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How does the Center decide whom to interview?
The Williams Center chooses interviewees whose recollections best fit within one of our projects. If you know someone whose life experience fits within one of our projects, let us know about that person and why you think their stories would contribute to our oral history collection. To nominate someone for an interview, complete and return our bio data form. However, due to limited resources, a better alternative may be to interview the person yourself. We can provide guidance and training in oral history techniques so that you can interview people whose stories and memories deserve preservation. To learn more about our training, please visit Create an Oral History (https://lib.lsu.edu/oralhistory) Answered by: Jennifer Cramer
What is a "collection?"
A collection is an interview, and as such, it is assigned a specific collection number, such as 4700.1234. There can be several recordings (indicated by a unique Tape #) within one collection if the person was interviewed on more than one occasion by the same interviewer. A collection is an interview, and as such, it is assigned a specific collection number, such as 4700.1234. There can be several recordings (indicated by a unique Tape #) within one collection if the person was interviewed on more than one occasion by the same interviewer. Answered by: Jennifer Cramer
Who can access LSU Special Collections?
Everyone! You do not have to be affiliated with LSU to access our collections. We do suggest that you create an account so that you can begin requesting materials. Create a Special Collections Request System account at the link below. Create a Create a Special Collections Request System Account Everyone! You do not have to be affiliated with LSU to access our collections. We do suggest that you create an account so that you can begin requesting materials. Create a Special Collections Request System account at the link below. Answered by: Kelly Larson
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
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Composition and diversity of drift gillnet catches landed at PPI Cikidang Pangandaran
Cikidang Fishing Port is one of the fishing ports that plays an important role in capture fisheries in Pangandaran, with drift gillnet as the main fishing gear. The high use of this tool encourages the analysis of the diversity and composition of the catch to assess fishery productivity and maintain resource sustainability. This study aimed to analyze the composition and diversity of drift gillnet catches landed at the PPI Cikidang Pangandaran. The methods used include the calculation of production amount and value, Catch Per Unit Effort, percentage of catch composition, and diversity index. The data analyzed were obtained from the Cikidang PPI statistical report during the 2019-2021 period. The results showed that white pomfret (Pampus argenteus) was the most dominant species in the catch, with an average contribution of 26.4% per year, whereas yellow mackerel (Selaroides leptolepis) had the least catch (only 0.62% per year). The catch diversity index showed a value of 2.77 in 2019, 2.19 in 2020, and 2.21 in 2021, which falls into the medium category. Habitat conservation efforts and increased awareness of fishermen are also needed so that fishery resources in the Cikidang Fishing Port remain sustainable and productive in the long term.
Drivers of Economic Catch-up in Asia : A Study of ASEAN Countries with Comparative Views of China and India
This paper conducts a comprehensive examination of the sources of economic growth and catch-up of ASEAN countries since the Asian Financial Crisis, with comparative views of China and India. The study employs different decomposition frameworks to gain insights into the drivers of the Association’s economic performance over the 1997-2017 period. Three findings are most notable. First, all ten member states, except for Brunei, recorded a strong catch-up performance, with labour productivity being the leading driver in most countries. Second, the drivers of labour productivity catch-up exhibit some distinctive patterns among countries, which depend on the level of income and economic structure. Third, in all decomposition analyses, ASEAN countries are well below China and India across sources of growth, which tends to suggest that countries in the grouping could improve their performance by enhancing market integration and policy coordination. Although the long-term prospect of ASEAN is bright, the COVID-19 pandemic and the recent military coup in Myanmar have indicated that the road to future prosperity of the region is expected to face formidable challenges.
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.
Regulatory element in fibrin triggers tension-activated transition from catch to slip bonds
Fibrin formation and mechanical stability are essential in thrombosis and hemostasis. To reveal how mechanical load impacts fibrin, we carried out optical trap-based single-molecule forced unbinding experiments. The strength of noncovalent A:a knob-hole bond stabilizing fibrin polymers first increases with tensile force (catch bonds) and then decreases with force when the force exceeds a critical value (slip bonds). To provide the structural basis of catch–slip-bond behavior, we analyzed crystal structures and performed molecular modeling of A:a knob-hole complex. The movable flap (residues γ 295 to γ 305) containing the weak calcium-binding site γ 2 serves as a tension sensor. Flap dissociation from the B domain in the γ -nodule and translocation to knob ‘A’ triggers hole ‘a’ closure, resulting in the increase of binding affinity and prolonged bond lifetimes. The discovery of biphasic kinetics of knob-hole bond rupture is quantitatively explained by using a theory, formulated in terms of structural transitions in the binding pocket between the low-affinity (slip) and high-affinity (catch) states. We provide a general framework to understand the mechanical response of protein pairs capable of tension-induced remodeling of their association interface. Strengthening of the A:a knob-hole bonds at 30- to 40-pN forces might favor formation of nascent fibrin clots subject to hydrodynamic shear in vivo.
On Some Subclasses of Oriented Catch Digraphs
As an analog of intersection graphs among digraphs, catch digraphs were introduced by Hiroshi Maehara in 1984. Later on, Prisner focused his research on interval catch digraphs, which he identified as diasteroidal triple free digraphs. It has been used to solve a variety of real-world challenges, such as networking, telecommunication operations, traffic control, and location problems. Oriented catch digraphs are those catch digraphs that have exactly one edge direction attached to each edge of the corresponding underlying graph. In this article, we consider two important subclasses of oriented catch digraphs, namely, oriented interval catch digraphs and oriented circular-arc catch digraphs. First, we characterize those oriented interval catch digraphs whose underlying graphs are trees in terms of forbidden subdigraphs. Next, we provide a characterization of connected proper oriented interval catch digraphs whose underlying graphs are chordal, by determining the complete list of forbidden induced subdigraphs. Another interesting result is characterizing oriented circular-arc catch digraphs, which are tournaments.
Weak reflection enhancement and picking from ultrasonic pitch-catch measurements in a cased-hole
Ultrasonic logging is a widely used technology to assess the cement bond condition in a cased-hole. Advanced ultrasonic pitch-catch measurements can capture reflections at the cement-formation interface (in short, the third interface echo [TIE]), which enables the evaluation of the bond condition, casing eccentering, and the velocity of the material in the cement annulus. However, the usage is limited by the TIE arrival picking due to the low amplitude that results from the eccentered tool and casing, overlapping with the tool-casing multiple reflections, as well as interference from the primary zero-order antisymmetric Lamb wave (A0). We develop a two-step method to enhance and accurately pick the TIE arrivals. We first use mathematical morphological filtering with a neural network framework to mitigate the influence of A0. Then, we use the local window multichannel singular-spectrum analysis to suppress the effect of tool-casing multiples on the TIE. In the second step, we develop a sinusoidal search method with a path postcorrection algorithm to track the TIE arrivals from the enhanced TIE waveforms automatically. Synthetic and field data results show that our TIE processing can effectively eliminate the A0 mode and suppress tool-casing multiples. In addition, the picked TIE arrivals are consistent with actual TIE curves.
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