A Long Slit-Like Entrance Promotes Ventilation in the Mud Nesting Social Wasp, Polybia spinifex: Visualization of Nest Microclimates using Computational Fluid Dynamics

Polybia spinifex Richards (Hymenoptera: Vespidae) constructs mud nests characterized by a long slit-like entrance. The ventilation and thermal characteristics of the P. spinifex nest were investigated to determine whether the nest microclimate is automatically maintained due to the size of the entrance. In order to examine this hypothesis, a numerical simulation was employed to predict the effects of the entrance length. The calculations were performed with 3D-virtual models that simulated the P. spinifex nest conditions, and the reliability of the simulations was experimentally examined by using gypsum-model nests and a P. spinifex nest. The ventilation effect was determined by blowing air through the nest at 1–3 m/s (airflow conditions); the airspeed was found to be higher in models with a longer entrance. The ventilation rate was also higher in models with longer entrances, suggesting that the P. spinifex nest is automatically ventilated by natural winds. Next, the thermal effect was calculated under condition of direct sunlight. Under a calm condition (airflow, 0 m/s), thermal convection and a small temperature drop were observed in the case of models with a long entrance, whereas the ventilation and thermoregulation effects seemed small. Under airflow conditions, the temperature at the mid combs steeply dropped due to the convective airflow through the entrance at 1–2 m/s, and at 3 m/s, most of the heat was eliminated due to high thermal conductivity of the mud envelope, rather than convection

Using Bayesian Networks and Machine Learning to Predict Computer Science Success

Bayesian Networks and Machine Learning techniques were evaluated and compared for predicting academic performance of Computer Science students at the University of Cape Town. Bayesian Networks performed similarly to other classification models. The causal links AQ1 inherent in Bayesian Networks allow for understanding of the contributing factors for academic success in this field. The most effective indicators of success in first-year ‘core’ courses in Computer Science included the student’s scores for Mathematics and Physics as well as their aptitude for learning and their work ethos. It was found that unsuccessful students could be identified with ≈91% accuracy. This could help to increase throughput as well as student wellbeing at university

Modelling with non-stratified chain event graphs

© 2019, Springer Nature Switzerland AG. Chain Event Graphs (CEGs) are recent probabilistic graphical modelling tools that have proved successful in modelling scenarios with context-specific independencies. Although the theory underlying CEGs supports appropriate representation of structural zeroes, the literature so far does not provide an adaptation of the vanilla CEG methods for a real-world application presenting structural zeroes also known as the non-stratified CEG class. To illustrate these methods, we present a non-stratified CEG representing a public health intervention designed to reduce the risk and rate of falling in the elderly. We then compare the CEG model to the more conventional Bayesian Network model when applied to this setting

Dynamic Bayesian network for semantic place classification in mobile robotics

In this paper, the problem of semantic place categorization in mobile robotics is addressed by considering a time-based probabilistic approach called dynamic Bayesian mixture model (DBMM), which is an improved variation of the dynamic Bayesian network. More specifically, multi-class semantic classification is performed by a DBMM composed of a mixture of heterogeneous base classifiers, using geometrical features computed from 2D laserscanner data, where the sensor is mounted on-board a moving robot operating indoors. Besides its capability to combine different probabilistic classifiers, the DBMM approach also incorporates time-based (dynamic) inferences in the form of previous class-conditional probabilities and priors. Extensive experiments were carried out on publicly available benchmark datasets, highlighting the influence of the number of time-slices and the effect of additive smoothing on the classification performance of the proposed approach. Reported results, under different scenarios and conditions, show the effectiveness and competitive performance of the DBMM

A Taxonomy of Explainable Bayesian Networks

Artificial Intelligence (AI), and in particular, the explainability thereof, has gained phenomenal attention over the last few years. Whilst we usually do not question the decision-making process of these systems in situations where only the outcome is of interest, we do however pay close attention when these systems are applied in areas where the decisions directly influence the lives of humans. It is especially noisy and uncertain observations close to the decision boundary which results in predictions which cannot necessarily be explained that may foster mistrust among end-users. This drew attention to AI methods for which the outcomes can be explained. Bayesian networks are probabilistic graphical models that can be used as a tool to manage uncertainty. The probabilistic framework of a Bayesian network allows for explainability in the model, reasoning and evidence. The use of these methods is mostly ad hoc and not as well organised as explainability methods in the wider AI research field. As such, we introduce a taxonomy of explainability in Bayesian networks. We extend the existing categorisation of explainability in the model, reasoning or evidence to include explanation of decisions. The explanations obtained from the explainability methods are illustrated by means of a simple medical diagnostic scenario. The taxonomy introduced in this paper has the potential not only to encourage end-users to efficiently communicate outcomes obtained, but also support their understanding of how and, more importantly, why certain predictions were made

“Hot Hand” on Strike: Bowling Data Indicates Correlation to Recent Past Results, Not Causality

Recently, the “hot hand” phenomenon regained interest due to the availability and accessibility of large scale data sets from the world of sports. In support of common wisdom and in contrast to the original conclusions of the seminal paper about this phenomenon by Gilovich, Vallone and Tversky in 1985, solid evidences were supplied in favor of the existence of this phenomenon in different kinds of data. This came after almost three decades of ongoing debates whether the “hot hand” phenomenon in sport is real or just a mis-perception of human subjects of completely random patterns present in reality. However, although this phenomenon was shown to exist in different sports data including basketball free throws and bowling strike rates, a somehow deeper question remained unanswered: are these non random patterns results of causal, short term, feedback mechanisms or simply time fluctuations of athletes performance. In this paper, we analyze large amounts of data from the Professional Bowling Association(PBA). We studied the results of the top 100 players in terms of the number of available records (summed into more than 450,000 frames). By using permutation approach and dividing the analysis into different aggregation levels we were able to supply evidence for the existence of the “hot hand” phenomenon in the data, in agreement with previous studies. Moreover, by using this approach, we were able to demonstrate that there are, indeed, significant fluctuations from game to game for the same player but there is no clustering of successes (strikes) and failures (non strikes) within each game. Thus we were lead to the conclusion that bowling results show correlation to recent past results but they are not influenced by them in a causal manner

Fundamental role of C1q in autoimmunity and inflammation

C1q, historically viewed as the initiating component of the classical complement pathway, also exhibits a variety of complement-independent activities in both innate and acquired immunity. Recent studies focusing on C1q\u27s suppressive role in the immune system have provided new insight into how abnormal C1q expression and bioactivity may contribute to autoimmunity. In particular, molecular networks involving C1q interactions with cell surface receptors and other ligands are emerging as mechanisms involved in C1q\u27s modulation of immunity. Here, we discuss the role of C1q in controlling immune cell function, including recently elucidated mechanisms of action, and suggest how these processes are critical for maintaining tissue homeostasis under steady-state conditions and in preventing autoimmunity