Dopamine Transients in the Ventral Tegmental Area Attenuate Aversive Prediction Error

Prediction-error, the discrepancy between real and expected outcomes, drives associative learning. It is best exemplified in the blocking paradigm. In blocking, impairment in learning about the predictive relation between a cue (e.g., a clicker) and an outcome (e.g. footshock) is observed when this learning takes place in the presence of a good predictor (e.g. a light) for the same outcome. Small prediction-error generated by the light leads to impairment in learning about the clicker-footshock relationship. The mere presentation of the two stimuli in compound in the absence of pre-training of one of those stimuli does not yield blocking. That is, in the so-called overshadowing control condition, the clicker is presented in compound with the light that was not previously associated with the footshock. This arrangement leads to robust learning about the clicker due to the presence of a maximum prediction-error. Dopamine (DA) in the ventral tegmental area (VTA) has been implicated in reward prediction-error (RPE). Evidence suggests an opposing role of DA in fear and reward. Here we undertook several experiments aimed at elucidating the role of VTA DA neurons in aversive prediction-error (APE). We used a powerful behavioural and theory-driven approach by combining blocking and the corresponding overshadowing control in the context of aversive (fear) learning along with optogenetics. We used the Th-cre+/- rats in order to exercise fine temporal control over VTA DA neurons during aversive learning. Taken together, our results provide evidence that optical stimulation of VTA DA neurons and their terminals in the nucleus accumbens (NAc) at the time of expected shock augmented the blocking effect by attenuating APE and further impaired learning about the blocked cue. We did not observe such an effect in the overshadowing control nor many neural control groups

A real-time demand response pricing model for the smart grid

Submitted to the University of Bedfordshire, in partial fulfilment of the requirements for the degree of Doctor of Philosophy (PhD)This thesis contributes to a novel model for Real-Time Price Suggestions (RTPS) of the Smart Grid (SG), which is the next generation modern bi-directional grid, particularly with respect to the pricing model. The research employs an experiment-based methodology which includes the use of a simulation technique. The research developed a Demand Response (DR) pricing model. Energy users are keen to reduce their bills, and Energy Providers (EP) is also keen on reducing their industrial costs. The DR model would benefit them both. The model has been tested with the UK-based traditional price value using real-time usage data. Energy users significantly reduced their bill and EP reduced their industrial cost due to load shifting. The Price Control Unit (PCU) and Price Suggestion Unit (PSU) utilise a set of embedded algorithms to vary price based upon demand. This model makes suggestions based on an energy threshold and makes use of Simultaneous Perturbation Stochastic Approximation Methods to produce prices. The results show that bill and peak load reductions benefit both the energy provider and users. The tests on a daily basis and monthly basis both benefit energy users and energy provider. The model has been validated by building a hardware prototype. This model also addresses users’ preferences; if users are non-responsive, they can still reduce their bills. The model contributes significantly to the existing models, and the novel contribution is the PSU which uniquely benefits energy users and provider. Therefore, there is a number of fundamental aspect of contributions to the model RTPS constitutes the final thesis of the PhD. The Real-Time Pricing is a better pricing system, algorithm developed on a daily basis and monthly basis and finally building a hardware prototype

Numerical evaluation of yielding shear panel device: A sustainable technique to minimise structural damages due to earthquakes

Earthquake is one of the most catastrophic natural events that affect human civilization causing loss of human lives and destruction of cities; recent earthquakes in the Asia-Pacific region highlight the importance of further research to develop sustainable techniques to limit structural damages due to this catastrophe. As the occurrence and severity of an earthquake are beyond our control, it is the after effects that we have to focus on to minimise the loss of human lives. Structures must be designed to absorb the enormous amount of energy exerted by earthquakes to sustain this sudden natural impact. The current research investigates the performance of a recently developed Yielding Shear Panel Device (YSPD), which will be designed to absorb earthquake energy by exploiting the significantly high ductility of stainless steel. YSPD is a small, inexpensive and easy to install device. The basic notion is to concentrate the inevitable structural damages to YSPDs and hence keeping the main structural components intact. The simplicity of YSPD would allow the damaged devices to be replaced by the new ones without any major structural reconstruction

Pull-out resistance of slef-tapping screws in cross-laminated timber made from radiata pine

Cross-laminated timber (CLT) is now considered a viable alternative to traditional construction materials both in mid-rise and high-rise structures. The structural response of CLT heavily relies on the type of timber used in manufacturing, and this can vary significantly based on the original source for this naturally grown raw material. Spruce has been widely used in Europe for CLT production, but in Australia, locally available radiata pine is used by XLam for the manufacturing of their CLT panels. Self-tapping screws (STS) are typically recommended by CLT manufacturers and are most commonly used in relevant construction due to their high load carrying capacities and easy installation process. VGS STSs produced by Rothoblaas were used to investigate their composite actions when pulled-out from three-layer XLam CLT panels with thicknesses of 105 mm and 135 mm. VGS screws with 11 mm in diameter were inserted both parallel-to-grain and perpendicular-to-grain on the narrow face of the CLT panels as part of the current study. Typical failure modes as well as critical penetration depths were carefully recorded. Obtained results showed significant increase of pull-out capacity as penetration depths were increased for considered cases. However, experimental results also showed some obvious inconsistencies. These observations clearly demonstrate the challenges associated with working naturally grown fibrous materials and highlights the importance of major research on this field

The serial blocking effect: a testbed for the neural mechanisms of temporal-difference learning

Temporal-difference (TD) learning models afford the neuroscientist a theory-driven roadmap in the quest for the neural mechanisms of reinforcement learning. The application of these models to understanding the role of phasic midbrain dopaminergic responses in reward prediction learning constitutes one of the greatest success stories in behavioural and cognitive neuroscience. Critically, the classic learning paradigms associated with TD are poorly suited to cast light on its neural implementation, thus hampering progress. Here, we present a serial blocking paradigm in rodents that overcomes these limitations and allows for the simultaneous investigation of two cardinal TD tenets; namely, that learning depends on the computation of a prediction error, and that reinforcing value, whether intrinsic or acquired, propagates back to the onset of the earliest reliable predictor. The implications of this paradigm for the neural exploration of TD mechanisms are highlighted

Arsenic detoxification by phytoremediation

Heavy metals pollution is amongst the commonest form of environmental pollution. These metals have accumulated over time from the smelting and mining activities of man, from poor waste disposal practices and from modernization. Recently the impact of heavy metal pollution of the environment is stirring up serious concerns since the discovery that some edible plants accumulate these metals to a level, toxic to both themselves and to the animals that consumes them. Common features of heavily polluted soil include barrenness, desertification, erosion, and this usually result in developmental stagnation in areas with such pollution. More researches have recently been stepped up in the field of remediating soils polluted with heavy metals. Traditional method includes excavation of the top soil, capping of the soil, stabilization of the polluting heavy metals, soil washing. In recent time, emphases have been drawn to the use of plants that has high metal accumulating and tolerating capacity to remediate metal-contaminated soil. This mini-review highlights the different conventional and recent practices in the control of heavy metal pollution

Identification of Radar Signals Based on Time-Frequency Agility using Short-Time Fourier Transform

With modern advances in radar technologies and increased complexity in aerial battle, there is need for knowledge acquisition on the abilities and operating characteristics of intercepted hostile systems. The required knowledge obtained through advanced signal processing is necessary for either real time-warning or in order to determine Electronic Order of Battle (EOB) of these systems. An algorithm was therefore developed in this paper based on a joint Time-Frequency Distribution (TFD) in order to identify the time-frequency agility of radar signals based on its changing pulse characteristics. The joint TFD used in this paper was the square magnitude of the Short-Time Fourier Transform (STFT), where power and frequency obtained at instants of time from its Time-Frequency Representation (TFR) was used to estimate the time and frequency parameters of the radar signals respectively. Identification was thereafter done through classification of the signals using a rule-based classifier formed from the estimated time and frequency parameters. The signals considered in this paper were the simple pulsed, pulse repetition interval modulated, frequency hopping and the agile pulsed radar signals, which represent cases of various forms of agility associated with modern radar technologies. Classification accuracy was verified using the Monte Carlo simulation performed at various ranges of Signal-to-Noise Ratios (SNRs) in the presence of noise modelled by the Additive White Gaussian Noise (AWGN). Results obtained showed identification accuracy of 99% irrespective of the signal at a minimum SNR of 0dB where signal and noise power were the same. The obtained minimum SNR at this classification accuracy showed that the developed algorithm can be deployed practically in the electronic warfare field for accurate agility classification of airborne radar signals