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Math Lab Rooms located in the Main Library in rooms 300X and 300Y
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Located at the first floor of the LSU Main Library.
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Processed vs unprocessed collection--what's the difference?
A processed collection has gone through several steps to become a cataloged record, thus available to the researching public. Those steps include a thorough vetting of copyright and restrictions, a verbatim transcription or thorough indexing of the interview including time-stamped calibration, the opportunity for the interviewee to review the transcription, the creation of a finding aid that includes important metadata about the collection, the preservation and optimization of audio files, the creation of user-copies, and cataloging. This process requires the efforts of several LSU Libraries staff members and it has been calculated that for every hour of recording, it takes 35-50 hours to fully process. For a detailed breakdown of the stages and fees associated with archiving oral histories, please see The Oral History Budget. All processed collections are found in the catalog record and many are available on the Louisiana Digital Library. An unprocessed collection is one that has not reached the final stage of completion and is not yet ready to be cataloged. Depending on the stage of processing, more or less of the interview will be available to patrons. See below for the availability of unprocessed collections. An unprocessed collection is not in the catalog record nor the Louisiana Digital Library. A processed collection has gone through several steps to become a cataloged record, thus available to the researching public. Those steps include a thorough vetting of copyright and restrictions, a verbatim transcription or thorough indexing of the interview including time-stamped calibration, the opportunity for the interviewee to review the transcription, the creation of a finding aid that includes important metadata about the collection, the preservation and optimization of audio files, the creation of user-copies, and cataloging. This process requires the efforts of several LSU Libraries staff members and it has been calculated that for every hour of recording, it takes 35-50 hours to fully process. For a detailed breakdown of the stages and fees associated with archiving oral histories, please see The Oral History Budget. All processed collections are found in the catalog record and many are available on the Louisiana Digital Library. An unprocessed collection is one that has not reached the final stage of completion and is not yet ready to be cataloged. Depending on the stage of processing, more or less of the interview will be available to patrons. See below for the availability of unprocessed collections. An unprocessed collection is not in the catalog record nor the Louisiana Digital Library. Answered by: Jennifer Cramer
What are Special Collections?
Special collections refer to unique materials that provide both primary and secondary sources to people conducting original research. Our collections are special due to their scarcity or rarity, historical value, monetary value, or research value. Archives are collections of original records created throughout the lifespan of a person, family, organization, or business. These materials essentially provide evidence of the activities, events, functions, and/or responsibilities of the creator(s). Archives and special collections differ from libraries in the types of materials collected and the ways in which they are acquired, organized, described, and made publicly accessible. These differences prompt us to create specific policies and procedures to ensure that our collections can continue to be used for decades or even centuries to come. Special collections refer to unique materials that provide both primary and secondary sources to people conducting original research. Our collections are special due to their scarcity or rarity, historical value, monetary value, or research value. Archives are collections of original records created throughout the lifespan of a person, family, organization, or business. These materials essentially provide evidence of the activities, events, functions, and/or responsibilities of the creator(s). Archives and special collections differ from libraries in the types of materials collected and the ways in which they are acquired, organized, described, and made publicly accessible. These differences prompt us to create specific policies and procedures to ensure that our collections can continue to be used for decades or even centuries to come. Answered by: Kelly Larson

Database Listing

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How to catch a ghost? Comparing two camera trap-based monitoring methods for elusive small mustelids in the Italian Alps
Small mustelids are increasingly recognized as species requiring conservation attention. In recent years, several camera-based methodologies have been developed to study them, but studies comparing different methods are still rare. To identify the most effective method to study small mustelid populations, we compared two camera-based monitoring methods in the Italian Alps. We also examined the effects of sampling session and habitat type on the occupancy probability and tested the “umbrella effect” of these methods for rodents. After superimposing a 700 × 700 m grid on an Alpine valley (Maritime Alps Natural Park, northwestern Italy), we surveyed 36 cells over three separate 45-day sessions from June to October 2023. In each cell, we employed (1) an “Alpine Mostela”, a foldable PVC box containing a camera trap and a PVC 9 cm Ø tube, and (2) a stand-alone trail camera. All devices were located at least 150 m from the others, and salmon oil was used as bait in half of the cells. To compare the methods, we used a single-season Bayesian occupancy model. The detection probability of stoats was higher with unbaited Alpine Mostelas and baited external cameras. We found the highest occupancy probability in the second session and non-forested habitats. Bait use positively affected the number of non-target videos. In this study, unbaited Alpine Mostelas and baited external cameras demonstrated reliable performance in detecting stoats. However, with the Alpine Mostela accomplishing slightly better results with much fewer non-target videos, it emerged as the preferred choice for long-term stoat monitoring.
Weaving a web to catch them all: inclusive pedagogies in mathematics
Inclusion is an area of strategic importance in Aotearoa New Zealand and internationally, requiring that all learners have access to quality education. Nonetheless, children with disabilities in our schools are often excluded from rich learning opportunities, particularly in mathematics. Teaching through inclusive pedagogies, by contrast, supports teachers to plan for and teach all children; however, we know little about how teachers may apply these approaches to their teaching of mathematics. In this article, we report on a case study of one teacher’s practice as we develop a ‘research lesson’ approach that enhances the engagement and learning of all children, including those who are most at risk of being excluded in mathematics lessons. Participants described challenges to inclusive teaching mathematics such as differentiation, engaging contexts, children’s experience of anxiety, and children’s comfort with oral language and discussion. We found that research lesson reflections enabled solutions to be found for these challenges as part of the process of identifying them. We argue that an approach focussed on inclusion is a tool with potential to help teachers to rethink their mathematics classroom organisation and planning. Inclusive teaching may be enacted differently depending on the context; however, we found the key teaching practice to be planning for inclusion at the outset, followed by focussed reflections on teaching. Further, we suggest that research into innovative mathematics pedagogies is most likely to be successful when there is a focus on inclusive pedagogy.
Catch before they fall: a pose-guided attention framework for indoor safety
Falls can pose a serious health threat, especially for older people, often leading to fractures, head injuries, or long-term disability. This highlights the need for reliable and non-invasive detection systems. Existing solutions often suffer from limitations such as user discomfort with wearables, constrained coverage of fixed sensors, or environmental challenges in vision-based methods. This paper proposes a dual-stream attention-guided robust indoor fall detection framework to solve the problem. The approach combines a pose-guided stream with a video stream, in which the former captures high-level skeletal features through MediaPipe and simultaneously processes spatiotemporal dynamics from raw RGB frames using a 3D Convolutional Neural Network (CNN) with a temporal attention module. In order to enhance classification precision, features from each stream are adaptively fused in a block based on attention mechanisms, which improves the model’s interpretation of posture and movement semantics. Testing on the KFall dataset reveals that the proposed method achieves 98.71% accuracy, surpassing pre-existing benchmarks. Triggering a visual alert by displaying a red rectangle on the screen upon the occurrence of this event is a subsequent outcome of this work. An ablation study highlights the effectiveness of each component. Finally, the proposed work advances fall detection by combining pose estimation and attention-based deep learning to deliver an accurate, interpretable, and deployable solution.
Results of trawl counts for juvenile pink salmon in the Bering and Okhotsk Seas in 2024 and prospects for the returns and catch in the Karaginsky subzone and Okhotsk Sea in 2025
The key stage of juvenile pink salmon monitoring is the survey of their fall feeding in the sea that detects the year-class strength. The results of this survey are used for forecasting of the pink salmon returns to the Karaginsky fishing subzone in Bering Sea and to the Okhotsk Sea and the landing in the next year. The trawl surveys with two research vessels have conducted in recent years that allows to cover vast areas in a short time and to exclude repeated counts of the same fish on neighbor transects. In 2024, such trawl survey for pink salmon counting was conducted in the western Bering Sea and Okhotsk Sea that provided representative data on the juveniles abundance used for forecasting their returns and catches in the Karaginsky fishing subzone and Okhotsk Sea. The abundance of pink salmon in the western Bering Sea was estimated in 452∙106 ind. that was about a half of their numbers in previous even years (2018, 2020, 2022). Their return to the Karaginsky fishing subzone in the next year is expected as 72∙106 ind. at the lower limit of confidence interval, that provides the catch of 49∙103 t with the expected weight of spawners about 1 kg and 32 % escapement to the spawning grounds. In the Okhotsk Sea, the abundance of pink salmon was estimated in 1077∙106 ind. that was lower than in 2020 and 2022. Their expected return to the Okhotsk Sea in the next year is expected as 123∙106 ind. at the lower limit of confidence interval, that provides the catch of 100∙103 t with the weight of 1.3 kg and escapement of 35 %. Abundance in «northern» and «southern» regional complexes of local stocks is estimated for pink salmon in the Okhotsk Sea using cluster analysis with the expectation-maximization algorithm (EM clustering); the «northern» group prevailed with the ratio 64:36 %.
Assessing the indicated impact of cantrang (boat Danish seine) based on catch characteristics in Java Sea, Indonesia
Cantrang (boat Danish seine) has been illegal since 2015 but remains prevalent in Indonesia’s Java Sea. Despite known negative impacts, no comprehensive ecological assessment of cantrang fishing exists. This study evaluates its effects by analyzing catch data based on taxa, trophic level, habitat, and fishing vulnerability by a multivariate approach. In this study, the size of 60 cantrang vessel samples were grouped into 4, namely 20–30, 31–50, 51–100, and 101–200 gross tons (GT), representing the spatial distribution of the fishing grounds. Larger vessels catch more diverse and abundant fish, primarily reef-associated and demersal species groups. There was a significant difference in the fishing vessel’s size on the catch’s composition (analysis of similarities, ANOSIM R = 0.114, p = 0.024). The dominant catches were families of Loliginidae (Loligo sp., 24.38%) and Nemipteridae (Nemipterus nematophorus, 19.29%), trophic level 2.7 (34.41–43.18%), reef-associated and demersal fish (37.06–46.09%), and low vulnerability group of fish (58.01–64.56%). Additionally, 2.69–8.56% of the endangered, threatened, and protected species of wedgefish (Rhyncobatus sp.) were also caught by the cantrang. This study confirms the impacts of cantrang on fish resources in the Java Sea, Indonesia’s Fisheries Management Area 712. The findings emphasize the need to improve management strategies to achieve sustainable fish resources and marine biodiversity in the region.
Catch fish optimization algorithm: a new human behavior algorithm for solving clustering problems
This paper is inspired by traditional rural fishing methods and proposes a new metaheuristic optimization algorithm based on human behavior: Catch Fish Optimization Algorithm (CFOA). This algorithm simulates the process of rural fishermen fishing in ponds, which is mainly divided into two phases: the exploration phase and the exploitation phase. In the exploration phase, there are two stages to search: first, the individual capture stage based on personal experience and intuition, and second, the group capture stage based on human proficiency in using tools and collaboration. Transition from independent search to group capture during the exploration phase. Exploitation phase: All fishermen will surround the shoal of fish and work together to salvage the remaining fish, a collective capture strategy. CFOA model is based on these two phases. This paper tested the optimization performance of CFOA using IEEE CEC 2014 and IEEE CEC 2020 test functions, and compared it with 11 other optimization algorithms. We employed the IEEE CEC2017 function to evaluate the overall performance of CFOA. The experimental results indicate that CFOA exhibits excellent and stable optimization capabilities overall. Additionally, we applied CFOA to data clustering problems, and the final results demonstrate that CFOA’s overall error rate in processing clustering problems is less than 20%, resulting in a better clustering effect. The comprehensive experimental results show that CFOA exhibits excellent optimization effects when facing different optimization problems. CFOA code is open at https://github.com/Meky-1210/CFOA.git.