Reusable Container System Optimization for Smart Cities

Federal and local governments are investing in methods to discourage use of disposable containers in order to reduce waste generation and protect the environment. In this project we propose the use of reusable takeout food containers as a replacement for disposable takeout food containers. Reusable takeout container systems may use barcode or RFID (radio frequency identification) technology to track and manage the distribution, collection, cleaning, and end-of-life recycling of reusable takeout food containers. Such systems will require the use of container collection bins. The design and optimization of a network of container collection bins is the topic of this project. We propose a method to optimize the location network of collection bins at a Smart City. As a case study we use data collected in the city of San Luis Obispo, CA. The reusable container use cycle can be described as follows. A company provides the reusable takeout food containers to restaurants. The restaurants distribute these containers to their customers. After the container is used a customer drops it off in a convenient location for the company to pick it up and wash it. Since convenience of container drop off is crucial to customer participation, strategically placing the drop off bins around the city such that they are highly visible and easily accessible will maximize user satisfaction and benefit to the city. Determining the optimal set of container collection bin locations was performed using a linear programming model that optimized the bin network visibility and accessibility. Visibility and accessibility were measured by traffic volume, pedestrian volume, and population density. The optimization model included varying the quantities of drop-off bins, as well as varying bin sizes and costs. An economic analysis was used to determine the optimal combination of quantity of bins, bin size, and bin cost that maximized the benefit to the city. We simulated the potential container collection routes in order to estimate collection and transportation times and determine the optimal set of collection routes. Similar to the linear programming model, the simulation model also had variable input capabilities. The flexibility of our models may prove useful for future efforts to plan reusable container systems for Smart Cities

Comparación del rendimiento entre angular4 y reactjs, basado en el modelo rail, en la progressive web app de Glup S.A.

El presente trabajo fue desarrollado con la finalidad de determinar que tecnología web, Angular4 o ReactJS, posee un mejor rendimiento. Para lo cual se usó como referencia al modelo RAIL, el cual define un conjunto de directrices que se deben cumplir para que una aplicación web pueda otorgar un buen rendimiento. Este trabajo cuenta con una gran cantidad de información recopilada relacionada al funcionamiento de las aplicaciones web, las nuevas tecnologías, estrategias que han surgido en los últimos años orientados a mejorar el rendimiento de una aplicación web y el impacto económico del rendimiento de una aplicación web en una empresa. Para lograr determinar la diferencia entre estas dos tecnologías, se desarrollaron dos aplicaciones software bajo la metodología XP, una con la tecnología ReactJS y otra con Angular4. Ambas aplicaciones fueron desplegadas en el servidor principal de la empresa GLUP S.A. durante un periodo de 36 días (18 días por tecnología) en los cuales se recopilo información de los dispositivos de los usuarios que accedieron a la página web de GLUP S.A. La información que se recolecto aborda los 4 pilares principales del rendimiento definidos por RAIL, la velocidad de respuesta a una acción, cantidad de fotogramas perdidos en una animación, cantidad de tiempos inactivos del CPU y la velocidad de carga de la página web. Todos los datos fueron procesados con la herramienta SPSS statistics 23, lo cual permitió concluir que Angular4 tiene una mejor velocidad de respuesta a acciones y permite animaciones más fluidez que React, sin embargo, React permite la aparición de mayores tiempos de inactividad del CPU y un mejor tiempo de carga

Subheap-Augmented Garbage Collection

Automated memory management avoids the tedium and danger of manual techniques. However, as no programmer input is required, no widely available interface exists to permit principled control over sometimes unacceptable performance costs. This dissertation explores the idea that performance-oriented languages should give programmers greater control over where and when the garbage collector (GC) expends effort. We describe an interface and implementation to expose heap partitioning and collection decisions without compromising type safety. We show that our interface allows the programmer to encode a form of reference counting using Hayes\u27 notion of key objects. Preliminary experimental data suggests that our proposed mechanism can avoid high overheads suffered by tracing collectors in some scenarios, especially with tight heaps. However, for other applications, the costs of applying subheaps---in human effort and runtime overheads---remain daunting

Distributed D3: A web-based distributed data visualisation framework for Big Data

The influx of Big Data has created an ever-growing need for analytic tools targeting towards the acquisition of insights and knowledge from large datasets. Visual perception as a fundamental tool used by humans to retrieve information from the outside world around us has its unique ability to distinguish patterns pre-attentively. Visual analytics via data visualisations is therefore a very powerful tool and has become ever more important in this era. Data-Driven Documents (D3.js) is a versatile and popular web-based data visualisation library that has tended to be the standard toolkit for visualising data in recent years. However, the library is technically inherent and limited in capability by the single thread model of a single browser window in a single machine, and therefore not able to deal with large datasets. The main objective of this thesis is to overcome this limitation and address possible challenges by developing the Distributed D3 framework that employs distributed mechanism to enable the possibility of delivering web-based visualisations for large-scale data, which also allows to effectively utilise the graphical computational resources of the modern visualisation environments. As a result, the first contribution is that the integrated version of Distributed D3 framework has been developed for the Data Observatory. The work proves the concept of Distributed D3 is feasible in reality and also enables developers to collaborate on large-scale data visualisations by using it on the Data Observatory. The second contribution is that the Distributed D3 has been optimised by investigating the potential bottlenecks for large-scale data visualisation applications. The work finds the key performance bottlenecks of the framework and shows an improvement of the overall performance by 35.7% after optimisations, which improves the scalability and usability of Distributed D3 for large-scale data visualisation applications. The third contribution is that the generic version of Distributed D3 framework has been developed for the customised environments. The work improves the usability and flexibility of the framework and makes it ready to be published in the open-source community for further improvements and usages.Open Acces