3,281 research outputs found

    Station segmentation of Lisbon bicycle sharing system based on users demand and supply

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    Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceBike-sharing systems are well known in the sustainable mobility field and have several aspects that need optimization and improvement. One of the most relevant aspects is station segmentation based on user demand and supply, and it is the focus of the thesis. The segmentation work has an enormous potential to reduce complexity in predicting the bicycle demand and supply, thus improving the overall quality of service. Several machine learning algorithms were used to investigate the aforementioned segmentation task. This work considers two popular and well-known clustering algorithms to extract and analyze interesting patterns, like the difference between arrivals and departures throughout time and stations: the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) and the hierarchical clustering. The algorithms are applied to the specific case of GIRA, the bicycle sharing system (BSS) of the city of Lisbon. The obtained results suggest that considering the variables under analysis, the optimal number of clusters to be used in a second phase of the BSS optimization (demand and supply forecast) is the same as the number of stations in the Lisbon BSS. The results are very insightful and allow future work to focus either on the demand forecast or the enrichment of the variables under study

    Designing algorithms to aid discovery by chemical robots

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    Recently, automated robotic systems have become very efficient, thanks to improved coupling between sensor systems and algorithms, of which the latter have been gaining significance thanks to the increase in computing power over the past few decades. However, intelligent automated chemistry platforms for discovery orientated tasks need to be able to cope with the unknown, which is a profoundly hard problem. In this Outlook, we describe how recent advances in the design and application of algorithms, coupled with the increased amount of chemical data available, and automation and control systems may allow more productive chemical research and the development of chemical robots able to target discovery. This is shown through examples of workflow and data processing with automation and control, and through the use of both well-used and cutting-edge algorithms illustrated using recent studies in chemistry. Finally, several algorithms are presented in relation to chemical robots and chemical intelligence for knowledge discovery

    11th German Conference on Chemoinformatics (GCC 2015) : Fulda, Germany. 8-10 November 2015.

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    The pharmacophore kernel for virtual screening with support vector machines

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    We introduce a family of positive definite kernels specifically optimized for the manipulation of 3D structures of molecules with kernel methods. The kernels are based on the comparison of the three-points pharmacophores present in the 3D structures of molecul es, a set of molecular features known to be particularly relevant for virtual screening applications. We present a computationally demanding exact implementation of these kernels, as well as fast approximations related to the classical fingerprint-based approa ches. Experimental results suggest that this new approach outperforms state-of-the-art algorithms based on the 2D structure of mol ecules for the detection of inhibitors of several drug targets

    An Unsupervised Neural Network for Real-Time Low-Level Control of a Mobile Robot: Noise Resistance, Stability, and Hardware Implementation

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    We have recently introduced a neural network mobile robot controller (NETMORC). The controller is based on earlier neural network models of biological sensory-motor control. We have shown that NETMORC is able to guide a differential drive mobile robot to an arbitrary stationary or moving target while compensating for noise and other forms of disturbance, such as wheel slippage or changes in the robot's plant. Furthermore, NETMORC is able to adapt in response to long-term changes in the robot's plant, such as a change in the radius of the wheels. In this article we first review the NETMORC architecture, and then we prove that NETMORC is asymptotically stable. After presenting a series of simulations results showing robustness to disturbances, we compare NETMORC performance on a trajectory-following task with the performance of an alternative controller. Finally, we describe preliminary results on the hardware implementation of NETMORC with the mobile robot ROBUTER.Sloan Fellowship (BR-3122), Air Force Office of Scientific Research (F49620-92-J-0499

    Bridging molecular docking to molecular dynamics in exploring ligand-protein recognition process: An overview

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    Computational techniques have been applied in the drug discovery pipeline since the 1980s. Given the low computational resources of the time, the first molecular modeling strategies relied on a rigid view of the ligand-target binding process. During the years, the evolution of hardware technologies has gradually allowed simulating the dynamic nature of the binding event. In this work, we present an overview of the evolution of structure-based drug discovery techniques in the study of ligand-target recognition phenomenon, going from the static molecular docking toward enhanced molecular dynamics strategies
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