We describe a method of constructing a B-rep solid model from a single hidden-line removed sketch view of a 3D object. The main steps of our approach are as follows. The sketch is first tidied in 2D (to remove digitisation errors). Line Iabelling is used to deduce the initial topology of the object and to locate hidden faces. Constraints are then produced from the line labelling and features in the drawing (such as probable symmetry) involving the unknown face coefficients and point depths. A least squares solution is found to the linear system and any grossly incompatible equations are rejected. Vertices are recalculated as the intersections of the faces to ensure we have a reconstructible solid. Any incomplete faces are then completed as far as possible from neighbounng faces, producing a solid model from the initial sketch, if successful. The current software works for polyhedral objects with tri hedral vertices.

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Structure-based drug design is a creative process that displays several features that make it closer to human reasoning than to machine automation. However, very often the user intervention is limited to the preparation of the input and analysis of the output of a computer simulation. In some cases, allowing human intervention directly in the process could improve the quality of the results by applying the researcher intuition directly into the simulation. Haptic technology has been previously explored as a useful method to interact with a chemical system. However, the need of expensive hardware and the lack of accessible software have limited the use of this technology to date. Here we are reporting the implementation of a haptic-based molecular mechanics environment aimed for interactive drug design and ligand optimization, using an easily accessible software/hardware combination.

Drug design is a creative process that combines different scientific expertise. With the development of increasingly powerful computers, disciplines such as molecular modeling and, in particular, drug design, are becoming an important component of drug discovery. However, modern software often limits the user interaction with the computer calculation, reducing the potential for researchers to use their knowledge in the design process. For this reason, interactive methodologies have been investigated in recent years. In particular, haptic-driven simulators offer the possibility for users to drive and control the modeling simulations, efficiently combining human knowledge and computational power. In this article, we will discuss the state-of-the-art and future perspectives of such methodologies.

In this article, we describe an improved cell-list approach designed to match the Kepler architecture of General-purpose graphics processing units (GPGPU). We explain how our approach improves load balancing for the above algorithm and how warp intrinsics are used to implement Newton's third law for the nonbonded force calculations. We also talk through our approach to exclusions handling together with a method to calculate bonded forces and 1-4 electrostatic scaling using a single Cuda kernel. Performance benchmarks are included in the last sections to show the linear scaling of our implementation using a step minimization method. In addition, multiple performance benchmarks demonstrate the contribution of various optimizations we used for our implementations. © 2013 Wiley Periodicals, Inc.

SUMMARYWe present the resource-aware visualization environment (RAVE) project, from its inception and design phases, through implementation choices and final actual use cases. RAVE is a distributed, collaborative Grid-enabled visualization environment that supports automated resource discovery across heterogeneous machines. RAVE runs as a background process using Web services, enabling us to share resources with other users rather than commandeering an entire machine. RAVE supports a wide range of machines, from hand-held PDAs to high-end servers with large-scale stereoscopic, tracked displays. The local display device may render all, some or none of the data set remotely, depending on its capability and present loading, utilizing remote servers to assist where necessary. This enables scientists and engineers to collaborate from their desks, in the field, or in front of specialized immersive displays.

Traffic cameras are a widely available source of open data that offer tremendous value to public authorities by providing real-time statistics to understand and monitor the activity levels of local populations and their responses to policy interventions such as those seen during the COrona VIrus Disease 2019 (COVID-19) pandemic. This paper presents an end-to-end solution based on the Google Cloud Platform with scalable processing capability to deal with large volumes of traffic camera data across the UK in a cost-efficient manner. It describes a deep learning pipeline to detect pedestrians and vehicles and to generate mobility statistics from these. It includes novel methods for data cleaning and post-processing using a Structure SImilarity Measure (SSIM)-based static mask that improves reliability and accuracy in classifying people and vehicles from traffic camera images. The solution resulted in statistics describing trends in the ‘busyness’ of various towns and cities in the UK. We validated time series against Automatic Number Plate Recognition (ANPR) cameras across North East England, showing a close correlation between our statistical output and the ANPR source. Trends were also favorably compared against traffic flow statistics from the UK’s Department of Transport. The results of this work have been adopted as an experimental faster indicator of the impact of COVID-19 on the UK economy and society by the Office for National Statistics (ONS).

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