Search

This paper presents the effects of chemical sludge, produced by coagulation processes, on the settleability and dewaterability of biological sludge, produced by the activated sludge processes. It was demonstrated that the settleability of the mixed biological and chemical sludges was significantly better compared with that of the unblended biological sludge. The average settling velocity and density of the discrete particles of the mixed sludges were much higher than those of the biological sludge. The improvement of the mixed sludges’ settleability was influenced by both mixing and ALT ratios of the chemical sludge. It was also determined that the dewaterability of the mixed sludges was improved and the specific resistance was lowered by one order of magnitude compared with the biological sludge.

During the coronavirus disease 2019 (COVID-19) Delta variant wave of the pandemic, governments implemented health orders and policies that included citizens in lockdowns and confined to the home. These health orders and home confinement reduced children’s time for engagement in physical activity and access to supervised, structured physical education (PE) lessons. To understand the potential impact on the physiological aspects of children’s physical health literacy, one must understand the adverse consequences of detraining in relation to the reduction in physical activity and supervised structured PE lessons. The aim of this paper is to give a brief overview of how PE students were affected during the current COVID-19 pandemic. It will also discuss the impact of confinement on physiological adaptions. The article ends with a short discussion on the resumption of face-to-face PE lessons after enforced confinement and PE programming considerations. In such an uncertain environment, PE teachers need to show caution and consider the effects of detraining when programming PE lessons to ensure their safety and strategies for reconditioning. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Deutschland and Bundesinstitut fur Sportwissenschaft, Deutscher Olympischer Sportbund, Deutsche Vereinigung fur Sportwissenschaft.

In this paper, we discuss the conditioning issue in D-S evidence theory in multi-dimensional space. Based on Dempster conditioning, Bayes’ rule and product rule, which are similar to that in probability theory, are presented in this paper. Two kinds of conditional independence called weak conditional independence and strong conditional independence are introduced, which can significantly simplify the inference process when evidence theory is applied to practical application.

Valuation-based systems (VBS) can be considered as a generic uncertainty framework that has many uncertainty calculi, such as probability theory, a version of possibility theory where combination is the product t-norm, Spohn’s epistemic belief theory, and Dempster-Shafer belief function theory, as special cases. In this paper, we focus our attention on conditioning, which is defined using the combination, marginalization, and removal operators of VBS. We show that conditioning can be expressed using the composition operator. We define decomposable compositional models in the VBS framework. Finally, we show that conditioning in decomposable compositional models can be done using local computation. Since all results are obtained in the VBS framework, they hold in all calculi that fit in the VBS framework.

The paper presents a novel modification to the generalised conditioning technique anti-windup compensator presented as a triplet of its possible modifications. The results are presented from application of pole-placement control, with an analysis of closed-loop system behaviour for a class of second-order minimumphase stable plants of oscillatory and aperiodic characteristics with different dead-times.

Learning when and how to generalize knowledge from past experience to novel circumstances is a challenging problem many agents face. In animals, this generalization can be caused by mediated conditioning--when two stimuli gain a relationship through the mediation of a third stimulus. For example, in sensory preconditioning, if a light is always followed by a tone, and that tone is later paired with a shock, the light will come to elicit a fear reaction, even though the light was never directly paired with shock. In this paper, we present a computational model of mediated conditioning based on reinforcement learning with predictive representations. In the model, animals learn to predict future observations through the temporal-difference algorithm. These predictions are generated using both current observations and other predictions. The model was successfully applied to a range of animal learning phenomena, including sensory preconditioning, acquired equivalence, and mediated aversion. We suggest that animals and humans are fruitfully understood as representing their world as a set of chained predictions and propose that generalization in artificial agents may benefit from a similar approach.

A conditioning graph (CG) is a graphical structure that attempt to minimize the implementation overhead of computing probabilities in belief networks. A conditioning graph recursively factorizes the network, but restricting each decomposition to a single node allows us to store the structure with minimal overhead, and compute with a simple algorithm. This paper extends conditioning graphs with optimizations that effectively reduce the height of the CG, thus reducing time complexity exponentially, while increasing the storage requirements by only a constant factor. We conclude that CGs are frequently as efficient as any other exact inference method, with the advantage of being vastly superior to VE and JT in terms of space complexity, and far simpler to implement.

This paper presents a neural conditioning model for on-line learning of behaviors on mobile robots. The model is based on Grossberg's neural model of conditioning as recently implemented by Chang and Gaudiano. It attempts to tackle some of the limitations of the original model by (1) using a temporal difference of the reinforcement to drive learning, (2) adding eligibility trace mechanisms to dissociate behaviour generation from learning, (3) automatically categorizing sensor readings and (4) bootstrapping the learning process through the use of unconditioned responses. Preliminary results of the model that learn simple behaviors on a mobile robot simulator are presented.

A necessity measure N is defined by an implication function. However, specification of an implication function is difficult. Necessity measures are closely related to inclusion relations. In this paper, we propose an approach to necessity measure specification by giving an equivalent parametric inclusion relation between fuzzy sets A and B to N A (B) ≥ h. It is shown that, by such a way, we can specify a necessity measure, i.e., an implication function. Moreover, given an implication function, an associated inclusion relation is discussed.

Almost every electrical activity recorded from the brain has been tested on operant conditioning. Different techniques for detection and transduction, as well as for quantification and feedback, have been used. Basically, studies dealing with the regulation and operant conditioning of brain electrical activity can be divided into three types: (a) conditioning of single neurons, (b) EEG frequency bands; and (c) event-related and slow potentials. The following overview attempts to summarize these studies. The papers presented in Part II and Part III provide an introduction to the research being conducted at present. A brief description of those fields which have received less attention in the more recent research will be presented in this paper.

The hemopoietic toxicity following accidental total body radiation can be circumvented by the injection of normal bone marrow (BM) cells after irradiation. The “hat-trick” of the protective effect of the injected BM cells appears to be the delivery of a new donor-type hemopoietic system [14, 25, 36]. After this was discovered, research efforts were reversed and unirradiated animals were deliberately irradiated to prepare (“condition”) them to receive a new hemopoietic system. Radiomimetic drugs were later introduced as conditioning agents for BM transplantation [17, 18, 27]. Increasing numbers of patients with hemopoietic disorders such as acute leukemia, severe immunodeficiency (SCID) or aplastic anemia (AA) appear to benefit from BM transplantation [2, 3, 34]. Optimal conditioning regimens for human BM transplantation remain to be defined. In this paper the information on total body irradiation (TBI) as a conditioning agent in experimental animals will be reviewed. On this data base new suggestions for the use of TBI in human BM transplantation will be made.

Axonal regeneration following axotomy may be accelerated in neurons which have undergone a previous axonal injury. When axonal regeneration following an axotomy (the testing lesion) is altered as a result of the axon having undergone a previous injury (the conditioning lesion), we refer to this change as a conditioning lesion effect. The effect of a conditioning lesion is usually (but not always) an acceleration or enhancement of axonal outgrowth. It represents an interesting experimental manipulation which may shed light on the factors which initiate and control axonal regeneration. In this paper, we shall review current knowledge about conditioning lesion effects, present some new data, and discuss possible mechanisms and practical applications of conditioning lesion effects.