Risk assessment of atmospheric emissions using machine learning

Supervised and unsupervised machine learning algorithms are used to perform statistical and logical analysis of several transport and dispersion model runs which simulate emissions from a fixed source under different atmospheric conditions. <br><br> First, a clustering algorithm is used to automatically group the results of different transport and dispersion simulations according to specific cloud characteristics. Then, a symbolic classification algorithm is employed to find complex non-linear relationships between the meteorological input conditions and each cluster of clouds. The patterns discovered are provided in the form of probabilistic measures of contamination, thus suitable for result interpretation and dissemination. <br><br> The learned patterns can be used for quick assessment of the areas at risk and of the fate of potentially hazardous contaminants released in the atmosphere

Personality And Language Behavior: A Restatement 1

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/98370/1/j.1467-1770.1979.tb01059.x.pd

The Neural Processes of Perceived Simultaneity and Temporal Order in Younger and Older Adults using EEG.

In order to make sense of the world, the central nervous system (CNS) must determine the temporal order of events and integrate cues that belong together. The process of integrating information from multiple sensory modalities is referred to as multisensory integration. The importance of this process is evident in everyday events such as speech communication or watching a movie. These events give rise to both auditory and visual sensations that are truly simultaneous or successive, which the CNS must determine. This thesis presents two experiments designed to determine how the CNS of younger and older adults processes audiovisual information to identify simultaneity and temporal order of events. 28 younger (experiment 1) and 28 older (experiment 2) adults participated in audiovisual tasks in which they were asked to decide whether audiovisual stimuli were presented simultaneously or successively (SJ) or which stimulus was presented first (TOJ). The probability of judging a light and a sound as occurring simultaneously, or whether a light occurred first were calculated to extract the point of subjective simultaneity (PSS) and the temporal binding window (TBW). The TBW represents the time within which auditory and visual cues are most likely perceived as being simultaneous. Event-related potentials (ERPs) time-locked to light and sound onset presented at 4 different stimulus onset asynchronies (SOAs) were also recorded. Results revealed task specific differences in perceiving simultaneity and temporal order, suggesting that each task may be subserved via different neural mechanisms. Auditory N1 and visual P1 ERP amplitudes confirmed that unisensory processing of audiovisual stimuli did not differ between the two tasks, indicating that performance differences between tasks arise from multisensory integration. Despite multisensory integration being implicated, the dissociation between SJ and TOJ was not revealed through auditory N1 and visual P1 amplitudes and latencies thus indicating that the decision-making role of higher-level networks may be contributing to the differences that exist between the two tasks. Consistent with previous literature, behavioural data tended towards older adults having a wider TBW than younger adults. While all participants had reported normal audition and vision, older adults showed a later visual P1 latency indicating that unisensory processing of visual information may be delayed with age. Compared to younger adults, older adults showed a sustained higher FCz auditory N1 ERP amplitude response across SOAs, which could correspond with broader response properties expected from an extended TBW. Together, this thesis provides compelling evidence that different neural mechanisms sub serve the SJ and TOJ tasks and that simultaneity and temporal order perception change with age

Biological and Pharmacological Activity of Plant Natural Compounds II

The Special issue "Biological and Pharmacological Activity of Plant Natural Compounds II" is continuing the intriguing research on the use of natural plant products. The second edition follows the aim of the first one

Applicability domains of neural networks for toxicity prediction

In this paper, the term "applicability domain" refers to the range of chemical compounds for which the statistical quantitative structure-activity relationship (QSAR) model can accurately predict their toxicity. This is a crucial concept in the development and practical use of these models. First, a multidisciplinary review is provided regarding the theory and practice of applicability domains in the context of toxicity problems using the classical QSAR model. Then, the advantages and improved performance of neural networks (NNs), which are the most promising machine learning algorithms, are reviewed. Within the domain of medicinal chemistry, nine different methods using NNs for toxicity prediction were compared utilizing 29 alternative artificial intelligence (AI) techniques. Similarly, seven NN-based toxicity prediction methodologies were compared to six other AI techniques within the realm of food safety, 11 NN-based methodologies were compared to 16 different AI approaches in the environmental sciences category and four specific NN-based toxicity prediction methodologies were compared to nine alternative AI techniques in the field of industrial hygiene. Within the reviewed approaches, given known toxic compound descriptors and behaviors, we observed a difficulty in being able to extrapolate and predict the effects with untested chemical compounds. Different methods can be used for unsupervised clustering, such as distance-based approaches and consensus-based decision methods. Additionally, the importance of model validation has been highlighted within a regulatory context according to the Organization for Economic Co-operation and Development (OECD) principles, to predict the toxicity of potential new drugs in medicinal chemistry, to determine the limits of detection for harmful substances in food to predict the toxicity limits of chemicals in the environment, and to predict the exposure limits to harmful substances in the workplace. Despite its importance, a thorough application of toxicity models is still restricted in the field of medicinal chemistry and is virtually overlooked in other scientific domains. Consequently, only a small proportion of the toxicity studies conducted in medicinal chemistry consider the applicability domain in their mathematical models, thereby limiting their predictive power to untested drugs. Conversely, the applicability of these models is crucial; however, this has not been sufficiently assessed in toxicity prediction or in other related areas such as food science, environmental science, and industrial hygiene. Thus, this review sheds light on the prevalent use of Neural Networks in toxicity prediction, thereby serving as a valuable resource for researchers and practitioners across these multifaceted domains that could be extended to other fields in future research

Leveraging eXtented Reality & Human-Computer Interaction for User Experi- ence in 360◦ Video

EXtended Reality systems have resurged as a medium for work and entertainment. While 360o video has been characterized as less immersive than computer-generated VR, its realism, ease of use and affordability mean it is in widespread commercial use. Based on the prevalence and potential of the 360o video format, this research is focused on improving and augmenting the user experience of watching 360o video. By leveraging knowledge from Extented Reality (XR) systems and Human-Computer Interaction (HCI), this research addresses two issues affecting user experience in 360o video: Attention Guidance and Visually Induced Motion Sickness (VIMS). This research work relies on the construction of multiple artifacts to answer the de- fined research questions: (1) IVRUX, a tool for analysis of immersive VR narrative expe- riences; (2) Cue Control, a tool for creation of spatial audio soundtracks for 360o video, as well as enabling the collection and analysis of captured metrics emerging from the user experience; and (3) VIMS mitigation pipeline, a linear sequence of modules (including optical flow and visual SLAM among others) that control parameters for visual modi- fications such as a restricted Field of View (FoV). These artifacts are accompanied by evaluation studies targeting the defined research questions. Through Cue Control, this research shows that non-diegetic music can be spatialized to act as orientation for users. A partial spatialization of music was deemed ineffective when used for orientation. Addi- tionally, our results also demonstrate that diegetic sounds are used for notification rather than orientation. Through VIMS mitigation pipeline, this research shows that dynamic restricted FoV is statistically significant in mitigating VIMS, while mantaining desired levels of Presence. Both Cue Control and the VIMS mitigation pipeline emerged from a Research through Design (RtD) approach, where the IVRUX artifact is the product of de- sign knowledge and gave direction to research. The research presented in this thesis is of interest to practitioners and researchers working on 360o video and helps delineate future directions in making 360o video a rich design space for interaction and narrative.Sistemas de Realidade EXtendida ressurgiram como um meio de comunicação para o tra- balho e entretenimento. Enquanto que o vídeo 360o tem sido caracterizado como sendo menos imersivo que a Realidade Virtual gerada por computador, o seu realismo, facili- dade de uso e acessibilidade significa que tem uso comercial generalizado. Baseado na prevalência e potencial do formato de vídeo 360o, esta pesquisa está focada em melhorar e aumentar a experiência de utilizador ao ver vídeos 360o. Impulsionado por conhecimento de sistemas de Realidade eXtendida (XR) e Interacção Humano-Computador (HCI), esta pesquisa aborda dois problemas que afetam a experiência de utilizador em vídeo 360o: Orientação de Atenção e Enjoo de Movimento Induzido Visualmente (VIMS). Este trabalho de pesquisa é apoiado na construção de múltiplos artefactos para res- ponder as perguntas de pesquisa definidas: (1) IVRUX, uma ferramenta para análise de experiências narrativas imersivas em VR; (2) Cue Control, uma ferramenta para a criação de bandas sonoras de áudio espacial, enquanto permite a recolha e análise de métricas capturadas emergentes da experiencia de utilizador; e (3) canal para a mitigação de VIMS, uma sequência linear de módulos (incluindo fluxo ótico e SLAM visual entre outros) que controla parâmetros para modificações visuais como o campo de visão restringido. Estes artefactos estão acompanhados por estudos de avaliação direcionados para às perguntas de pesquisa definidas. Através do Cue Control, esta pesquisa mostra que música não- diegética pode ser espacializada para servir como orientação para os utilizadores. Uma espacialização parcial da música foi considerada ineficaz quando usada para a orientação. Adicionalmente, os nossos resultados demonstram que sons diegéticos são usados para notificação em vez de orientação. Através do canal para a mitigação de VIMS, esta pesquisa mostra que o campo de visão restrito e dinâmico é estatisticamente significante ao mitigar VIMS, enquanto mantem níveis desejados de Presença. Ambos Cue Control e o canal para a mitigação de VIMS emergiram de uma abordagem de Pesquisa através do Design (RtD), onde o artefacto IVRUX é o produto de conhecimento de design e deu direcção à pesquisa. A pesquisa apresentada nesta tese é de interesse para profissionais e investigadores tra- balhando em vídeo 360o e ajuda a delinear futuras direções em tornar o vídeo 360o um espaço de design rico para a interação e narrativa

Pruning methods for rule induction

Machine learning is a research area within computer science that is mainly concerned with discovering regularities in data. Rule induction is a powerful technique used in machine learning wherein the target concept is represented as a set of rules. The attraction of rule induction is that rules are more transparent and easier to understand compared to other induction methods (e.g., regression methods or neural network). Rule induction has been shown to outperform other learners on many problems. However, it is not suitable to handle exceptions and noisy data in training sets, which can be solved by pruning. This thesis is concerned with investigating whether preceding rule induction with instance reduction techniques can help in reducing the complexity of rule sets by reducing the number of rules generated without adversely affecting the predictive accuracy. An empirical study is undertaken to investigate the application of three different rule classifiers to datasets that were previously reduced with promising instance-reduction methods. Furthermore, we propose a new instance reduction method based on Ant Colony Optimization (ACO). We evaluate the effectiveness of this instance reduction method for k nearest neighbour algorithms in term of predictive accuracy and amount of reduction. Then we compared it with other instance reduction methods.We show that pruning classification rules with instance-reduction methods lead to a statistically significant decrease in the number of generated rules, without adversely affecting performance. On the other hand, applying instance-reduction methods enhances the predictive accuracy on some datasets. Moreover, the results provide evidence that: (1) our proposed instance reduction method based on ACO is competitive with the well-known k-NN algorithm; (2) the reduced sets computed by our method offers better classification accuracy than those obtained by the compared algorithms