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.

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.

Monitoring the corrosion of concrete reinforcement using control curves

Corrosion testing (half-cell and LPR) was carried out on a number reinforced concrete panels which had been taken from the fascia of a twenty five year old high rise building in Melbourne, Australia. Corrosion, predominantly as a result of carbonation of the concrete, was associated with a limited amount of cracking. A monitoring technique was established in which probe electrodes (reference and counter) were retro-fitted into the concrete. The probe electrode setup was identical for all panels tested. It was found that the corrosion behaviour of all panels tested closely fitted a family of results when the corrosion potential is plotted against the polarisation resistance (Rp). This enabled the development of a so-called 'control curve' relating the corrosion potential to the Rp for all of the panels under investigation. This relationship was also confirmed on laboratory samples, indicating that for a fixed geometry and experimental conditions a relationship between the potential and polarisation resistance of steel can be established for the steel-concrete system. Experimental results will be presented which indicate that for a given monitoring cell geometry, it may be possible to propose criteria for the point at which remediation measures should be considered. The establishment of such a control curve has enabled the development of a powerful monitoring tool for the assessment of a number of proposed corrosion remediation techniques. The actual effect of any corrosion remediation technique becomes clearly apparent via the type and magnitude of deviation of post remediation data from the original (preremediation) control curve.