Using Individualised Choice Maps to Capture the Spatial Dimensions of Value Within Choice Experiments

Understanding how the value of environmental goods and services is influenced by their location relative to where people live can help identify the economically optimal spatial distribution of conservation interventions across landscapes. However, capturing these spatial relationships within the confines of a stated preference study has proved challenging. We propose and implement a novel approach to incorporating space within the design and presentation of stated preference choice experiments (CE). Using an investigation of preferences concerning land use change in Great Britain, CE scenarios are presented through individually generated maps, tailored to each respondent’s home location. Each choice situation is generated in real time and is underpinned by spatially tailored experimental designs that reflect current British land uses and incorporate locational attributes relating to physical and administrative dimensions of space. To the best of our knowledge, this represents the first CE study to integrate space into both the survey design and presentation of choice tasks in this way. Presented methodology provides means for testing how presentation of spatial information influence stated preferences. We contrast our spatially explicit (mapped) approach with a commonly applied tabular CE approach finding that the former exhibits a number of desirable characteristics relative to the latter

Genome sequencing analysis identifies new loci associated with Lewy body dementia and provides insights into its genetic architecture

The genetic basis of Lewy body dementia (LBD) is not well understood. Here, we performed whole-genome sequencing in large cohorts of LBD cases and neurologically healthy controls to study the genetic architecture of this understudied form of dementia, and to generate a resource for the scientific community. Genome-wide association analysis identified five independent risk loci, whereas genome-wide gene-aggregation tests implicated mutations in the gene GBA. Genetic risk scores demonstrate that LBD shares risk profiles and pathways with Alzheimer's disease and Parkinson's disease, providing a deeper molecular understanding of the complex genetic architecture of this age-related neurodegenerative condition

A Scheduling Algorithm for Defeating Collusion

By exploiting idle time on volunteer machines, desktop grids provide a way to execute large sets of tasks with negligible maintenance and low cost. Although desktop grids are attractive for cost-conscious projects, relying on external resources may compromise the correctness of application execution due to the well-known unreliability of nodes. In this paper, we consider the most challenging threat model: organized groups of cheaters that may collude to produce incorrect results. By using a previously described on-line algorithm for detecting collusion and characterizing the participant behaviors, we propose a scheduling algorithm that tackles collusion. Using several real-life traces, we show that our approach min- imizes redundancy while maximizing the number of correctly certified results

2014 SEAFDEC/AQD highlights

The SEAFDEC Aquaculture Department is mandated to: 1) conduct scientific research to generate aquaculture technologies appropriate for Southeast Asia; 2) develop managerial, technical and skilled manpower for the aquaculture sector; and, 3) disseminate and exchange aquaculture information. The Aquaculture Department in the Philippines maintains 4 stations: the Tigbauan Main Station and Dumangas Brackishwater Station in Iloilo; the Igang Marine Station in Guimaras; and, the Binangonan Freshwater Station in Rizal. Highlights are provided of the seven research programmes and activities conducted by the department during the year 2014

Proceedings, MSVSCC 2011

Proceedings of the 5th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 14, 2011 at VMASC in Suffolk, Virginia. 186 pp

Beyond Traditional Teaching: The Potential of Large Language Models and Chatbots in Graduate Engineering Education

In the rapidly evolving landscape of education, digital technologies have repeatedly disrupted traditional pedagogical methods. This paper explores the latest of these disruptions: the potential integration of large language models (LLMs) and chatbots into graduate engineering education. We begin by tracing historical and technological disruptions to provide context and then introduce key terms such as machine learning and deep learning and the underlying mechanisms of recent advancements, namely attention/transformer models and graphics processing units. The heart of our investigation lies in the application of an LLM-based chatbot in a graduate fluid mechanics course. We developed a question bank from the course material and assessed the chatbot's ability to provide accurate, insightful responses. The results are encouraging, demonstrating not only the bot's ability to effectively answer complex questions but also the potential advantages of chatbot usage in the classroom, such as the promotion of self-paced learning, the provision of instantaneous feedback, and the reduction of instructors' workload. The study also examines the transformative effect of intelligent prompting on enhancing the chatbot's performance. Furthermore, we demonstrate how powerful plugins like Wolfram Alpha for mathematical problem-solving and code interpretation can significantly extend the chatbot's capabilities, transforming it into a comprehensive educational tool. While acknowledging the challenges and ethical implications surrounding the use of such AI models in education, we advocate for a balanced approach. The use of LLMs and chatbots in graduate education can be greatly beneficial but requires ongoing evaluation and adaptation to ensure ethical and efficient use.Comment: 44 pages, 16 figures, preprint for PLOS ON