OceanGPT: A Large Language Model for Ocean Science Tasks

Ocean science, which delves into the oceans that are reservoirs of life and biodiversity, is of great significance given that oceans cover over 70% of our planet's surface. Recently, advances in Large Language Models (LLMs) have transformed the paradigm in science. Despite the success in other domains, current LLMs often fall short in catering to the needs of domain experts like oceanographers, and the potential of LLMs for ocean science is under-explored. The intrinsic reason may be the immense and intricate nature of ocean data as well as the necessity for higher granularity and richness in knowledge. To alleviate these issues, we introduce OceanGPT, the first-ever LLM in the ocean domain, which is expert in various ocean science tasks. We propose DoInstruct, a novel framework to automatically obtain a large volume of ocean domain instruction data, which generates instructions based on multi-agent collaboration. Additionally, we construct the first oceanography benchmark, OceanBench, to evaluate the capabilities of LLMs in the ocean domain. Though comprehensive experiments, OceanGPT not only shows a higher level of knowledge expertise for oceans science tasks but also gains preliminary embodied intelligence capabilities in ocean technology. Codes, data and checkpoints will soon be available at https://github.com/zjunlp/KnowLM.Comment: Work in progress. Project Website: https://zjunlp.github.io/project/OceanGPT

Computational Models to Detect Radiation in Urban Environments: An Application of Signal Processing Techniques and Neural Networks to Radiation Data Analysis

Radioactive sources, such as uranium-235, are nuclides that emit ionizing radiation, and which can be used to build nuclear weapons. In public areas, the presence of a radioactive nuclide can present a risk to the population, and therefore, it is imperative that threats are identified by radiological search and response teams in a timely and effective manner. In urban environments, such as densely populated cities, radioactive sources may be more difficult to detect, since background radiation produced by surrounding objects and structures (e.g., buildings, cars) can hinder the effective detection of unnatural radioactive material. This article presents a computational model to detect radioactive sources in urban environments, which uses signal processing techniques to identify radiation signatures. Moreover, the model uses artificial neural networks to identify types of radiation sources, classifying them as innocuous or harmful, and discerning between weapons-grade material and radioactive isotopes used in medical or industrial settings

Asset Cueing Nuclear Radiation Anomaly Detection Using an Embedded Neural Network Resource

Nuclear radiation detection is inherently a challenging task, coupled with a high background variation or increase in anomalies, the accuracy for detection can plummet. A key factor in the success of nuclear detection hinges on the sensor’s ability to generalize its model and directly leads to the model’s robustness. The goal of this project is to develop algorithms suitable for use on the University of Nebraska-Lincoln’s Pingora chip, a low-power, system-on-chip device with an active neural processing unit (NPU) made for nuclear radiation detection. The thesis aims to improve Pingora’s overall generalization ability in nuclear radiation source detection. A multiphase multi-layer perceptron neural network (MLPNN) design was used to train the network offline until a low error rate was achieved. The development dataset includes over 100,000 samples with varying source presence. The difficulty of working with this dataset was the high variation in the data characteristics for both background and source samples. Regardless, the model achieved on average a 12% error across all test sets, including the worst-case dataset, which was defined as the dataset that includes the least identifiable characteristics. Advisors: Michael Hoffman and Sina Balkı

NASA Thesaurus. Volume 1: Hierarchical listing

There are 16,713 postable terms and 3,716 nonpostable terms approved for use in the NASA scientific and technical information system in the Hierarchical Listing of the NASA Thesaurus. The generic structure is presented for many terms. The broader term and narrower term relationships are shown in an indented fashion that illustrates the generic structure better than the more widely used BT and NT listings. Related terms are generously applied, thus enhancing the usefulness of the Hierarchical Listing. Greater access to the Hierarchical Listing may be achieved with the collateral use of Volume 2 - Access Vocabulary

NASA thesaurus. Volume 1: Hierarchical Listing

There are over 17,000 postable terms and nearly 4,000 nonpostable terms approved for use in the NASA scientific and technical information system in the Hierarchical Listing of the NASA Thesaurus. The generic structure is presented for many terms. The broader term and narrower term relationships are shown in an indented fashion that illustrates the generic structure better than the more widely used BT and NT listings. Related terms are generously applied, thus enhancing the usefulness of the Hierarchical Listing. Greater access to the Hierarchical Listing may be achieved with the collateral use of Volume 2 - Access Vocabulary and Volume 3 - Definitions

Categorization of Radioxenon

This report summarizes a study into some false positive issues in the use of radioxenon as a method to verify a clandestine nuclear weapons explosion. False positives arise due to similarities between the radioxenon signature generated in medical isotope production and that generated in a nuclear weapon explosion. This report also discusses how to categorize the radioxenon by levels of urgency for manual analysis and interpretation and recommends applying machine learning and time series analysis techniques in the automation of radioxenon characterization. The literature indicates that medical isotope production is a major contributor to atmospheric radioxenon and is the main source of confusion in determining the source of radioxenon. While radioxenon emissions from nuclear power plants can be distinguished from that from nuclear weapon explosions, emissions from medical isotope production generate signatures similar to certain nuclide ratios found in nuclear weapons explosions. Different techniques for analyzing nuclide concentrations and ratios as well as including other sensing modalities via sensor fusion are discussed

Master of Science

thesisThis thesis encompasses the experimentation and development of neutron activation analysis protocols for the University of Utah Nuclear Engineering Program (UNEP). The University of Utah TRIGA Reactor (UUTR) was used as a neutron source to activate various materials to examine the inorganic elements. The Activity Estimator calculator was developed to approximate the activities of activated isotopes. Gamma ray activities, from activated samples, were acquired and measured on high purity germanium gamma spectroscopy detectors. Using the data collected from the gamma spectroscopy activated isotopes were identified and quantified. The activities from the identified isotopes were used to calculate the elemental concentrations of the sample materials using the Elemental Concentration Calculator and SRM Ratio Calculator. Complete NAA protocols and procedures were developed for a wide variety of materials and uses such as: criminal forensics, metals in soil, rock and water as well as minerals in fruits and vegetables