls, high performance motor vehicles, aircraft components and sports goods. Their high specific strength and specific stiffness give them the edge in applications where weight savings are critical, but their long-term durability is often questioned. These materials are susceptible to environmental conditions such as temperature and humidity. There is also a lack of relevant data, due to the long time-scales required for testing. In this study, the Raman technique has been used to monitor the degradation of two composite systems, namely: a rubber toughened vinylester material used in the marine industry and a high temperature bismaleimide/carbon fibre aerospace composite. Preliminary Raman studies show that the toughening rubber particles dispersed in the cured vinylester resin are leached out during hygrothermal ageing. The weight gain during ageing suggests that this leaching process occurs concurrently with the absorption of water molecules. An increase in the degree of cross-linking is observed when bismaleimide/carbon fibre composite is aged at high temperature. This cross- linking tendency decreases with increasing depth within the carbon fibre bundle.

Smoking and drinking behaviours : the role of Reinforcement Sensitivity Theory

This thesis examines the predictions of Jeffrey Gray's Reinforcement Sensitivity Theory in the development of smoking and hazardous drinking behaviours in young women. Impulsivity was found to significantly predict alcohol use and young women who smoked and drank at hazardous levels were significantly more impulsive than hazardous drinkers.

Reinforcement sensitivity theory : a paradigm for understanding the motivational processes underlying alcohol use

Provides an examination of the processes which motivate individuals to drink alcohol. It was found that heavier drinkers are highly sensitive to reward yet more likely to experience negative affect, both generally, and in response to alcohol-related cues. Based on these findings a model focusing on the links between personality, mood and craving for alcohol was developed.

Extending robust industrial optimisation using simulation and reinforcement learning

Traditional optimisation methods are incapable of capturing the complexity of today's dynamic manufacturing systems. A new methodology, integrating simulation models and intelligent learning agents, was successfully applied to identify solutions to a fundamental scheduling problem. The robustness of this approach was then demonstrated through a series of real-world industrial applications.