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Articles 17911 - 17940 of 294599

Full-Text Articles in Physical Sciences and Mathematics

Accurate Estimation Of Jujube Leaf Chlorophyll Content Using Optimized Spectral Indices And Machine Learning Methods Integrating Geospatial Information, Nigela Tuerxun, Sulei Naibi, Jianghua Zheng, Renjun Wang, Lei Wang, Binbin Lu, Danlin Yu Mar 2025

Accurate Estimation Of Jujube Leaf Chlorophyll Content Using Optimized Spectral Indices And Machine Learning Methods Integrating Geospatial Information, Nigela Tuerxun, Sulei Naibi, Jianghua Zheng, Renjun Wang, Lei Wang, Binbin Lu, Danlin Yu

Department of Earth and Environmental Studies Faculty Scholarship and Creative Works

Leaf chlorophyll content (LCC) is vital for photosynthesis and ecosystem functioning; it influences carbon, water, and energy exchanges while serving as an indicator of photosynthetic activity and nitrogen levels in precision agriculture. Hyperspectral data enable precise LCC monitoring by extracting spectral indices through optimal band combination (OBC) and predicting LCC with machine learning. However, OBC faces dimensionality issues, and machine learning models often overlook geographical influences, potentially reducing prediction accuracy. This study hypothesizes that developing spectral indices from important wavelengths and integrating geospatial data into machine learning models can address these issues and increase prediction accuracy. To test this hypothesis, …

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Lagging Brain Gene Expression Patterns Of Drosophila Melanogaster Young Adult Males Confound Comparisons Between Sexes, Flannery Mclamb, Zuying Feng, Jeanne P. Vu, Lindsey Griffin, Miguel F. Vasquez, Goran Bozinovic Mar 2025

Lagging Brain Gene Expression Patterns Of Drosophila Melanogaster Young Adult Males Confound Comparisons Between Sexes, Flannery Mclamb, Zuying Feng, Jeanne P. Vu, Lindsey Griffin, Miguel F. Vasquez, Goran Bozinovic

Center for Life in Extreme Environments Publications

Many species, including fruit flies (Drosophila melanogaster), are sexually dimorphic. Phenotypic variation in morphology, physiology, and behavior can affect development, reproduction, health, and aging. Therefore, designating sex as a variable and sex-blocking should be considered when designing experiments. The brain regulates phenotypes throughout the lifespan by balancing survival and reproduction, and sex-specific development at each life stage is likely. Changes in morphology and physiology are governed by differential gene expression, a quantifiable molecular marker for age- and sex-specific variations. We assessed the fruit fly brain transcriptome at three adult ages for gene expression signatures of sex, age, and sex-by-age: 6698 …

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Why Ai’S Role In Advancing Sustainability Is Underestimated, Lipika Bhattacharya Mar 2025

Why Ai’S Role In Advancing Sustainability Is Underestimated, Lipika Bhattacharya

CCX Research

AI has quietly, but powerfully, woven itself into the fabric of our everyday lives. Yet, AI's potential impact on creating a more sustainable world is undervalued. The author examined artificial intelligence's (AI) role in advancing sustainability. She outlined how AI can be applied in various domains such as agriculture, water management, industry, urban planning, and biodiversity conservation for transformative effects.

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A New Measure Of Non-Parametric Correlation For Variables In The Likert Scale, Shubhabrata Das Mar 2025

A New Measure Of Non-Parametric Correlation For Variables In The Likert Scale, Shubhabrata Das

Working Papers

We propose a new measure of nonparametric correlation that is especially suited for measuring association between variables measured in the Likert scale where data is ordinal and tied observations are extremely common. The proposed general structure of the measure is based on graded level of concordance and discordance between the pairs of metrics. The general form of the measure has all the desirable properties except the measure is not necessarily zero for independent variables. This limitation is acceptable given only ordinal nature of the metrics. Three versions of the measure are studied. The first is based on simple equi-distant weights. …

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Assessment Of Water Pollution Levels In Lake Batur, Indonesia And Their Effect On Water Weed Presence, Syarif Prasetyo, Tri Retnaningsih Soeprobowati, Tirtaduna Tirtaduna, Agus Purnomo, Sri Hartati, Umi Chodrijah, Prihatiningsih Prihatiningsih, Ari Wahyono, Jumari Jumari, Rossi Prabowo Mar 2025

Assessment Of Water Pollution Levels In Lake Batur, Indonesia And Their Effect On Water Weed Presence, Syarif Prasetyo, Tri Retnaningsih Soeprobowati, Tirtaduna Tirtaduna, Agus Purnomo, Sri Hartati, Umi Chodrijah, Prihatiningsih Prihatiningsih, Ari Wahyono, Jumari Jumari, Rossi Prabowo

The Philippine Agricultural Scientist

Lake Batur in Bangli Regency, Bali Province is a priority area for ecological quality restoration in Indonesia due to its vulnerability to environmental changes caused by diverse and intensive utilization. Agricultural activities, fisheries, and tourism affect the lake’s water quality, leading to the uncontrolled growth of water weeds such as water hyacinth (Pontederia crassipes). To determine the relationship between water quality, community activities, and water weed presence, water samples were collected from 9 locations consisting of residential, tourist, aquaculture, and agricultural areas. In situ analysis was then conducted to measure turbidity, pH, temperature, dissolved oxygen (DO), total dissolved …

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Draft Final Bpsou Unreclaimed Sites Ur-05 Remedial Action Work Plan (Rawp), Pioneer Technical Services, Inc. Mar 2025

Draft Final Bpsou Unreclaimed Sites Ur-05 Remedial Action Work Plan (Rawp), Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

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Draft Final Quarterly Operations And Maintenance Report Butte Treatment Lagoon System – Fourth Quarter 2024, Pioneer Technical Services, Inc. Mar 2025

Draft Final Quarterly Operations And Maintenance Report Butte Treatment Lagoon System – Fourth Quarter 2024, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

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Technical Cooperation Programme To Build Countries’ Capacities For Science-Based Sustainable Marine Management, Marc Metian, Jana Friedrich, Pere Masque, Sam Dupont, Magali Zapata, Michel Warnau, Carlos M. Alonso-Hernandez Mar 2025

Technical Cooperation Programme To Build Countries’ Capacities For Science-Based Sustainable Marine Management, Marc Metian, Jana Friedrich, Pere Masque, Sam Dupont, Magali Zapata, Michel Warnau, Carlos M. Alonso-Hernandez

Research outputs 2022 to 2026

No abstract provided.

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Revised Final - Multi-Pathway Residential Metals Abatement Program Plan, Butte-Silver Bow Department Of Reclamation And Environmental Services, Atlantic Richfield Company Mar 2025

Revised Final - Multi-Pathway Residential Metals Abatement Program Plan, Butte-Silver Bow Department Of Reclamation And Environmental Services, Atlantic Richfield Company

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

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Draft Final Quarterly Operations And Maintenance Report Butte Treatment Lagoon System – Fourth Quarter 2024, Pioneer Technical Services, Inc. Mar 2025

Draft Final Quarterly Operations And Maintenance Report Butte Treatment Lagoon System – Fourth Quarter 2024, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

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Biophysical Response Of A Coastal Woodland To Extreme Water Deficit During A Year Of Record-Breaking Heat, Caitlin E. Moore, Sally E. Thompson, Jason Beringer, Wesley Cooper, Simone Gelsinari, Hoang Long Nguyen, Huanhuan Wang, Qiaoyun Xie, Richard P. Silberstein Mar 2025

Biophysical Response Of A Coastal Woodland To Extreme Water Deficit During A Year Of Record-Breaking Heat, Caitlin E. Moore, Sally E. Thompson, Jason Beringer, Wesley Cooper, Simone Gelsinari, Hoang Long Nguyen, Huanhuan Wang, Qiaoyun Xie, Richard P. Silberstein

Research outputs 2022 to 2026

Global terrestrial and ocean surface temperatures continue to reach record levels, resulting in heat waves, drought, and prolonged heat stress experienced by vegetation in many regions. In 2023-2024, coastal terrestrial ecosystems in Western Australia were particularly affected, experiencing their driest and hottest summer since observations began in the early 1900s. Banksia woodlands are a threatened ecological community in this region that are endangered by the cumulative impacts of climate change, clearing and changing groundwater regimes, the last of which is strongly influenced by the dual use of groundwater resources by the ecosystem and by the ∼2 million people in the …

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Muon Veto Panel Uniformity Tests At Yale Wright Laboratory For The Cuore And Cupid Experiments And Visualizing Dirac Spinors In The Chiral Basis With The Bloch Sphere, Reagen Neeva Garcia Mar 2025

Muon Veto Panel Uniformity Tests At Yale Wright Laboratory For The Cuore And Cupid Experiments And Visualizing Dirac Spinors In The Chiral Basis With The Bloch Sphere, Reagen Neeva Garcia

Physics

This senior project is divided into three sections that describe my path and experiences in under graduate research. The first provides an introduction to the field of particle physics and the search for neutrinoless double beta decay (0νββ) with the Cryogenic Underground Observatory for Rare Events (CUORE) experiment, as well as its upgrade, CUPID (CUORE Upgrade with Particle IDentification). The second section details my contributions to these experiments, including my summer working at Yale’s Wright Laboratory on R&D for the CUPID experiment. In this section I describe my work on the experiment’s muon veto system and on light detector development …

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A Contrastive Framework With User, Item And Review Alignment For Recommendation, Viet Hoang Dong, Yuan Fang, Hady Wirawan Lauw Mar 2025

A Contrastive Framework With User, Item And Review Alignment For Recommendation, Viet Hoang Dong, Yuan Fang, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Learning effective latent representations for users and items is the cornerstone of recommender systems. Traditional approaches rely on user-item interaction data to map users and items into a shared latent space, but the sparsity of interactions often poses challenges. While leveraging user reviews could mitigate this sparsity, existing review-aware recommendation models often exhibit two key limitations. First, they typically rely on reviews as additional features, but reviews are not universal, with many users and items lacking them. Second, such approaches do not integrate reviews into the useritem space, leading to potential divergence or inconsistency among user, item, and review representations. …

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Hand1000: Generating Realistic Hands From Text With Only 1,000 Images, Haozhuo Zhang, Bin Zhu, Yu Cao, Yanbin Hao Mar 2025

Hand1000: Generating Realistic Hands From Text With Only 1,000 Images, Haozhuo Zhang, Bin Zhu, Yu Cao, Yanbin Hao

Research Collection School Of Computing and Information Systems

Text-to-image generation models have achieved remarkable advancements in recent years, aiming to produce realistic images from textual descriptions. However, these models often struggle with generating anatomically accurate representations of human hands. The resulting images frequently exhibit issues such as incorrect numbers of fingers, unnatural twisting or interlacing of fingers, or blurred and indistinct hands. These issues stem from the inherent complexity of hand structures and the difficulty in aligning textual descriptions with precise visual depictions of hands. To address these challenges, we propose a novel approach named Hand1000 that enables the generation of realistic hand images with target gesture using …

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Neurovig: Integrating Event Cameras For Resource-Efficient Video Grounding, Dulanga Weerakoon, Vigneshwaran Subbaraju, Joo Hwee Lim, Archan Misra Mar 2025

Neurovig: Integrating Event Cameras For Resource-Efficient Video Grounding, Dulanga Weerakoon, Vigneshwaran Subbaraju, Joo Hwee Lim, Archan Misra

Research Collection School Of Computing and Information Systems

Spatio-Temporal Video Grounding (STVG) - the task of identifying the target object in the field-of-view that the language instruction refers to - is a fundamental vision-language task. Current STVG approaches typically utilize feeds from an RGB camera that is assumed to be always-on and process the video frames using complex neural network pipelines. As a result they often impose prohibitive system overheads (energy latency) on pervasive devices. To address this we propose NeuroViG with two key innovations: (a) leveraging on event streams from a low-power neuromorphic event camera sensor to perform selective triggering of the more energy-hungry RGB camera for …

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Birdie: Natural Language-Driven Table Discovery Using Differentiable Search Index, Yuxiang Guo, Zhonghao Hu, Yuren Mao, Baihua Zheng, Yunjun Gao, Mingwei Zhou Mar 2025

Birdie: Natural Language-Driven Table Discovery Using Differentiable Search Index, Yuxiang Guo, Zhonghao Hu, Yuren Mao, Baihua Zheng, Yunjun Gao, Mingwei Zhou

Research Collection School Of Computing and Information Systems

Natural language (NL)-driven table discovery identifies relevant tables from large table repositories based on NL queries. While current deep-learning-based methods using the traditional dense vector search pipeline, i.e., representation-index-search, achieve remarkable accuracy, they face several limitations that impede further performance improvements: (i) the errors accumulated during the table representation and indexing phases affect the subsequent search accuracy; and (ii) insufficient query-table interaction hinders effective semantic alignment, impeding accuracy improvements. In this paper, we propose a novel framework Birdie, using a differentiate search index. It unifies the indexing and search into a single encoder-decoder language model, thus getting rid of error …

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Ragg: Retrieval-Augmented Grasp Generation Model, Zhenhua Tang, Bin Zhu, Yanbin Hao, Chong-Wah Ngo, Richang Hong Mar 2025

Ragg: Retrieval-Augmented Grasp Generation Model, Zhenhua Tang, Bin Zhu, Yanbin Hao, Chong-Wah Ngo, Richang Hong

Research Collection School Of Computing and Information Systems

Intent-based grasp generation inherently involves challenges such as manipulation ambiguity and modality gaps. To address these, we propose a novel Retrieval-Augmented Grasp Generation model (RAGG). Our key insight is that when humans manipulate new objects, they initially mimic the interaction patterns observed in similar objects, then progressively adjust hand-object contact. Consequently, we develop RAGG as a two-stage approach, encompassing retrieval-guided generation and structurally stable grasp refinement. In the first stage, we propose a Retrieval-Augmented Diffusion Model (ReDim), which identifies the most relevant interaction instance from a knowledge base to explicitly guide grasp generation, thereby mitigating ambiguity and bridging modality gaps …

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Aligning Large Language Models For Faithful Integrity Against Opposing Argument, Yong Zhao, Yang Deng, See-Kiong Ng, Tat-Seng Chua Mar 2025

Aligning Large Language Models For Faithful Integrity Against Opposing Argument, Yong Zhao, Yang Deng, See-Kiong Ng, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have demonstrated impressive capabilities in complex reasoning tasks. However, they can be easily misled by unfaithful arguments during conversations, even when their original statements are correct. To this end, we investigate the problem of maintaining faithful integrity in LLMs. This involves ensuring that LLMs adhere to their faithful statements in the face of opposing arguments and are able to correct their incorrect statements when presented with faithful arguments. In this work, we propose a novel framework, named Alignment for Faithful Integrity with Confidence Estimation (AFICE), which aims to align the LLM responses with faithful integrity. Specifically, …

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Offline Safe Reinforcement Learning Using Trajectory Classification, Ze Gong, Akshat Kumar, Pradeep Varakantham Mar 2025

Offline Safe Reinforcement Learning Using Trajectory Classification, Ze Gong, Akshat Kumar, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Offline safe reinforcement learning (RL) has emerged as a promising approach for learning safe behaviors without engaging in risky online interactions with the environment. Most existing methods in offline safe RL rely on cost constraints at each time step (derived from global cost constraints) and this can result in either overly conservative policies or violation of safety constraints. In this paper, we propose to learn a policy that generates desirable trajectories and avoids undesirable trajectories. To be specific, we first partition the pre-collected dataset of state-action trajectories into desirable and undesirable subsets. Intuitively, the desirable set contains high reward and …

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Personamagic: Stage-Regulated High-Fidelity Face Customization With Tandem Equilibrium, Xinzhe Li, Jiahui Zhan, Shengfeng He, Yangyang Xu, Junyu Dong, Huaidong Zhang, Yong Du Mar 2025

Personamagic: Stage-Regulated High-Fidelity Face Customization With Tandem Equilibrium, Xinzhe Li, Jiahui Zhan, Shengfeng He, Yangyang Xu, Junyu Dong, Huaidong Zhang, Yong Du

Research Collection School Of Computing and Information Systems

Personalized image generation has made significant strides in adapting content to novel concepts. However, a persistent challenge remains: balancing the accurate reconstruction of unseen concepts with the need for editability according to the prompt, especially when dealing with the complex nuances of facial features. In this study, we delve into the temporal dynamics of the text-to-image conditioning process, emphasizing the crucial role of stage partitioning in introducing new concepts. We present PersonaMagic, a stage-regulated generative technique designed for high-fidelity face customization. Using a simple MLP network, our method learns a series of embeddings within a specific timestep interval to capture …

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Unlocking The Potential Of Black-Box Pre-Trained Gnns For Graph Few-Shot Learning, Qiannan Zhang, Shichao Pei, Yuan Fang, Xiangliang Zhang Mar 2025

Unlocking The Potential Of Black-Box Pre-Trained Gnns For Graph Few-Shot Learning, Qiannan Zhang, Shichao Pei, Yuan Fang, Xiangliang Zhang

Research Collection School Of Computing and Information Systems

Few-shot learning has emerged as an important problem on graphs to combat label scarcity, which can be approached by current trends in pre-trained graph neural networks (GNNs) and meta-learning. Recent efforts integrate both paradigms in a white-box setting, leaving the more realistic black-box setting largely underexplored, where the parameters and gradients in the pre-trained GNNs are inaccessible. In this paper, we study the critical problem: Leveraging black-box pre-trained GNNs for graph few-shot learning. Despite its appeal, two key issues hinder the unlocking of its potential: the inherent task gap between pre-training and downstream stages, which can introduce irrelevant knowledge and …

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Retrieval Augmented Recipe Generation, Guoshan Liu, Hailong Yin, Bin Zhu, Jingjing Chen, Chong-Wah Ngo, Yu-Gang Jiang Mar 2025

Retrieval Augmented Recipe Generation, Guoshan Liu, Hailong Yin, Bin Zhu, Jingjing Chen, Chong-Wah Ngo, Yu-Gang Jiang

Research Collection School Of Computing and Information Systems

The growing interest in generating recipes from food images has drawn substantial research attention in recent years. Existing works for recipe generation primarily utilize a two-stage training method—first predicting ingredients from a food image and then generating instructions from both the image and ingredients. Large Multi-modal Models (LMMs), which have achieved notable success across a variety of vision and language tasks, shed light on generating both ingredients and instructions directly from images. Nevertheless, LMMs still face the common issue of hallu- cinations during recipe generation, leading to suboptimal performance. To tackle this issue, we propose a retrieval augmented large multimodal …

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Respear: Earable-Based Robust Respiratory Rate Monitoring, Yang Liu, Kayla-Jade Butkow, Jake Stuchbury-Wass, Adam Pullin, Dong Ma, Cecilia Masolo Mar 2025

Respear: Earable-Based Robust Respiratory Rate Monitoring, Yang Liu, Kayla-Jade Butkow, Jake Stuchbury-Wass, Adam Pullin, Dong Ma, Cecilia Masolo

Research Collection School Of Computing and Information Systems

Respiratory rate (RR) monitoring is integral to understanding physical and mental health and tracking fitness. Existing studies have demonstrated the feasibility of RR monitoring under specific user conditions (e.g., while remaining still, or while breathing heavily). Yet, performing accurate, continuous and non-obtrusive RR monitoring across diverse daily routines and activities remains challenging. In this work, we present RespEar, an earable-based system for robust RR monitoring. By leveraging the unique properties of in-ear microphones in earbuds, RespEar enables the use of Respiratory Sinus Arrhythmia (RSA) and Locomotor Respiratory Coupling (LRC), physiological couplings between cardiovascular activity, gait and respiration, to indirectly determine …

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Mimic: Ai And Ar-Enhanced Multi-Modal, Immersive, Relative Instruction Comprehension, Dhanuja Wanniarachchi, Archan Misra Mar 2025

Mimic: Ai And Ar-Enhanced Multi-Modal, Immersive, Relative Instruction Comprehension, Dhanuja Wanniarachchi, Archan Misra

Research Collection School Of Computing and Information Systems

We present a multimodal instruction comprehension framework, called MImIC, that utilizes visual sensing (including LIDAR and 2D RGB sensing) & AI spatial reasoning capabilities to support more seamless and immersive interaction between humans and AI-driven situated assistive agents. MImIC's key new capability is to support disambiguation of a wider set of relative spatial references that users naturally employ while issuing spatially-situated instructions. To support enhanced visual grounding via a combination of both fully-qualified and relative attribute references, MImIC uses (a) a fine-tuned transformer-based language translation DNN to accurately convert natural verbal commands into a structured set of machine understandable constraints …

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Explainable Neural Networks With Guarantee: A Sparse Estimation Approach, Antoine Ledent, Peng Liu Mar 2025

Explainable Neural Networks With Guarantee: A Sparse Estimation Approach, Antoine Ledent, Peng Liu

Research Collection School Of Computing and Information Systems

Balancing predictive power and interpretability has long been a challenging research area, particularly in powerful yet complex models like neural networks, where nonlinearity obstructs direct interpretation. This paper introduces a novel approach to constructing an explainable neural network that harmonizes predictiveness and explainability. Our model is designed as a linear combination of a sparse set of jointly learned features, each derived from a different trainable function applied to a single 1-dimensional input feature. Leveraging the ability to learn arbitrarily complex relationships, our neural network architecture enables automatic selection of a sparse set of important features, with the final prediction being …

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Late Cretaceous Ammonites And Other Non-Rudist Mollusks From Costa Rica And Their Paleoecology, Esteban David Lopez-Murillo Mar 2025

Late Cretaceous Ammonites And Other Non-Rudist Mollusks From Costa Rica And Their Paleoecology, Esteban David Lopez-Murillo

Earth and Planetary Sciences ETDs

The Nicoya region in northwestern Costa Rica contains a rich record of Upper Cretaceous fossiliferous sediments, which have received variable levels of study over the past several decades. In particular, rudists from these deposits have been well-characterized, however, the non-rudist ammonite, gastropod, and bivalve fauna have received much less attention. However, this area is particularly important because the Nicoya region was attached to the southernmost tip of North America, making it a unique crossroad between the western Tethys (Caribbean) and Pacific Oceans. Here I present a detailed investigation of these rocks and their fauna between the Late Campanian to Early …

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Evaluating The Physics-Based Slope Stability Program Pisa-M At A Regional Scale, Hudson J. Koch, William C. Haneberg, Matt M. Crawford, Jason M. Dortch, Meredith Swallom Mar 2025

Evaluating The Physics-Based Slope Stability Program Pisa-M At A Regional Scale, Hudson J. Koch, William C. Haneberg, Matt M. Crawford, Jason M. Dortch, Meredith Swallom

Report of Investigations--KGS

Physics based landslide modeling can be difficult and data intensive. Generating high quality and practical map results is often not feasible outside of small, thoroughly characterized study areas. Using the physics based program Probabilistic Infinite Slope Analysis (PISA m), users can perform expedient assessments of landslide hazard over large study areas where comprehensive geotechnical data may be lacking, but other data inputs are robust. PISA m uses an infinite slope equation and spatial layers, such as a digital elevation model (DEM), a lidar derived forest cover layer, and a soil map, to calculate the probability that the factor of safety …

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Generation Of Patient Specific Cardiac Chamber Models Using Generative Neural Networks Under A Bayesian Framework For Electroanatomical Mapping, Sunil Mathew, Jasbir Sra, Daniel B. Rowe Mar 2025

Generation Of Patient Specific Cardiac Chamber Models Using Generative Neural Networks Under A Bayesian Framework For Electroanatomical Mapping, Sunil Mathew, Jasbir Sra, Daniel B. Rowe

Mathematical and Statistical Science Faculty Research and Publications

Electroanatomical mapping is a technique used in cardiology to create a detailed 3D map of the electrical activity in the heart. It is useful for diagnosis, treatment planning and real time guidance in cardiac ablation procedures to treat arrhythmias like atrial fibrillation. A probabilistic machine learning model trained on a library of CT/MRI scans of the heart can be used during electroanatomical mapping to generate a patient-specific 3D model of the chamber being mapped. The use of probabilistic machine learning models under a Bayesian framework provides a way to quantify uncertainty in results and provide a natural framework of interpretability …

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Refining Chlorosulfonation Methods For The Synthesis Of A Perfluoroalkyl Arylsulfonimide (Pfsi) Monomer, Ainsley P. Foster, Ben Varney, Hua Mei Mar 2025

Refining Chlorosulfonation Methods For The Synthesis Of A Perfluoroalkyl Arylsulfonimide (Pfsi) Monomer, Ainsley P. Foster, Ben Varney, Hua Mei

SPARK Symposium Presentations

Proton-exchange membrane (PEM) fuel cells are sources of energy that are clean, quiet, and highly responsive to changes in power needs, making them promising for use in automobiles and other portable power devices. The electrolyte of a PEM cell–the layer responsible for conductivity–is a polymer membrane, commonly consisting of perfluoroalkyl sulfonic acid (PFSA) polymers. In place of the PFSA polymers, perfluoroalkyl arylsulfonimide (PFSI) polymers are expected to improve the efficiency of PEM fuel cells with better stability and proton conductivity. The trifluovinylether (TFVE) aryl perfluorosulfonamide monomer is a new PFSI monomer proposed to fulfill these benefits once polymerized. The previous …

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A. Overview Of The 1970 Flathead Lake Seismic Survey, Robert Lankston Mar 2025

A. Overview Of The 1970 Flathead Lake Seismic Survey, Robert Lankston

1970 Flathead Lake Seismic Survey

This book describes the evolution of the Wold-Friedel subbottom profiling system that was used during the 1970 reflection seismic survey at Flathead Lake. The book summarizes the key phases of acquisition and use of the reflection seismic data that were collected and describes the evolution of the physical and digital archives of that seismic project.

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