Differential graphical games for H∞ control of linear heterogeneous multiagent systems

Differential graphical games have been introduced in the literature to solve state synchronization problem for linear homogeneous agents. When the agents are heterogeneous, the previous notion of graphical games cannot be used anymore and a new definition is required. In this paper, we define a novel concept of differential graphical games for linear heterogeneous agents subject to external unmodeled disturbances, which contain the previously introduced graphical game for homogeneous agents as a special case. Using our new formulation, we can solve both the output regulation and H_∞ output regulation problems. Our graphical game framework yields coupled Hamilton‐Jacobi‐Bellman equations, which are, in general, impossible to solve analytically. Therefore, we propose a new actor‐critic algorithm to solve these coupled equations numerically in real time. Moreover, we find an explicit upper bound for the overall ‐gain of the output synchronization error with respect to disturbance. We demonstrate our developments by a simulation example.     

The influence of conformity and group identity on drink walking intentions: Comparing intentions to drink walk across risky pedestrian crossing scenarios

Despite the dangers associated with drink walking, limited research is currently available regarding the factors which influence individuals to engage in this risky behaviour. This study examined the influence of psychosocial factors upon individuals’ intentions to drink walk across four experimental scenarios (and a control condition). Specifically, a 2 × 2 repeated measures design was utilised in which all of the scenarios incorporated a risky pedestrian crossing situation (i.e., a pedestrian crossing against a red man signal) but differed according to the level of group identity (i.e., low/strangers and high/friends) and conformity (low and high). Individuals were assessed for their intentions to drink walk within each of these different scenarios. Undergraduate students (N = 151), aged 17–30 years, completed a questionnaire. Overall, most of the study's hypotheses were supported with individuals reporting the highest intentions to drink walk when in the presence of friends (i.e., high group identity) and their friends were said to be also crossing against the red man signal (i.e., high conformity). The findings may have significant implications for the design of countermeasures to reduce drink walking. For instance, the current findings would suggest that potentially effective strategies may be to promote resilience to peer influence as well as highlight the negative consequences associated with following the behaviour of other intoxicated pedestrians who are crossing against a red signal.     

A urine patch framework to simulate nitrogen leaching on New Zealand dairy farms

On New Zealand dairy farms, it is the nitrogen excreted directly onto pasture, particularly urine, that drives nitrogen (N) leaching from the farm. A new framework (UPF: Urine Patch Framework) is presented that post-processes the results of a whole farm model and runs a mechanistic soil model to simulate the urine patches. Two alternative methods to simulate the spatial distribution of urine patches were implemented and compared (Grid: spatially explicit, and Probabilistic: based on the probability of different temporal urination patterns). This paper describes the implementation of these two methods in connection with a Whole Farm Model; and compares the N leaching predictions with observed data. Two examples are provided, one analyzing the impact of urine patch overlap and another, the relative risk of N leaching at different times of urinary N deposition. The model showed good correlation and predictive ability between simulated annual N leaching results and observed data [R² = 94 %, mean relative prediction error (MRPE) = 10 % for Grid and R² = 72 %, MRPE = 20 % for Probabilistic]. The two methods produced similar results across an 8-year period for monthly and annual N leaching (R² = 96 %, MRPE = 10 % and R² = 86 %, MRPE = 8 %; respectively). Only 8 % of the paddock area was covered with multiple urinations during 1 year, but as much as 39 % of the total urine volume was deposited on overlapped patches. Systematically removing all urinary N for 1 month in either May or June reduced N leaching by approximately 20 %. Avoiding urinary N deposition during autumn or early winter could be highly effective in mitigating N leached during the following winter.     

Investigation of the mechanisms governing doxorubicin and irinotecan release from drug-eluting beads: mathematical modeling and experimental verification

Drug-eluting beads (DEBs) are embolising devices in clinical use for the treatment of liver cancer by transarterial chemoembolisation. In this study, release kinetics of doxorubicin (DOX) and irinotecan (IRI) were investigated by experimental evaluations and mathematical modeling, based on Langmuir isotherm and two phenomenological models (Boyd/Bhaskar) developed to determine the actual mechanisms controlling drug release rate. The model was validated through release studies, in particular by assessing how drug loading, ionic strength of the release medium and device swelling during release influence drug release kinetics. Results demonstrated that IRI is released much faster than DOX, and that DEB volume strongly depends upon drug loading and fractional release. This effect was properly taken into account in developing the mathematical model. Experimental results were well fit by numerical simulations, and two different rate-controlling mechanisms were found to govern DOX and IRI delivery.     

Using conscious reflection,group processes,and AI to facilitate development of expertise

Three experiments investigated procedures derived from research on knowledge acquisition, group processes, and artificial intelligence for facilitating the development of expertise. In each experimental session, subjects learned to control a simulated sugar production factory. Then they formulated written policies for controlling sugar production either alone as individuals or in small groups. An adaptive AI system provided feedback on policy quality. The research also investigated the use of forced reflective practice in which learners attempted to predict what their policy would do while performing the task. The AI system provided feedback about what their policy would have done in each situation and the outcome based on their policy's response or their suggested alternative response. Results indicated that group interaction and feedback from the AI system improved policy quality. However, only when all three procedures were employed, group interaction, AI feedback, and forced reflective practice, was the development of individual expertise on the task enhanced.