Relevant bibliographies by topics / Prompt engineering

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Prompt engineering'

Author: Grafiati
Published: 29 July 2024
Last updated: 30 July 2024

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Journal articles on the topic "Prompt engineering"

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Leung, Chi Hong. "Promoting optimal learning with ChatGPT: A comprehensive exploration of prompt engineering in education." Asian Journal of Contemporary Education 8, no. 2 (June 6, 2024): 104–14. http://dx.doi.org/10.55493/5052.v8i2.5101.

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The purpose of this paper is to study the topic of prompt engineering, which serves as a valuable tool for teachers in creating optimal prompts that effectively enhance students' learning experiences with ChatGPT. This paper explores a variety of strategies related to prompt engineering. These strategies include assigning specific roles to ChatGPT, clearly defining objectives, applying constraints, utilizing structural prompt formats, refining answers through dialogues, and integrating practice exercises. Moreover, this paper specifically delves into relevant approaches to prompt engineering in the field of education, such as close question prompts, open question prompts, role-playing prompts, and Socratic prompts. It also presents the outcomes derived from a comprehensive survey conducted to assess teachers' attitudes towards the implementation of prompt engineering with ChatGPT. The collected data indicates that prompt engineering significantly contributes to the enhancement of the learning experience. This is achieved by tailoring prompts to suit individual needs, fostering greater engagement, promoting critical thinking skills, and facilitating collaborative and interactive learning environments. The findings of this study hold significant practical implications for educators. By effectively implementing prompt engineering strategies, teachers can fully harness the potential of ChatGPT to enhance students' learning experiences. By customizing prompts to individual students, educators can foster engagement, stimulate reasoning, and facilitate collaboration among students.
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Velásquez-Henao, Juan David, Carlos Jaime Franco-Cardona, and Lorena Cadavid-Higuita. "Prompt Engineering: a methodology for optimizing interactions with AI-Language Models in the field of engineering." DYNA 90, no. 230 (November 3, 2023): 9–17. http://dx.doi.org/10.15446/dyna.v90n230.111700.

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ChatGPT is a versatile conversational Artificial Intelligence model that responds to user input prompts, with applications in academia and various sectors. However, crafting effective prompts can be challenging, leading to potentially inaccurate or contextually inappropriate responses, emphasizing the importance of prompt engineering in achieving accurate outcomes across different domains. This study aims to address this void by introducing a methodology for optimizing interactions with Artificial Intelligence language models, like ChatGPT, through prompts in the field of engineering. The approach is called GPEI and relies on the latest advancements in this area; and consists of four steps: define the objective, design the prompt, evaluate the response, and iterate. Our proposal involves two key aspects: data inclusion in prompt design for engineering applications and the integration of Explainable Artificial Intelligence principles to assess responses, enhancing transparency. It combines insights from various methodologies to address issues like hallucinations, emphasizing iterative prompt refinement techniques like posing opposing questions and using specific patterns for improvement. This methodology could improve prompt precision and utility in engineering.
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Heston, Thomas F., and Charya Khun. "Prompt Engineering in Medical Education." International Medical Education 2, no. 3 (August 31, 2023): 198–205. http://dx.doi.org/10.3390/ime2030019.

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Artificial intelligence-powered generative language models (GLMs), such as ChatGPT, Perplexity AI, and Google Bard, have the potential to provide personalized learning, unlimited practice opportunities, and interactive engagement 24/7, with immediate feedback. However, to fully utilize GLMs, properly formulated instructions are essential. Prompt engineering is a systematic approach to effectively communicating with GLMs to achieve the desired results. Well-crafted prompts yield good responses from the GLM, while poorly constructed prompts will lead to unsatisfactory responses. Besides the challenges of prompt engineering, significant concerns are associated with using GLMs in medical education, including ensuring accuracy, mitigating bias, maintaining privacy, and avoiding excessive reliance on technology. Future directions involve developing more sophisticated prompt engineering techniques, integrating GLMs with other technologies, creating personalized learning pathways, and researching the effectiveness of GLMs in medical education.
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IŞIN, Zişan Cihangir, Hilal FIDAN, Beyşan Tarık IŞIN, Erşan IŞIN, and Tamer IŞIN. "Is Prompt Engineering a Profession?" International Journal of Artificial Intelligence & Applications 15, no. 3 (May 29, 2024): 29–39. http://dx.doi.org/10.5121/ijaia.2024.15303.

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Prompt Engineering, the systematic design and construction of prompts for human-AI interaction, raises questions regarding its professional status. This paper examines Prompt Engineering and evaluates whether it qualifies as a distinct profession. Through an analysis of its defining characteristics, including specialized skills, ethical considerations, and societal impact, this study explores the parallels between Prompt Engineering and established professions. Drawing on examples from various fields, it argues for the recognition of Prompt Engineering as a legitimate profession. By addressing the complexities of human-AI interaction and the evolving demands of technology, this research contributes to the ongoing discourse on the professionalization of emerging disciplines
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Rathod, Jay Dinesh. "Systematic Study of Prompt Engineering." International Journal for Research in Applied Science and Engineering Technology 12, no. 6 (June 30, 2024): 597–613. http://dx.doi.org/10.22214/ijraset.2024.63182.

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Abstract: Now a days Generative Artificial Intelligence is the buzz in the field of technology and science it is the implementation of the Artificial intelligence to generate different types of contents with the help of its models and ease the human life to a extend. Prompt Engineering is one of the arts of crafting instructions to guide large language models (LLMs), and has emerged as a critical technique in natural language processing (NLP). This systematic study delves into the intricacies of prompt engineering, exploring its techniques, evaluation methods, and applications. The study categorizes prompt engineering techniques into instruction-based, information-based, reformulation, and metaphorical prompts. It emphasizes the importance of evaluating prompt effectiveness using metrics like accuracy, fluency, and relevance. Additionally, the study investigates factors influencing prompt effectiveness, including prompt length, complexity, specificity, phrasing, vocabulary choice, framing, and context. The study highlights the impact of prompt engineering in enhancing LLM performance for NLP tasks like machine translation, question answering, summarization, and text generation. It underscores the role of prompt engineering in developing domainspecific LLM applications, enabling knowledge extraction, creative content generation, and addressing domain-specific challenges. The study concludes by addressing ethical considerations in prompt engineering, emphasizing the need to mitigate bias and discrimination while ensuring transparency
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Sivarajkumar, Sonish, Mark Kelley, Alyssa Samolyk-Mazzanti, Shyam Visweswaran, and Yanshan Wang. "An Empirical Evaluation of Prompting Strategies for Large Language Models in Zero-Shot Clinical Natural Language Processing: Algorithm Development and Validation Study." JMIR Medical Informatics 12 (April 8, 2024): e55318. http://dx.doi.org/10.2196/55318.

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Background Large language models (LLMs) have shown remarkable capabilities in natural language processing (NLP), especially in domains where labeled data are scarce or expensive, such as the clinical domain. However, to unlock the clinical knowledge hidden in these LLMs, we need to design effective prompts that can guide them to perform specific clinical NLP tasks without any task-specific training data. This is known as in-context learning, which is an art and science that requires understanding the strengths and weaknesses of different LLMs and prompt engineering approaches. Objective The objective of this study is to assess the effectiveness of various prompt engineering techniques, including 2 newly introduced types—heuristic and ensemble prompts, for zero-shot and few-shot clinical information extraction using pretrained language models. Methods This comprehensive experimental study evaluated different prompt types (simple prefix, simple cloze, chain of thought, anticipatory, heuristic, and ensemble) across 5 clinical NLP tasks: clinical sense disambiguation, biomedical evidence extraction, coreference resolution, medication status extraction, and medication attribute extraction. The performance of these prompts was assessed using 3 state-of-the-art language models: GPT-3.5 (OpenAI), Gemini (Google), and LLaMA-2 (Meta). The study contrasted zero-shot with few-shot prompting and explored the effectiveness of ensemble approaches. Results The study revealed that task-specific prompt tailoring is vital for the high performance of LLMs for zero-shot clinical NLP. In clinical sense disambiguation, GPT-3.5 achieved an accuracy of 0.96 with heuristic prompts and 0.94 in biomedical evidence extraction. Heuristic prompts, alongside chain of thought prompts, were highly effective across tasks. Few-shot prompting improved performance in complex scenarios, and ensemble approaches capitalized on multiple prompt strengths. GPT-3.5 consistently outperformed Gemini and LLaMA-2 across tasks and prompt types. Conclusions This study provides a rigorous evaluation of prompt engineering methodologies and introduces innovative techniques for clinical information extraction, demonstrating the potential of in-context learning in the clinical domain. These findings offer clear guidelines for future prompt-based clinical NLP research, facilitating engagement by non-NLP experts in clinical NLP advancements. To the best of our knowledge, this is one of the first works on the empirical evaluation of different prompt engineering approaches for clinical NLP in this era of generative artificial intelligence, and we hope that it will inspire and inform future research in this area.
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Shepherd, Jennifer, and Donald Geisheimer. "FAQs: AI and prompt engineering." American Nurse Journal 19, no. 6 (June 8, 2024): 14–19. http://dx.doi.org/10.51256/anj062414.

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Haverkamp, Andrea. "Engineering in Crisis – Critical Reflection Writing Prompt." International Journal of Engineering, Social Justice, and Peace 8, no. 2 (October 18, 2021): 117. http://dx.doi.org/10.24908/ijesjp.v8i2.15135.

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Writing Prompt sent to the International Engineering, Social Justice, and Peace community and other engineering education sub-communitiess (primarily in North America: Our objective is to capture your thoughts, experiences, and responses to intersecting crises of COVID-19, white supremacy, anti-blackness, police violence, late capitalism, technologies and engineerings, power formations, state violence, academia, and engineering education over the past year. We wish to break the mould and create a space for the entire engineering community - students, educators, and professionals to share varied perspectives. Being oral history, this project is free from the usual academic barriers or gatekeeping. No citations needed if you do not wish to do so. While we aim to keep editorial interference at a minimum, we do not intend to include entries that (in our aesthetic and axiological judgement) can cause significant structural, cultural, or emotional harm to marginalised communities. We recognise that such filtering is hard to fully specify. The "objectives" statement above could be a guide for providing you a sense for what we are looking for. Entries should align with IJESJP's focus on engendering dialog on engineering practices that enhance gender, racial, class, and cultural equity and are democratic, non-oppressive, and non-violent. We acknowledge that even this filter limits the expression of particular forms of knowing and being. Our commitments are available here: http://esjp.org/about-esjp/our-commitments We are inspired by the way stories are told and archived through oral history, and feel the need to capture these stories before they become lost in the flux of our ongoing crises. Such history can be a story, anger and frustrations through rant, back of the envelope ideas and theories, poems, prose, fiction, critiques. This history is anything and everything you wish to document in time. Instructions: Please provide the following information by August 15th, 2021. Entry. Title, optional File upload, optional. Name, gender pronouns, and affiliations of authors Do you want your submission anonymous?
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Schmidt, Douglas C., Jesse Spencer-Smith, Quchen Fu, and Jules White. "Towards a Catalog of Prompt Patterns to Enhance the Discipline of Prompt Engineering." ACM SIGAda Ada Letters 43, no. 2 (June 6, 2024): 43–51. http://dx.doi.org/10.1145/3672359.3672364.

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The rapid advent of Large Language Models (LLMs), such as ChatGPT and Claude, is revolutionizing various fields, from education and healthcare to the engineering of reliable software systems. These LLMs operate through "prompts," which are natural language inputs that users employ to query and leverage the models' capabilities. Given the novelty of LLMs, the understanding of how to effectively use prompts remains largely anecdotal, based on isolated use cases. This fragmented approach limits the reliability and utility of LLMs, especially when they are applied in mission-critical software environments. To harness the full potential of LLMs in such crucial contexts, therefore, we need a systematic, disciplined approach to "prompt engineering" that guides interactions with and evaluations of these LLMs.
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Kochanek, Mateusz, Igor Cichecki, Oliwier Kaszyca, Dominika Szydło, Michał Madej, Dawid Jędrzejewski, Przemysław Kazienko, and Jan Kocoń. "Improving Training Dataset Balance with ChatGPT Prompt Engineering." Electronics 13, no. 12 (June 8, 2024): 2255. http://dx.doi.org/10.3390/electronics13122255.

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The rapid evolution of large language models, in particular OpenAI’s GPT-3.5-turbo and GPT-4, indicates a growing interest in advanced computational methodologies. This paper proposes a novel approach to synthetic data generation and knowledge distillation through prompt engineering. The potential of large language models (LLMs) is used to address the problem of unbalanced training datasets for other machine learning models. This is not only a common issue but also a crucial determinant of the final model quality and performance. Three prompting strategies have been considered: basic, composite, and similarity prompts. Although the initial results do not match the performance of comprehensive datasets, the similarity prompts method exhibits considerable promise, thus outperforming other methods. The investigation of our rebalancing methods opens pathways for future research on leveraging continuously developed LLMs for the enhanced generation of high-quality synthetic data. This could have an impact on many large-scale engineering applications.
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Dissertations / Theses on the topic "Prompt engineering"

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Zhou, Wenda. "Strategies To Prompt Cellular Response To Its Environment In Tissue Engineering." University of Akron / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=akron1438250827.

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Suttayamully, Somprasong. "INtelligent TRaffic Evaluator for Prompt Incident Diagnosis in a Multi Media environment : INTREPID MM /." The Ohio State University, 1995. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487864986610039.

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Thornton, Katherine C. (Katherine Claire). "Minimum carbon tax level needed to prompt a widespread shift to nuclear power." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/41687.

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Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Nuclear Science and Engineering, 2007.
Includes bibliographical references (leaves 45-50).
Carbon dioxide is suspected to be a major contributor to global warming. In the United States, nearly 70% of electricity is produced using coal or natural gas, both of which emit carbon dioxide into the environment. Nuclear power, which does not emit any carbon dioxide, produces 17% of the electricity consumed in the United States. In order to persuade utilities to switch from coal or natural gas to nuclear power and thus reduce carbon dioxide emissions, a carbon tax should be implemented. Depending on the cost of construction for new nuclear plants and the level of savings that will incentivize utilities to switch, the carbon tax needed to promote nuclear power will range between $20/tC and $200/tC. The full range of carbon tax scenarios are developed in this thesis, with the most likely carbon tax being $1 10/tC. This cost assumes a $1800/kw capital construction cost and a 10% risk perception premium on the bus bar cost of power to address the financial and industry community's somewhat negative perception of nuclear investments. From a policy perspective, this carbon tax will be more effective in causing utilities to move to nuclear power than a cap and trade policy. From a utility standpoint, switching to nuclear power under a carbon tax is less expensive than implementing a program of carbon capture and sequestration.
by Katherine C. Thornton.
S.B.
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Styczynski, John R. "Assessment of the use of prompt gamma emission for proton therapy range verification." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/54468.

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Thesis (S.M. and S.B.)--Massachusetts Institute of Technology, Dept. of Nuclear Science and Engineering, 2009.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 69-70).
PURPOSE: Prompt gamma rays emitted from proton-nucleus interactions in tissue present a promising non-invasive, in situ means of monitoring proton beam based radiotherapy. This study investigates the fluence and energy distribution of prompt gamma rays emitted during proton irradiation of phantoms. This information was used to develop a correlation between the measured and calculated gamma emission and the proton beam range, which would allow treatments to more effectively exploit the sharp distal falloff in the dose distributions of protons. METHOD & MATERIALS: A model of a cylindrical Lucite phantom with a monoenergetic proton beam and an annular array of ideal photon tallies arranged orthogonal to the beam was developed using the Monte Carlo code MCNPX 2.6.0. Heterogeneous geometries were studied by inserting metal implants into the Lucite phantom, and simulating a phantom composed of bone and lung equivalent materials and polymethyl methacrylate. RESULTS: Experimental and computational results indicated a correlation between gamma emission and the proton depth-dose profile. Several peaks were evident in the calculated energy spectrum and the 4.44 MeV emission from 12C was the most intense line having any apparent correlation with the depth dose profile. Arbitrary energy binning of 4-5 MeV and 4-8 MeV was performed on the Monte Carlo data; this binned data yielded a distinct emission peak 1cm proximal to the Bragg peak. In all cases in the Lucite phantom the position of the Bragg peak's 80% distal falloff corresponded with the position of the 4-8MeV binned 50% distal falloff. The 4-5MeV binning strategy was successful with the heterogeneous phantom in which the proton beam entered lung and stopped in bone. However, the density disparity between the bone and lung equivalent materials rendered this technique unsuccessful for the heterogeneous phantom in which the beam entered bone and stopped in lung. For this 1.4MeV binning was conducted, assessing the 1.37 MeV characteristic gamma peak of 24Mg, which was only present in the lung slab. CONCLUSIONS: The results are promising and indicate the feasibility of prompt gamma emission detection as a means of characterizing the proton beam range in situ. This study has established the measurement and omputational tools necessary to pursue the development of this technique.
by John R. Styczynski.
S.M.and S.B.
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Morrison, Christopher. "Improving Prompt Temperature Feedback by Stimulating Doppler Broadening in Heterogeneous Composite Nuclear Fuel Forms." Thesis, Rensselaer Polytechnic Institute, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10608366.

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Nuclear fuels with similar aggregate material composition, but with different millimeter and micrometer spatial configurations of the component materials can have very different safety and performance characteristics. This research focuses on modeling and attempting to engineer heterogeneous combinations of nuclear fuels to improve negative prompt temperature feedback in response to reactivity insertion accidents.

Improvements in negative prompt temperature feedback are proposed by developing a tailored thermal resistance in the nuclear fuel. In the event of a large reactivity insertion, the thermal resistance allows for a faster negative Doppler feedback by temporarily trapping heat in material zones with strong absorption resonances.

A multi-physics simulation framework was created that could model large reactivity insertions. The framework was then used to model a comparison of a heterogeneous fuel with a tailored thermal resistance and a homogeneous fuel without the tailored thermal resistance. The results from the analysis confirmed the fundamental premise of prompt temperature feedback and provide insights into the neutron spectrum dynamics throughout the transient process.

A trade study was conducted on infinite lattice fuels to help map a design space to study and improve prompt temperature feedback with many results. A multi-scale fuel pin analysis was also completed to study more realistic geometries.

The results of this research could someday allow for novel nuclear fuels that would behave differently than current fuels. The idea of having a thermal barrier coating in the fuel is contrary to most current thinking. Inherent resistance to reactivity insertion accidents could enable certain reactor types once considered vulnerable to reactivity insertion accidents to be reevaluated in light of improved negative prompt temperature feedback.

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Sutherland, Derek A. (Derek Aiden). "A study of prompt fast ion losses from neutral beam injection in the DIII-D tokamak." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/76955.

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Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Nuclear Science and Engineering, 2012.
"June 2012." Cataloged from PDF version of thesis.
Includes bibliographical references (p. 29).
A study of the prompt losses of injected neutral beam born fast ions was conducted on the DIII-D tokamak at General Atomics using scintillator based fast ion loss detectors (FILD) and a reverse orbit calculation code. Prompt losses, also called first orbit losses, result from injected neutrals that are ionized on orbits that terminate to the outer wall before making a complete neoclassical, poloidal revolution. A strike map code has been developed which generates meshes that overlay optical fast ion signals from the FILD scintillator, providing a measurement of the pitch angles and gyroradii of incident fast ions. The pitch angles and gyroradii of incident ions are inputs to a reverse orbit calculation code used to calculate the trajectories of the incident ions in reverse time back to their birth at the intersection of the reverse orbit and an overlaid neutral beam injection footprint. The megahertz (MHz) sampling frequency of the FILD scintillator, along with finer time resolution neutral beam signals, enabled a comparison of the measured time delay between the onset of the neutral beam injection and the measured FILD loss signals with the calculated transit time based on the path length of the simulated reverse orbit. Consistency between the experimentally measured transit times and the simulation orbit times was observed. This result indicates the generated strike maps which provide a measurement of incident ions' gyroradii and pitch angles are accurate. This study supplements current studies seeking to improve the understanding of fast ion transport due to magnetohydrodynamic (MHD) activity, such as reverse shear Alfven eigenmodes (RSAEs) and toroidal Alfven eigenmodes (TAEs), which will be of great importance for predominately self-heated reactor scenarios.
by Derek A. Sutherland.
S.B.
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Hadad, Kamal 1958. "Measurement of the e-folding time in prompt critical pulsing, for the University of Arizona TRIGA Mark I reactor." Thesis, The University of Arizona, 1990. http://hdl.handle.net/10150/277320.

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An electronic system was designed to receive a signal from a Self Powered Neutron Detector (SPND) and measure the initial period (e-folding time) of the TRIGA reactor at the University of Arizona. The design and fabrication of the SPND used was part of this work. The electronic system together with the SPND were used to construct a reactivity worth calibration curve for the transient rod based on pulse measurements.
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Turkoglu, Danyal J. "Evaluation of Prompt Gamma-ray Data and Nuclear Structure of Niobium-94 with Statistical Model Calculations." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1417737555.

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Alsubail, Rayan A. "Aesthetics vs. Functionality in User Prompt Design: A Mobile Interface Usability Study on the iOS Touch ID Feature." Ohio University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1447775012.

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Strong, Kristin Marie. "Supporting Adolescent Metacognition in Engineering Design Through Scripted Prompts from Peer Tutors: A Comparative Case Study." DigitalCommons@USU, 2018. https://digitalcommons.usu.edu/etd/7335.

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In 2013, developers of the Next Generation Science Standards implemented national K -12 directives and elevated engineering design to the level of scientific inquiry. Teaching design, however, is challenging to educators due to the complex nature of design problems, which cannot be solved via simple algorithms. Solving design problems requires a more reflective and iterative approach that emphasizes metacognitive skills like planning, monitoring, and taking another person’s perspective. Educators are further challenged by children’s immature metacognitive skills, which may be insufficient to engage in the entire design process. A qualitative study of paired seventh graders demonstrated a pragmatic learning activity for enhancing adolescent designs during their earliest phases through guided peer interactions with metacognitive prompts. Four distinct interaction styles were observed among the pairs. Each style varied by which verbal and social phenomena were used to make changes. The metacognitive prompts used in the learning activity can be adapted to any design challenge. Furthermore, an additional, exploratory case demonstrated a restructuring of the learning activity in which the metacognitive prompts were generated naturally by the students themselves. The student-generated prompts were design-specific and timely; delivered in the moment when a student was struggling with a design element. The result was a dynamic co-construction and co-ownership of the designs.
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Books on the topic "Prompt engineering"

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Anderson, John E., Christian Bucher, Bruno Briseghella, Xin Ruan, and Tobia Zordan, eds. Sustainable Structural Engineering. Zurich, Switzerland: International Association for Bridge and Structural Engineering (IABSE), 2015. http://dx.doi.org/10.2749/sed014.

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<p>Sustainability is the defining challenge for engineers in the twenty-first century. In addition to safe, economic, and effi-cient structures, a new criterion, sustainable, must be met. Furthermore, this new design paradigm–addressing social, economic, and environmental aspects–requires prompt action. In particular, mitigation of climate change requires sustainable solutions for new as well as existing structures. Taking from both practice and research, this book provides engineers with applicable, timely, and innovative information on the state-of-the-art in sustainable structural design. <p>This Structural Engineering Document addresses safety and regulations, integration concepts, and a sustainable approach to structural design. Life-cycle assessment is presented as a critical tool to quantify design options, and the importance of existing structures–in particular cultural heritage structures–is critically reviewed. Consideration is also given to bridge design and maintenance, structural reassessment, and disaster risk reduction. Finally, the importance of environmentally friendly concrete is examined. Consequently, structural engineers are shown to have the technical proficiency, as well as ethical imperative, to lead in designing a sustainable future.
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Trevor, Turner Barry, ed. Engineering design for profit. Chichester, [West Sussex]: Ellis Horwood, 1985.

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Lindsey, Brodie, and Knovel (Firm), eds. Competitive engineering: A handbook for systems engineering, requirements engineering, and software engineering using planguage. Oxford: Butterworth-Heinemann, 2005.

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1920-, Enrick Norbert Lloyd, and Mottley Harry E, eds. Quality control for profit. 2nd ed. New York: M. Dekker, 1985.

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Cramer, Jürgen. Financial Engineering durch Finanzinnovationen: Ertrags- und Risikooptimierung bei Banken und Unternehmen. Wiesbaden: Deutscher Universitäts-Verlag, 1993.

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1940-, MacMillan Ian C., ed. Opportunity engineering: How to profit from uncertainty without increasing risk. Upper Saddle River, N.J: Wharton School Pub., 2009.

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Dórea, Ribeiro Alexandre, and Promon (Firm), eds. Promon, 40 anos: Imagens. [São Paulo, Brazil]: DBA, 2001.

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1927-, Roulston F. R., ed. The foundations of engineering contracts. London: E. & F.N. Spon, 1989.

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Horgan, M. O'C. The foundations of engineering contracts. London: Spon, 1989.

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Cable, John. Profit-sharing and productivity: An analysis of UK engineering firms. Coventry: University of Warwick, Dept. of Economics, 1988.

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Book chapters on the topic "Prompt engineering"

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Taulli, Tom. "Prompt Engineering." In ChatGPT and Bard for Business Automation, 51–64. Berkeley, CA: Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9852-7_4.

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Kansal, Aarushi. "Prompt Engineering Techniques." In Building Generative AI-Powered Apps, 143–63. Berkeley, CA: Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0205-8_8.

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McTear, Michael, and Marina Ashurkina. "Advanced Prompt Engineering." In Transforming Conversational AI, 115–44. Berkeley, CA: Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0110-5_6.

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McTear, Michael, and Marina Ashurkina. "Introduction to Prompt Engineering." In Transforming Conversational AI, 85–113. Berkeley, CA: Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0110-5_5.

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Tamenaoul, Hamza, Mahmoud El Hamlaoui, and Mahmoud Nassar. "Prompt Engineering: User Prompt Meta Model for GPT Based Models." In Lecture Notes in Networks and Systems, 428–33. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-48573-2_61.

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Marvin, Ggaliwango, Nakayiza Hellen, Daudi Jjingo, and Joyce Nakatumba-Nabende. "Prompt Engineering in Large Language Models." In Data Intelligence and Cognitive Informatics, 387–402. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-7962-2_30.

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Chakraborty, Shayak, and Partha Pakray. "Abstractive Summarization Evaluation for Prompt Engineering." In Advances in Visual Informatics, 629–40. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-7339-2_50.

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Harmon, Sarah, and Sophia Rutman. "Prompt Engineering for Narrative Choice Generation." In Interactive Storytelling, 208–25. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-47655-6_13.

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Huang, Ken, Grace Huang, Yuyan Duan, and Ju Hyun. "Utilizing Prompt Engineering to Operationalize Cybersecurity." In Generative AI Security, 271–303. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-54252-7_9.

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Rozo-Torres, Alexander, and Wilson J. Sarmiento. "Prompt Engineering, An Alternative for Video Game Development?" In Communications in Computer and Information Science, 242–56. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-57982-0_19.

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Conference papers on the topic "Prompt engineering"

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Ahmed, Awais, Mengshu Hou, Rui Xi, Xiaoyang Zeng, and Syed Attique Shah. "Prompt-Eng: Healthcare Prompt Engineering: Revolutionizing Healthcare Applications with Precision Prompts." In WWW '24: The ACM Web Conference 2024. New York, NY, USA: ACM, 2024. http://dx.doi.org/10.1145/3589335.3651904.

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Clarisó, Robert, and Jordi Cabot. "Model-Driven Prompt Engineering." In 2023 ACM/IEEE 26th International Conference on Model Driven Engineering Languages and Systems (MODELS). IEEE, 2023. http://dx.doi.org/10.1109/models58315.2023.00020.

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Liu, Peng, He Wang, Chen Zheng, and Yuqing Zhang. "Prompt Fix: Vulnerability Automatic Repair Technology Based on Prompt Engineering." In 2024 International Conference on Computing, Networking and Communications (ICNC). IEEE, 2024. http://dx.doi.org/10.1109/icnc59896.2024.10556123.

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de Fonseca, Felipe Penhorate Carvalho, Ivandré Paraboni, and Luciano Antonio Digiampietri. "Contextual stance classification using prompt engineering." In Simpósio Brasileiro de Tecnologia da Informação e da Linguagem Humana. Sociedade Brasileira de Computação, 2023. http://dx.doi.org/10.5753/stil.2023.233708.

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This paper introduces a prompt-based method for few-shot learning addressing, as an application example, contextual stance classification, that is, the task of determining the attitude expressed by a given statement within a conversation thread with multiple points of view towards another statement. More specifically, we envisaged a method that uses the existing conversation thread (i.e., messages that are part of the test data) to create natural language prompts for few-shot learning with minimal reliance on training samples, whose preliminary results suggest that prompt engineering may be a competitive alternative to supervised methods both in terms of accuracy and development costs for the task at hand.
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Rodriguez, Alberto D., Katherine R. Dearstyne, and Jane Cleland-Huang. "Prompts Matter: Insights and Strategies for Prompt Engineering in Automated Software Traceability." In 2023 IEEE 31st International Requirements Engineering Conference Workshops (REW). IEEE, 2023. http://dx.doi.org/10.1109/rew57809.2023.00087.

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Resch, Olaf. "PROMPT ENGINEERING IN IT MANAGEMENT EDUCATION." In 18th International Technology, Education and Development Conference. IATED, 2024. http://dx.doi.org/10.21125/inted.2024.0722.

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Chakraborty, Shayak, and Partha Pakray. "ATCoPE - Abstractive text comparison using prompt engineering." In PROCEEDINGS ON SMART AND SUSTAINABLE DEVELOPMENTS IN ENGINEERING AND TECHNOLOGY: (PICET 2023). AIP Publishing, 2024. http://dx.doi.org/10.1063/5.0208416.

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Kruis, Joost, Maria Soledad Pera, Zoë ten Napel, Monica Landoni, Emiliana Murgia, Theo Huibers, and Remco Feskens. "Toward Personalised Learning Experiences: Beyond Prompt Engineering." In IDC '24: Interaction Design and Children. New York, NY, USA: ACM, 2024. http://dx.doi.org/10.1145/3628516.3659367.

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Yu, Lang, Qin Chen, Jiaju Lin, and Liang He. "Black-box Prompt Tuning for Vision-Language Model as a Service." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/187.

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In the scenario of Model-as-a-Service (MaaS), pre-trained models are usually released as inference APIs. Users are allowed to query those models with manually crafted prompts. Without accessing the network structure and gradient information, it's tricky to perform continuous prompt tuning on MaaS, especially for vision-language models (VLMs) considering cross-modal interaction. In this paper, we propose a black-box prompt tuning framework for VLMs to learn task-relevant prompts without back-propagation. In particular, the vision and language prompts are jointly optimized in the intrinsic parameter subspace with various evolution strategies. Different prompt variants are also explored to enhance the cross-model interaction. Experimental results show that our proposed black-box prompt tuning framework outperforms both hand-crafted prompt engineering and gradient-based prompt learning methods, which serves as evidence of its capability to train task-relevant prompts in a derivative-free manner.
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Ha, Hyeonmin, Jihye Lee, Wookje Han, and Byung-Gon Chun. "Meta-Learning of Prompt Generation for Lightweight Prompt Engineering on Language-Model-as-a-Service." In Findings of the Association for Computational Linguistics: EMNLP 2023. Stroudsburg, PA, USA: Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.findings-emnlp.159.

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Reports on the topic "Prompt engineering"

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Xu, Changwen. Introduction to Prompt Engineering. Ames (Iowa): Iowa State University, May 2024. http://dx.doi.org/10.31274/cc-20240624-929.

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Fariborzi, Hadi, and Piers Steel. New ChatGPT and AI Tools for Academic Research and Publishing. Instats Inc., 2024. http://dx.doi.org/10.61700/wdf7gomkxo8yh1080.

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This transformative half-day seminar is designed to equip participants with the latest AI tools and techniques to assist the process of academic research and publishing. Perfect for both newcomers and seasoned AI users, this seminar promises to revolutionize your approach to scholarly work by using AI tools as research assistants. The seminar is comprised of four separate sessions, each designed to explore different facets of AI in academic research, including: revolutionizing research with AI tools and technologies; mastering prompt engineering; navigating the ethics and security of AI; and applying AI in advanced research and writing.
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Alonso-Robisco, Andres, and Jose Manuel Carbo. Analysis of CBDC Narrative OF Central Banks using Large Language Models. Madrid: Banco de España, August 2023. http://dx.doi.org/10.53479/33412.

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Central banks are increasingly using verbal communication for policymaking, focusing not only on traditional monetary policy, but also on a broad set of topics. One such topic is central bank digital currency (CBDC), which is attracting attention from the international community. The complex nature of this project means that it must be carefully designed to avoid unintended consequences, such as financial instability. We propose the use of different Natural Language Processing (NLP) techniques to better understand central banks’ stance towards CBDC, analyzing a set of central bank discourses from 2016 to 2022. We do this using traditional techniques, such as dictionary-based methods, and two large language models (LLMs), namely Bert and ChatGPT, concluding that LLMs better reflect the stance identified by human experts. In particular, we observe that ChatGPT exhibits a higher degree of alignment because it can capture subtler information than BERT. Our study suggests that LLMs are an effective tool to improve sentiment measurements for policy-specific texts, though they are not infallible and may be subject to new risks, like higher sensitivity to the length of texts, and prompt engineering.
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Christensen, Dana Carl. Decision making in a non-profit engineering environment. Office of Scientific and Technical Information (OSTI), June 1997. http://dx.doi.org/10.2172/510338.

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Research, Gratis. Regenerative Medicine: A Breakthrough in the Branch of Medicine. Gratis Research, November 2020. http://dx.doi.org/10.47496/gr.blog.04.

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Regenerative medicine, being an interdisciplinary field, applies the principle of engineering and life science to promote regeneration. Regenerative medicine supports the treatment of chronic diseases and acute insults
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Morrison, Laura, Anushah Hossain, Myles Elledge, Brian Stoner, and Jeffrey Piascik. User-Centered Guidance for Engineering and Design of Decentralized Sanitation Technologies. RTI Press, June 2018. http://dx.doi.org/10.3768/rtipress.2018.rb.0017.1806.

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Technological innovations in sanitation are poised to address the great need for sanitation improvements in low-income countries. Worldwide, more than 2.4 billion people lack access to improved sanitation facilities. Innovative waste treatment and sanitation technologies aim to incorporate user-centered findings into technology engineering and design. Without a focus on users, even the most innovative technology solutions can encounter significant barriers to adoption. Drawing on a household survey conducted in urban slum communities of Ahmedabad, India, this research brief identifies toilet and sanitation preferences, amenities, and attributes that might promote adoption of improved sanitation technologies among potential user populations. This work uses supplemental insights gained from focus groups and findings from the literature. Based on our research, we offer specific guidance for engineering and design of sanitation products and technologies.
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Dominguez, Ximena, Regan Vidiksis, Tiffany Leones, Danae Kamdar, Ashley Lewis Presser, Marcia Bueno, and Jillian Orr. Integrating Science, Mathematics, and Engineering: Linking Home and School Learning for Young Learners. Digital Promise, April 2023. http://dx.doi.org/10.51388/20.500.12265/180.

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This report describes the co-design of a preschool science program, Early Science with Nico & NorⓇ, with partner teachers and families, curricula and media developers at GBH, and Digital Promise and EDC researchers. It also summarizes findings from a field study to understand how teachers and families utilized the resources at school and home to promote STEM teaching and learning.
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Striuk, Andrii M. Software engineering: first 50 years of formation and development. [б. в.], December 2018. http://dx.doi.org/10.31812/123456789/2880.

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The article analyzes the main stages of software engineering (SE) development. Based on the analysis of materials from the first SE conferences (1968-1969), it was determined how the software crisis prompted scientists and practitioners to join forces to form an engineering approach to programming. Differences in professional training for SE are identified. The fundamental components of the training of future software engineers are highlighted. The evolution of approaches to the design, implementation, testing and documentation of software is considered. The system scientific, technological approaches and methods for the design and construction of computer programs are highlighted. Analysis of the historical stages of the development of SE showed that despite the universal recognition of the importance of using the mathematical apparatus of logic, automata theory and linguistics when developing software, it was created empirically without its use. The factor that led practitioners to turn to the mathematical foundations of an SE is the increasing complexity of software and the inability of empirical approaches to its development and management to cope with it. The training of software engineers highlighted the problem of the rapid obsolescence of the technological content of education, the solution of which lies in its fundamentalization through the identification of the basic foundations of the industry. It is determined that mastering the basics of computer science is the foundation of vocational training in SE.
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Ступнік, М. І., В. С. Моркун, and З. П. Бакум. Information and Communication Technologies in the Process of Mining Engineer Training. Криворізький державний педагогічний університет, 2013. http://dx.doi.org/10.31812/0564/405.

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Based on scientific analysis the authors of the article argued the necessity of solving priority tasks – the development of new educational technologies aimed at supporting the training of engineers in terms of the mining engineering as high-tech industry. The features of mining computer technologies are determined. There was worked out the project of the adaptive system of a mining engineer individual training "Electronic manual" aimed at the development of future professionals. The essence of individual preparation of future mining engineer ICT is defined. It is proved that the efficiency of the designing and planning of mining operations through the introduction of ICT at present is the real way to influence the quality of mining products that will promote individual learning orientation. For the first time pedagogical foundations for introducing adaptive training of mining engineers are clarified.
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Norelli, John L., Moshe Flaishman, Herb Aldwinckle, and David Gidoni. Regulated expression of site-specific DNA recombination for precision genetic engineering of apple. United States Department of Agriculture, March 2005. http://dx.doi.org/10.32747/2005.7587214.bard.

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Objectives: The original objectives of this project were to: 1) evaluate inducible promoters for the expression of recombinase in apple (USDA-ARS); 2) develop alternative selectable markers for use in apple to facilitate the positive selection of gene excision by recombinase (Cornell University); 3) compare the activity of three different recombinase systems (Cre/lox, FLP/FRT, and R/RS)in apple using a rapid transient assay (ARO); and 4) evaluate the use of recombinase systems in apple using the best promoters, selectable markers and recombinase systems identified in 1, 2 and 3 above (Collaboratively). Objective 2 was revised from the development alternative selectable markers, to the development of a marker-free selection system for apple. This change in approach was taken due to the inefficiency of the alternative markers initially evaluated in apple, phosphomannose-isomerase and 2-deoxyglucose-6-phosphate phosphatase, and the regulatory advantages of a marker-free system. Objective 3 was revised to focus primarily on the FLP/FRT recombinase system, due to the initial success obtained with this recombinase system. Based upon cooperation between researchers (see Achievements below), research to evaluate the use of the FLP recombinase system under light-inducible expression in apple was then conducted at the ARO (Objective 4). Background: Genomic research and genetic engineering have tremendous potential to enhance crop performance, improve food quality and increase farm profits. However, implementing the knowledge of genomics through genetically engineered fruit crops has many hurdles to be overcome before it can become a reality in the orchard. Among the most important hurdles are consumer concerns regarding the safety of transgenics and the impact this may have on marketing. The goal of this project was to develop plant transformation technologies to mitigate these concerns. Major achievements: Our results indicate activity of the FLP\FRTsite-specific recombination system for the first time in apple, and additionally, we show light- inducible activation of the recombinase in trees. Initial selection of apple transformation events is conducted under dark conditions, and tissue cultures are then moved to light conditions to promote marker excision and plant development. As trees are perennial and - cross-fertilization is not practical, the light-induced FLP-mediated recombination approach shown here provides an alternative to previously reported chemically induced recombinase approaches. In addition, a method was developed to transform apple without the use of herbicide or antibiotic resistance marker genes (marker free). Both light and chemically inducible promoters were developed to allow controlled gene expression in fruit crops. Implications: The research supported by this grant has demonstrated the feasibility of "marker excision" and "marker free" transformation technologies in apple. The use of these safer technologies for the genetic enhancement of apple varieties and rootstocks for various traits will serve to mitigate many of the consumer and environmental concerns facing the commercialization of these improved varieties.
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