Analysis of the current distribution in an aluminum electrolysis cell with copper collector-bar cathode assembly

Understanding the magneto-hydrodynamic forces generated due to the external magnetic field and current density distribution within the cell (current in cell linings) is important in the optimization of cell dynamics. It is well documented that these factors play a crucial role in establishing the metal-pad stability of the cell. Conventional cells use the cathode-collector-bar assembly to carry the current through molten aluminium, the cathode and the steel collector-bar to nearest external bus. The electrical conductivity of the steel is so poor relative to the molten aluminium that the outer third of the collector bar carries the maximum load, which in turn increases the horizontal components of the current within the cell. Previous studies have modelled improvement in the cell instability through external magnetic compensation by redistributing current in the cathode busbar. Very little to date has been published on work to improve the current distribution within the cell. In this work, the current distribution in an aluminium electrolysis cell with copper collector-bar was predicted using finite element modelling. A 2D cross-section of a commercial cell was used under steady conditions of electrical fields in anode, electrolyte, molten aluminium and copper cathode-assembly. Different shapes and sizes of the cathode assembly are also considered to optimise the distribution of current throughout the cathode lining. The findings indicated that the copper-bar of similar size to steel could save voltage up to 150 mV. There is a reduction of more than 70% in peak current density value due to the copper inserts. The predicted trends of current distribution show a good agreement with previously published data.

Gymnasium-based unsupervised exercise maintains benefits in oxygen uptake kinetics obtained following supervised training in type 2 diabetes

Supervised exercise (SE) in patients with type 2 diabetes improves oxygen uptake kinetics at the onset of exercise. Maintenance of these improvements, however, has not been examined when supervision is removed. We explored if potential improvements in oxygen uptake kinetics following a 12-week SE that combined aerobic and resistance training were maintained after a subsequent 12-week unsupervised exercise (UE). The involvement of cardiac output (CO) in these improvements was also tested. Nineteen volunteers with type 2 diabetes were recruited. Oxygen uptake kinetics and CO (inert gas rebreathing) responses to constant-load cycling at 50% ventilatory threshold (V_T), 80% V_T, and mid-point between V_T and peak workload (50% Δ) were examined at baseline (on 2 occasions) and following each 12-week training period. Participants decided to exercise at a local gymnasium during the UE. Thirteen subjects completed all the interventions. The time constant of phase 2 of oxygen uptake was significantly faster (p < 0.05) post-SE and post-UE compared with baseline at 50% V_T (17.3 ± 10.7 s and 17.5 ± 5.9 s vs. 29.9 ± 10.7 s), 80% V_T (18.9 ± 4.7 and 20.9 ± 8.4 vs. 34.3 ± 12.7s), and 50% Δ (20.4 ± 8.2 s and 20.2 ± 6.0 s vs. 27.6 ± 3.7 s). SE also induced faster heart rate kinetics at all 3 intensities and a larger increase in CO at 30 s in relation to 240 s at 80% V_T; and these responses were maintained post-UE. Unsupervised exercise maintained benefits in oxygen uptake kinetics obtained during a supervised exercise in subjects with diabetes, and these benefits were associated with a faster dynamic response of heart rate after training.

Interval type-2 fuzzy logic systems for load forecasting : a comparative study

Accurate short term load forecasting (STLF) is essential for a variety of decision-making processes. However, forecasting accuracy can drop due to the presence of uncertainty in the operation of energy systems or unexpected behavior of exogenous variables. This paper proposes the application of Interval Type-2 Fuzzy Logic Systems (IT2 FLSs) for the problem of STLF. IT2 FLSs, with additional degrees of freedom, are an excellent tool for handling uncertainties and improving the prediction accuracy. Experiments conducted with real datasets show that IT2 FLS models precisely approximate future load demands with an acceptable accuracy. Furthermore, they demonstrate an encouraging degree of accuracy superior to feedforward neural networks and traditional type-1 Takagi-Sugeno-Kang (TSK) FLSs.

Differential corticosteroid receptor regulation of mesoaccumbens dopamine efflux during the peak and nadir of the circadian rhythm : a molecular equilibrium in the midbrain?

Corticosteroid receptor modulation of mesoaccumbens dopamine neurotransmission is believed to be a key neurobiological mechanism mediating the effects of stress in addiction. Importantly, nucleus accumbens (NAc) subregions (core and shell) are reported to respond differentially to fluctuating basal levels of glucocorticoids, with dopaminergic responses in the core of the NAc being somewhat impervious to fluctuating levels of glucocorticoids relative to the shell. To investigate the corticosteroid receptor mechanisms mediating basal dopamine efflux in the core of the NAc, we have used chronoamperometry in combination with stearate-modified graphite paste electrodes in urethane anesthetized male Long–Evans rats during the peak and nadir of the circadian cycle. Blockade of ventral tegmental area low-affinity glucocorticoid (GR) or high-affinity mineralocorticoid (MR) receptors with mifepristone (1 μg/μl) or spironolactone (0.2 μg/μl), respectively, indicated that endogenous phase-dependent corticosteroid receptor activation (GRs during peak; MRs during nadir) facilitated extracellular NAc dopamine efflux. Conversely, the alternate receptor's actions appeared inhibitory at these time points (MRs during peak; GRs during nadir). Pharmacological activation of either the GR or MR with corticosterone (2 μg/μl) or aldosterone (0.2 μg/μl), respectively, potentiated NAc dopamine efflux, irrespective of circadian phase. Together, these data suggest that dominant corticosteroid receptor activation stimulates tonic mesoaccumbens dopamine transmission, enabling MRs and GRs to differentially maintain basal NAc dopamine release over the course of the circadian cycle. This points to an important molecular mechanism through which relatively stable NAc core dopamine extracellular levels could be maintained in the face of fluctuating corticosterone circadian rhythms.

Peak picking and the assessment of separation performance in two-dimensional high performance liquid chromatography

An algorithm was developed for 2DHPLC that automated the process of peak recognition, measuring their retention times, and then subsequently plotting the information in a two-dimensional retention plane. Following the recognition of peaks, the software then performed a series of statistical assessments of the separation performance, measuring for example, correlation between dimensions, peak capacity and the percentage of usage of the separation space. Peak recognition was achieved by interpreting the first and second derivatives of each respective one-dimensional chromatogram to determine the 1D retention times of each solute and then compiling these retention times for each respective fraction ‘cut’. Due to the nature of comprehensive 2DHPLC adjacent cut fractions may contain peaks common to more than one cut fraction. The algorithm determined which components were common in adjacent cuts and subsequently calculated the peak maximum profile by interpolating the space between adjacent peaks. This algorithm was applied to the analysis of a two-dimensional separation of an apple flesh extract separated in a first dimension comprising a cyano stationary phase and an aqueous/THF mobile phase as the first dimension and a second dimension comprising C18-Hydro with an aqueous/MeOH mobile phase. A total of 187 peaks were detected.

Short term load forecasting using a hybrid neural network

Short Term Load Forecasting (STLF) is very important from the power systems grid operation point of view. STLF involves forecasting load demand in a short term time frame. The short term time frame may consist of half hourly prediction up to weekly prediction. Accurate forecasting would benefit the utility in terms of reliability and stability of the grid ensuring adequate supply is present to meet with the load demand. Apart from that it would also affect the financial performance of the utility company. An accurate forecast would result in better savings while maintaining the security of the grid. This paper outlines the STLF using a novel hybrid online learning neural network, known as the Gaussian Regression (GR). This new hybrid neural network is a combination of two existing online learning neural networks which are the Gaussian Adaptive Resonance Theory (GA) and the Generalized Regression Neural Network (GRNN). Both GA and GRNN implemented online learning, but each of them suffers from limitation. Originally GA is used for unsupervised clustering by compressing the training samples into several categories. A supervised version of GA is available, namely Gaussian ARTMAP (GAM). However, the GAM is still not capable on solving regression problem. On the other hand, GRNN is designed for solving real value estimation (regression) problem, but the learning process would involve of memorizing all training samples, hence high computational cost. The hybrid GR is considered an enhanced version of GRNN with compression ability while still maintains online learning properties. Simulation results show that GR has comparable prediction accuracy and has less prototype as compared to the original GRNN as well as the Support Vector Regression.

Quantification of physical load in elite junior Australian football players

Results from this thesis provide insights into the physical loads experienced by the elite junior Australian footballer. The information presented can assist in the facilitation of best practice advice for player management and training prescription through the use of training diaries and GPS TMA and HR device technologies.

Improving load forecasting accuracy through combination of best forecasts

Neural network (NN) models have been widely used in the literature for short-term load forecasting. Their popularity is mainly due to their excellent learning and approximation capability. However, their forecasting performance significantly depends on several factors including initializing parameters, training algorithm, and NN structure. To minimize negative effects of these factors, this paper proposes a practically simple, yet effective and an efficient method to combine forecasts generated by NN models. The proposed method includes three main phases: (i) training NNs with different structures, (ii) selecting best NN models based on their forecasting performance for a validation set, and (iii) combination of forecasts for selected best NNs. Forecast combination is performed through calculating the mean of forecasts generated by best NN models. The performance of the proposed method is examined using real world data set. Comparative studies demonstrate that the accuracy of combined forecasts is significantly superior to those obtained from individual NN models.

Quantification of tackling demands in professional Australian football using integrated wearable athlete tracking technology

Objectives
To describe and quantify the frequency, velocity and acceleration at impact during tackling in Australian football using a combination of video and athlete tracking technology.

Design
Quasi-experimental.

Methods
Data was collected from twenty professional Australian Football League players during four in-season matches. All tackles made by the player and those against the player were video-coded and time stamped at the point of contact and then subjectively categorised into low, medium and high intensity impact groups. Peak GPS and acceleration data were identified at the point of contact. Two-way analysis of variance was used to assess differences (p < 0.05) between tackle type (made and against) and tackle intensity.

Results
A total of 173 tackles made and 179 tackles against were recorded. Significant differences were found between all tackle intensity groups. Peak velocity was significantly greater in high (19.5 ± 6.1 km h−1) compared to medium (13.4 ± 5.8 km h−1) and low intensity (11.3 ± 5.0 km h−1) tackles. Peak Player Load™, a modified vector magnitude of tri-axial acceleration, was significantly greater in high (7.5 ± 1.7 a.u.) compared to medium (4.9 ± 1.5 a.u.) and low intensity (4.0 ± 1.3 a.u.) tackles.

Conclusions
High intensity tackles, although less frequent, are significantly greater in speed of movement immediately prior to contact and in the resultant impact acceleration compared to tackles of lower intensity. Differences in accelerometer data between tackles observed to be progressively greater in intensity suggest a level of ecological validity and provide preliminary support for the use of accelerometers to assess impact forces in contact invasion sports.

An approach for wind power integration using demand side resources

Renewable energy resources, especially wind power, are expected to provide a considerable portion of the world energy requirements in the near future. Large-scale wind power penetration impacts the electricity industry in many aspects and raises a number of technical challenges for the electricity network. A day-ahead network-constrained market clearing formulation is proposed which considers demand side resources. The proposed approach can provide flexible load profile and reduce the need for ramp up/down services by the conventional generators. This method can potentially facilitate a large penetration of wind power by shifting the wind power generation from the off-peak periods to the high-peak hours. The validity of the proposed approach has been verified using the IEEE 30 bus and 57 bus test systems.