Modelling multimodal passenger choices with stated preference data

Redland Shire Council has recently started the implementation of an Integrated Local Transport Plan (ILTP) that aims to reduce the car dependency by enhancing the usage of alternative modes of transport. A multi mode choice model is required that can forecast the travel behaviour across the region in order to achieve the targets set in ILTP. This paper presents the findings of a state-of-the-art literature review done on mode choice modelling and outlines the development and calibration of a model to investigate the travel behaviour of Redlands’ residents. The present study attempts to develop a nested logit model and calibrate it using data obtained from a stated preference (SP) survey to be conducted in the Shire. The model development will consider all the vital attributes of the travelling modes used in the study area including various public transit access modes. The possibility of combining SP and revealed preference (RP) data to calibrate the model using joint-estimation method will be further assessed. It is expected that the outcomes of the research will assist policy makers in the areas of public transport planning and the development of network for public transport access modes including walkways and cycleways

Injection Safety in Central Asia

The capstone reviews the issue of injection safety in Central Asia. Unsafe injections have been a cause of several HIV outbreaks in the region and poses a significant public health challenge. The capstone goes over the process used to engage the local health departments to assess injection practices in the region and the development of an assessment tool to be used to evaluate injection safety practices in the region

ARTIFICIAL INTELLIGENCE (AI) AS SUSTAINABLE SOLUTION FOR THE AGRICULTURE SECTOR: FINDINGS FROM DEVELOPING ECONOMIES

Agricultural production plays an important role both in national and global economies. The efficient and safe methods of sustainable agricultural production is crucial, and the use of information technology is imperative to meet this end. Among the available information technology tools, this study highlights the IT based cognitive solutions supported with the artificial intelligence (AI) algorithm for sustainable solutions in the agriculture sector of developing economies. For this purpose, a systematic review of 87 papers has been conducted in the chosen last 20 years from 2000 to 2019 to identify the major trends, challenges, limitation related to the applicability of AI supported cognitive solutions in the agricultural industry of developing countries. The results derived from the systematic literature review represents some major flaws in the existing technological & cognitive solutions being used for agriculture means in the developing economies, with special emphasis on the lack of advanced AI techniques that are required for development of robust and precise farming methods. This is due the farmers’ inability to use sustainable technological solutions that are limited by the high cost of available technological tools. Moreover, contrary to other disciplines of science, a human expertise is scare and very costly in the agriculture industry. Hence, there is a need to actively introduce the concept of AI in the agriculture sector by making AI more viable and affordable for the farming community in the developing economies. Besides, there is also a need to create a centralized AI model for the agriculture industry which will integrate AI into a single central system for the entire economy that could be used in various enterprises of the agriculture industry.

ARTIFICIAL INTELLIGENCE (AI) AS SUSTAINABLE SOLUTION FOR THE AGRICULTURE SECTOR: FINDINGS FROM DEVELOPING ECONOMIES

Agricultural production plays an important role both in national and global economies. The efficient and safe methods of sustainable agricultural production is crucial, and the use of information technology is imperative to meet this end. Among the available information technology tools, this study highlights the IT based cognitive solutions supported with the artificial intelligence (AI) algorithm for sustainable solutions in the agriculture sector of developing economies. For this purpose, a systematic review of 87 papers has been conducted in the chosen last 20 years from 2000 to 2019 to identify the major trends, challenges, limitation related to the applicability of AI supported cognitive solutions in the agricultural industry of developing countries. The results derived from the systematic literature review represents some major flaws in the existing technological & cognitive solutions being used for agriculture means in the developing economies, with special emphasis on the lack of advanced AI techniques that are required for development of robust and precise farming methods. This is due the farmers’ inability to use sustainable technological solutions that are limited by the high cost of available technological tools. Moreover, contrary to other disciplines of science, a human expertise is scare and very costly in the agriculture industry. Hence, there is a need to actively introduce the concept of AI in the agriculture sector by making AI more viable and affordable for the farming community in the developing economies. Besides, there is also a need to create a centralized AI model for the agriculture industry which will integrate AI into a single central system for the entire economy that could be used in various enterprises of the agriculture industry.

Locality-aware data replication in the Last-Level Cache

Next generation multicores will process massive data with varying degree of locality. Harnessing on-chip data locality to optimize the utilization of cache and network resources is of fundamental importance. We propose a locality-aware selective data replication protocol for the last-level cache (LLC). Our goal is to lower memory access latency and energy by replicating only high locality cache lines in the LLC slice of the requesting core, while simultaneously keeping the off-chip miss rate low. Our approach relies on low overhead yet highly accurate in-hardware run-time classification of data locality at the cache line granularity, and only allows replication for cache lines with high reuse. Furthermore, our classifier captures the LLC pressure at the existing replica locations and adapts its replication decision accordingly. The locality tracking mechanism is decoupled from the sharer tracking structures that cause scalability concerns in traditional coherence protocols. Moreover, the complexity of our protocol is low since no additional coherence states are created. On a set of parallel benchmarks, our protocol reduces the overall energy by 16%, 14%, 13% and 21% and the completion time by 4%, 9%, 6% and 13% when compared to the previously proposed Victim Replication, Adaptive Selective Replication, Reactive-NUCA and Static-NUCA LLC management schemes

The locality-aware adaptive cache coherence protocol

Next generation multicore applications will process massive amounts of data with significant sharing. Data movement and management impacts memory access latency and consumes power. Therefore, harnessing data locality is of fundamental importance in future processors. We propose a scalable, efficient shared memory cache coherence protocol that enables seamless adaptation between private and logically shared caching of on-chip data at the fine granularity of cache lines. Our data-centric approach relies on in-hardware yet low-overhead runtime profiling of the locality of each cache line and only allows private caching for data blocks with high spatio-temporal locality. This allows us to better exploit the private caches and enable low-latency, low-energy memory access, while retaining the convenience of shared memory. On a set of parallel benchmarks, our low-overhead locality-aware mechanisms reduce the overall energy by 25% and completion time by 15% in an NoC-based multicore with the Reactive-NUCA on-chip cache organization and the ACKwise limited directory-based coherence protocol.United States. Defense Advanced Research Projects Agency. The Ubiquitous High Performance Computing Progra

A Case for Fine-Grain Adaptive Cache Coherence

As transistor density continues to grow geometrically, processor manufacturers are already able to place a hundred cores on a chip (e.g., Tilera TILE-Gx 100), with massive multicore chips on the horizon. Programmers now need to invest more effort in designing software capable of exploiting multicore parallelism. The shared memory paradigm provides a convenient layer of abstraction to the programmer, but will current memory architectures scale to hundreds of cores? This paper directly addresses the question of how to enable scalable memory systems for future multicores. We develop a scalable, efficient shared memory architecture that enables seamless adaptation between private and logically shared caching at the fine granularity of cache lines. Our data-centric approach relies on in hardware runtime profiling of the locality of each cache line and only allows private caching for data blocks with high spatio-temporal locality. This allows us to better exploit on-chip cache capacity and enable low-latency memory access in large-scale multicores