Interventional Effect of Nanosilver Paint on Fungal Load of Indoor Air in a Hospital Ward

Hospital ward environments contain various types of microorganisms, in which fungal agents are one of the main contaminants that may cause hospital-acquired infections. Regarding this, the aim of the present study was to evaluate the effect of nanosilver paint on reducing fungal contaminants of indoor air in an educational, research, and treatment center. Two rooms in the hematology ward were selected. One room was painted using usual paint (control room) and the other room was painted with paint containing nanosilver particles (experimental room). One hundred and twelve samples were collected using active (Anderson BioSampler) and passive (settle plate or open plate) air sampling techniques. The samples were incubated for 3–7 days at 35°C, and the positive fungal cultures were examined according to morphological and microscopic characteristics. Following active sampling, the mean and standard deviation of the number of colony-forming units (CFU/m3) of fungi colonies in the experimental and control rooms were 29.21 ± 17.99 and 22.50 ± 10.02 before intervention and 13.79 ± 6.20 and 31.07 ± 21.1 after intervention, respectively. Following passive sampling, the number of CFU/plate in the experimental and control rooms was 6 and 0 before and 1and 1 after intervention, respectively. The use of the nanosilver paint was effective in reducing air fungal contamination. Moreover, the active sampling method was more sensitive to measuring the concentration changes for fungal bioaerosols.

Active Learning Methods for Dynamic Job Shop Scheduling using Genetic Programming under Uncertain Environment

<p>Scheduling is an important problem in artificial intelligence and operations research. In production processes, it deals with the problem of allocation of resources to different tasks with the goal of optimizing one or more objectives. Job shop scheduling is a classic and very common scheduling problem. In the real world, shop environments dynamically change due to events such as the arrival of new jobs and machine breakdown. In such manufacturing environments, uncertainty in shop parameters is typical. It is of vital importance to develop methods for effective scheduling in such practical settings. Scheduling using heuristics like dispatching rules is very popular and suitable for such environments due to their low computational cost and ease of implementation. For a dynamic manufacturing environment with varying shop scenarios, using a universal dispatching rule is not very effective. But manual development of effective dispatching rules is difficult, time consuming and requires expertise. Genetic programming is an evolutionary approach which is suitable for automatically designing effective dispatching rules. Since the genetic programming approach searches in the space of heuristics (dispatching rules) instead of building up a schedule, it is considered a hyper-heuristic approach. Genetic programming like many other evolutionary approaches is computationally expensive. Therefore, it is of vital importance to present the genetic programming based hyper-heuristic (GPHH) system with scheduling problem instances which capture the complex shop scenarios capturing the difficulty in scheduling. Active learning is a related concept from machine learning which concerns with effective sampling of those training instances to promote the accuracy of the learned model. The overall goal of this thesis is to develop effective and efficient genetic programming based hyper-heuristic approaches using active learning techniques for dynamic job shop scheduling problems with one or more objectives. This thesis develops new representations for genetic programming enabling it to incorporate the uncertainty information about processing times of the jobs. Furthermore, a cooperative co-evolutionary approach is developed for GPHH which evolves a pair of dispatching rules for bottleneck and non-bottleneck machines in the dynamic environment with uncertainty in processing times arising due to varying machine characteristics. The results show that the new representations and training approaches are able to significantly improve the performance of evolved dispatching rules. This thesis develops a new GPHH framework in order to incorporate active learning methods toward sampling DJSS instances which promote the evolution of more effective rules. Using this framework, two new active sampling methods were developed to identify those scheduling problem instances which promoted evolution of effective dispatching rules. The results show the advantages of using active learning methods for scheduling under the purview of GPHH. This thesis investigates a coarse-grained model of parallel evolutionary approach for multi-objective dynamic job shop scheduling problems using GPHH. The outcome of the investigation was utilized to extend the coarse-grained model and incorporate an active sampling heuristic toward identifying those scheduling problem instances which capture the conflict between the objectives. The results show significant improvement in the quality of the evolved Pareto set of dispatching rules. Through this thesis, the following contributions have been made. (1) New representations and training approaches for GPHH to incorporate uncertainty information about processing times of jobs into dispatching rules to make them more effective in a practical shop environment. (2) A new GPHH framework which enables active sampling of scheduling problem instances toward evolving dispatching rules effective across complex shop scenarios. (3) A new active sampling heuristic based on a coarse-grained model of parallel evolutionary approach for GPHH for multi-objective scheduling problems.</p>

Active Learning Methods for Dynamic Job Shop Scheduling using Genetic Programming under Uncertain Environment

<p>Scheduling is an important problem in artificial intelligence and operations research. In production processes, it deals with the problem of allocation of resources to different tasks with the goal of optimizing one or more objectives. Job shop scheduling is a classic and very common scheduling problem. In the real world, shop environments dynamically change due to events such as the arrival of new jobs and machine breakdown. In such manufacturing environments, uncertainty in shop parameters is typical. It is of vital importance to develop methods for effective scheduling in such practical settings. Scheduling using heuristics like dispatching rules is very popular and suitable for such environments due to their low computational cost and ease of implementation. For a dynamic manufacturing environment with varying shop scenarios, using a universal dispatching rule is not very effective. But manual development of effective dispatching rules is difficult, time consuming and requires expertise. Genetic programming is an evolutionary approach which is suitable for automatically designing effective dispatching rules. Since the genetic programming approach searches in the space of heuristics (dispatching rules) instead of building up a schedule, it is considered a hyper-heuristic approach. Genetic programming like many other evolutionary approaches is computationally expensive. Therefore, it is of vital importance to present the genetic programming based hyper-heuristic (GPHH) system with scheduling problem instances which capture the complex shop scenarios capturing the difficulty in scheduling. Active learning is a related concept from machine learning which concerns with effective sampling of those training instances to promote the accuracy of the learned model. The overall goal of this thesis is to develop effective and efficient genetic programming based hyper-heuristic approaches using active learning techniques for dynamic job shop scheduling problems with one or more objectives. This thesis develops new representations for genetic programming enabling it to incorporate the uncertainty information about processing times of the jobs. Furthermore, a cooperative co-evolutionary approach is developed for GPHH which evolves a pair of dispatching rules for bottleneck and non-bottleneck machines in the dynamic environment with uncertainty in processing times arising due to varying machine characteristics. The results show that the new representations and training approaches are able to significantly improve the performance of evolved dispatching rules. This thesis develops a new GPHH framework in order to incorporate active learning methods toward sampling DJSS instances which promote the evolution of more effective rules. Using this framework, two new active sampling methods were developed to identify those scheduling problem instances which promoted evolution of effective dispatching rules. The results show the advantages of using active learning methods for scheduling under the purview of GPHH. This thesis investigates a coarse-grained model of parallel evolutionary approach for multi-objective dynamic job shop scheduling problems using GPHH. The outcome of the investigation was utilized to extend the coarse-grained model and incorporate an active sampling heuristic toward identifying those scheduling problem instances which capture the conflict between the objectives. The results show significant improvement in the quality of the evolved Pareto set of dispatching rules. Through this thesis, the following contributions have been made. (1) New representations and training approaches for GPHH to incorporate uncertainty information about processing times of jobs into dispatching rules to make them more effective in a practical shop environment. (2) A new GPHH framework which enables active sampling of scheduling problem instances toward evolving dispatching rules effective across complex shop scenarios. (3) A new active sampling heuristic based on a coarse-grained model of parallel evolutionary approach for GPHH for multi-objective scheduling problems.</p>

The dynamics of decision-making and action during active sampling

Embodied Cognition Theories (ECTs) of decision-making propose that the decision process pervades the execution of choice actions and manifests itself in these actions. Decision-making scenarios where actions not only express the choice but also help sample information can provide a valuable, ecologically relevant model for this framework. We present a study to address this paradigmatic situation in humans. Subjects categorized (2AFC task) a central object image, blurred to different extents, by moving a cursor toward the left or right of the display. Upward cursor movements reduced the image blur and could be used to sample information. Thus, actions for decision and actions for sampling were orthogonal to each other. We analyzed response trajectories to test whether information-sampling movements co-occurred with the ongoing decision process. Trajectories were bimodally distributed, with one kind being direct towards one response option (non-sampling), and the other kind containing an initial upward component before veering off towards an option (sampling). This implies that there was an initial decision at the early stage of a trial, whether to sample information or not. Importantly, in sampling trials trajectories were not purely upward, but rather had a significant horizontal deviation early on. This result suggests that movements to sample information exhibit an online interaction with the decision process, therefore supporting the prediction of the ECTs under ecologically relevant constrains.

Organization and engagement of a prefrontal-olfactory network during olfactory selective attention

Sensory perception is profoundly shaped by attention. Attending to an odor strongly regulates if and how a smell is perceived – yet the brain systems involved in this process are unknown. Here we report integration of the medial prefrontal cortex (mPFC), a collection of brain regions integral to attention, with the olfactory system in the context of selective attention to odors. First, we used tracing methods to establish the tubular striatum (TuS, also known as the olfactory tubercle) as the primary olfactory region to receive direct mPFC input in rats. Next, we recorded local field potentials from the olfactory bulb (OB), mPFC, and TuS while rats completed an olfactory selective attention task. Gamma power and coupling of gamma oscillations with theta phase were consistently high as rats flexibly switched their attention to odors. Beta and theta synchrony between mPFC and olfactory regions were elevated as rats switched their attention to odors. Finally, we found that sniffing was consistent despite shifting attentional demands, suggesting that the mPFC-OB theta coherence is independent of changes in active sampling. Together, these findings begin to define an olfactory attention network wherein mPFC activity, as well as that within olfactory regions, are coordinated in manners based upon attentional states.