ScholarWorks

ScholarWorks is an open access repository for the capture of the intellectual work of Montana State University (MSU) in support of its teaching, research and service missions. MSU ScholarWorks is a central point of discovery for accessing, collecting, sharing, preserving, and distributing knowledge to the Montana State University community and the world.

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Scholarship & Research

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Viewfinder Case Study: Implementing an AI Platform for Local Governments
(Montana State University, 2026-11)
Peña, Angelica
This case study examines the mid-implementation phase of Madison AI within the City of San Leandro, with particular attention to its impact on the San Leandro Public Library as a City department. Madison AI is a web-based AI platform designed for local governments. Using Madison, municipalities upload public records to a secure, dedicated system, where they are used to generate responses and support staff tasks. Because this tool was selected at the City level and is designed primarily for administrative work, its usefulness in a public library setting is limited. Viewfinder provided a clear method for evaluating how the project aligns with library values, particularly around privacy and consent. The reflection also informed clearer communication with IT and highlighted considerations for future AI projects that may affect the library.
Deep Learning Predictions of SST at the Atlantis II Seamounts with Links to Global Teleconnections
(American Meteorological Society, 2025-05)
Cullen, Kerstin E.
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Reynolds, Carolyn A.
;
Rydbeck, Adam
Located in the western North Atlantic, the subsurface temperature and salinity structure at the Atlantis II Seamounts is sensitive to fluctuations in the Gulf Stream, and historical ocean structure profiles at this location show a bimodal distribution, making seasonal climatologies a poor predictor of ocean states. Historical CMIP6 ensemble means show dynamic changes in temperature between 1980 and 2007 (0.5°C), with no corresponding change in sea surface height anomalies, suggestive of decadal changes independent of the Gulf Stream position. While previous deep learning models have been able to predict SST globally, the Gulf Stream region has some of the lowest accuracy and shortest forecast horizons. We use a deep learning (convolutional neural network) model to predict sea surface height anomalies and sea surface temperature for 1 month to 2 years, focusing on the Atlantis II Seamounts. The training dataset consists of the global 1850–2007 CMIP6 temperature, height, and mixed layer depth that capture climate modes of variability that impact the Gulf Stream. We test our model predictions with Ocean Reanalysis System 5 (ORAS5) reanalysis data. Our model can beat the climatology for up to 24 months by predicting different decadal regimes in the Gulf Stream. Using back-propagation saliency maps, we connect Atlantis II Seamounts predictions to multiple global modes of climate variability, such as the North Atlantic Oscillation, the Indian Ocean dipole, the Pacific decadal oscillation, and ENSO.
Mode vector modulation: receiver architectures and DSP algorithms for polarization tracking
(Optica Publishing Group, 2025-10)
Roudas, Ioannis
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Biswas, Aishik
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Brisson, Alexander
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Dadras, Sina
The emergence of modern hyperscale datacenters and artificial general intelligence (AGI) supercomputers creates an unprecedented demand for high-speed, scalable optical interconnects. Contemporary datacenter optical networks rely on pluggable transponders using M-ary pulse amplitude modulation (M-PAM) and direct detection (DD). To scale capacity, the prevailing strategy is to use parallel fiber strands that carry independent M-PAM/DD tributaries, a form of space-division multiplexing (SDM). Meanwhile, multicore fibers (MCFs) with nominally uncoupled, homogeneous, single-mode cores are intended to replace fiber strands in the future to reduce fiber count and simplify data center connectivity. Rather than treating each MCF core as an independent lightpath, we can exploit all MCF spatial and polarization degrees of freedom jointly to transmit multidimensional symbols via space modulation—a format we term mode vector modulation (MVM). This paper presents several novel MVM DD receiver architectures, each offering a different balance between hardware complexity and performance. In parallel, we introduce and evaluate a suite of digital signal processing (DSP) algorithms for polarization tracking, including both blind and pilot-assisted methods. Together, these innovations enhance the practicality of MVM as a high-sensitivity, bandwidth-efficient alternative to M-PAM for future direct-detection-based optical interconnects.
The Choice among Sales Mechanisms: Auction v. Negotiated Sales of Private Timber
(Springer Science and Business Media LLC, 2025-05)
Rucker, Randal R.
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Leffler, Keith B.
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Munn, Ian A.
For many non-industrial private forest (NIPF) landowners, the standing timber on their property is an important component of their investment portfolios. The value of their timber is determined not only by the biological and physical attributes of their timber tracts, but also by the transaction costs associated with different contracting practices, pricing schemes, and sales mechanisms. In this paper, we examine the determinants of the choice by landowners between the two predominant sales mechanisms for selling standing timber—auctions and negotiations. Our examination provides industry participants with a framework for thinking about the benefits and costs of the choice between these two alternatives. Further, it provides them with information on empirical relationships among sales mechanisms and their determinants. We also discuss potential implications of our results for the USDA Forest Service (Forest Service), which is required to sell almost all of its tracts through auctions.
A remote sensing and GIS-based analysis of forest cover changes and carbon sequestration trends over three decades
(Elsevier BV, 2025-05)
Raza, Ali
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Rubab Syed, Neyha
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Fahmeed, Romana
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Acharki, Siham
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Hussain, Sajjad
Reducing deforestation is essential to mitigate greenhouse gas (GHG) emissions and protect vulnerable forest ecosystems. Afforestation initiatives have significantly reduced deforestation effect in Pakistan through large-scale plantation efforts. This study uses Landsat imagery to determine land use land cover changes (LULCC) for assessing carbon changes from which emission and sequestration can be deduced in the Nasirabad (district of Balochistan), Pakistan. For this purpose, Landsat imagery from 1993, 2003, 2013, and 2023 years were processed using geographic information system (GIS) software and classified based on maximum likelihood classification (MLC) algorithm to prepare land use land cover (LULC) maps. The LULCC analysis revealed a significant decline (21.9 %) in forest area from 1993 to 2003, followed by a notable recovery (2003–2013: 24.53 %, 2013–2023: 6.33 %) over the two decades. Under government led afforestation-initiatives, forests covered approximately 994.13 km2, indicating a 30.85 % increase over twenty years. The rise in forest cover due to these initiatives has increased carbon storage from 2003 to 2023. However, forest area decreased by 705.85 km2 between 1993 and 2003, resulting in total annual emissions (TAE) of approximately, 117612.13 tons per year. This study provides critical data to promote afforestation and forest conservation, aiding climate change mitigation.