Digital Commons Network^™

The Rarest Alcid: Status And History Of The Guadalupe Murrelet Synthliboramphus Hypoleucus At Isla Guadalupe, Mexico (1892-2007), Darrell L. Whitworth, Harry R. Carter, Eduardo Palacios, Josh S. Koepke, William R. Mciver, Christine D. Hamilton, Gerard J. Mcchesney, Franklin Gress

Marine Ornithology

The Guadalupe Murrelet (GUMU) Synthliboramphus hypoleucus is the rarest alcid in the world (< 5000 pairs), with regular breeding documented only at Islas Guadalupe (hereafter, “Guadalupe”) and San Benito off the west-central coast of Baja California, Mexico. GUMU were discovered at Guadalupe in 1892, but by then feral cats Felis catus had already devastated the population and limited most nesting to predator-free islets just offshore. Despite its status as the only major GUMU breeding island, the population at Guadalupe was never adequately surveyed during the 20th century. In March–April 2007, we used survey techniques developed specifically for Synthliboramphus murrelets to assess the status and distribution of GUMU at Guadalupe. We counted 1511 GUMU during spotlight surveys in waters around the island. GUMU were abundant near the islets, but they were also relatively numerous near suitable breeding habitats off the north and south coasts of Guadalupe proper. Searches on four islets yielded 93 nests, most at Islotes Zapato (66; 71%) and Morro Prieto (21; 23%), where scores of abandoned eggs indicated intense competition for nests. We found seven nests on Guadalupe proper, the first proof of breeding there since 1950, but we also found 68 carcasses that confirmed significant predation by cats and raptors. A spotlight survey correction factor yielded 2418 breeding pairs (95% confidence interval = 1662–4367) at Guadalupe and its islets, which accounted for ~90% of the world breeding population (1822–4789 pairs). About 1150–1750 pairs were estimated on Islotes Zapato and Morro Prieto in 1968, but comparable data were lacking to assess historical or recent changes in population size. Basic studies of GUMU biology have only recently been initiated and should be a continuing priority. Recent conservation actions have benefited the native biota of Guadalupe, but further measures, such as localized rodent control and robust biosecurity on the islets, should be considered to mitigate serious threats to the vulnerable GUMU population and other seabirds.

Levels Of Predation At Two Leach's Storm Petrel Hydrobates Leucorhous Breeding Colonies, Rielle Hoeg, Dave Shutler, Ingrid L. Pollet

Marine Ornithology

Seabirds are declining worldwide, and predation by introduced and endemic species is one threat that affects many of them. Leach’s Storm Petrel Hydrobates leucorhous (hereafter LHSP) populations are declining in a number of locations for reasons that are likely multicausal, and perhaps local or regional, but relative contributions of individual causes are poorly understood. To help understand causes, we quantified predation at two Nova Scotian LHSP breeding colonies that differ in predator management: no management occurs on Bon Portage (BP) Island, whereas management is used on Country Island (CI). Transects were monitored for evidence of predation approximately biweekly on BP …

Estimating Streaked Shearwater Calonectris Leucomelas Abundance In The Republic Of Korea Using Automated Acoustic Recorders, Kirk A. Hart, Steffen Oppel, Grant R.W. Humphries, Andrew Blackburn, Ki-Baek Nam

Marine Ornithology

Traditional methods to assess population size in seabirds are applicable only to species that nest in visible locations, leaving cryptic nocturnal, burrow-nesting seabirds underrepresented in typical long-term population monitoring programs. Alternative methods to count burrow-nesting birds, however, are extremely labor-intensive and therefore are applicable only to small and possibly unrepresentative areas, and may have negative effects on burrow-nesting populations. We suggest the use of automatic acoustic recorders as a possible survey technique to overcome logistic difficulties of research of seabirds on remote islands in the Republic of Korea, where funding for ecological research is extremely limited but where globally important …

Motionfusion: A Robust Ensemble Learning Framework For Accurate Sensor-Based Human Activity Recognition, Hussein K. Almulla, Hussam J. Mohammed, Alaa S. Al-Waisy, Shumoos Al-Fahdawi, Ahmed Adnan Had, Bourair Al-Attar

Iraqi Journal for Computer Science and Mathematics

Human activity Recognition (HAR) has emerged as an important research area due to its potential applications in health, sport, and recreation. The widespread availability of smartphone sensors has facilitated data collection for HAR systems. Although machine learning and deep learning models have proven to be effective in detecting human activity from sensor data, their performance may be limited, this study proposes MotionFusion which is an ensemble learning model to increase HAR accuracy utilizing accelerometer and gyroscope data from a smartphone. By combining Histogram-Based Gradient Boosting, Random Forest, and Extra Trees models with a Support Vector Machine classifier and using feature …

Underwater Behavior Of Christmas Shearwaters Puffinus Nativitatis From Kure Atoll, Ilana Nimz, Mark A. Renshaw, Matthew Iacchei, Eric Vanderwerf, Cynthia Vanderlip, Matthew Saunter, Naomi Worcester, K. David Hyrenbach

Marine Ornithology

The Christmas Shearwater Puffinus nativitatis is an aquatic, tropical shearwater. While individuals of this species are known to forage by pursuit plunging and diving, there are no published reports quantifying their sub-surface foraging behavior. We obtained diving information from eight chick-rearing Christmas Shearwaters on Kure Atoll during June–August 2017. Over the 33 days during which instruments were deployed, 1521 dives were recorded. Individual deployments lasted three to eight days and were independent of the maximum depths recorded. Individual maximum depths were 10.7–24.1 m, averaging 15.4 m. While the deepest and longest recorded dive reached 24.1 m and lasted 31 s, …

Stable Isotope Analysis Of Multiple Tissues From Chick Carcasses Of Three Pygoscelid Penguins In Antarctica, Yanina A. Ciriani, Mariana A. Juáres, M. Mercedes Santos, Steven D. Emslie

Marine Ornithology

Many types of animal tissues are increasingly being used for stable isotope analysis, with their application dependent on the time frame they reflect and their availability for collection. Here, we investigated the isotopic values (δ13C and δ15N) of four tissues (feather, skin, toenail, and bone) collected from fledgling-period chick carcasses of three species of pygoscelid penguins to compare the variability and accuracy of the data among tissues. Samples were collected at 25 de Mayo/King George Island during the 2017/18 austral summer. Chick carcasses are commonly found at active penguin colonies, and “opportunistic sampling” can easily be performed without disturbing nesting …

Number And Distribution Records Of Caspian Tern Hydroprogne Caspia In Indonesia, With Special Reference To Sumatra, Muhammad Iqbal, Yus Rusila Noor, Deni Mulyana, Henni Martini, Ferry Hasudungan, Iwan Febrianto, Fransisca Noni Tirtaningtyas, Arum Setiawan, Indra Yustian, Hilda Zulkifli

Marine Ornithology

We summarize all reports of Caspian Tern Hydroprogne caspia recorded in Indonesia, with particular focus on Sumatra (1983–2019). There were 29 records in total, which together indicate that the Caspian Tern is a regular visitor to Indonesia, in contrast to previous assumptions that it is rare. This species has been recorded during the non-breeding period (September to March in Indonesia, and August to April in Sumatra) in small numbers ranging from one to 40 birds. Many potential sites for this species, particularly along east coast of Sumatra, have still not been well explored. Additional search efforts will advance our knowledge …

Trends In Density, Abundance, And Response To Storm Damage For Westland Petrels Procellaria Westlandica, 2007-2019, Susan M. Waugh, Christophe Barbraud, Karine Delord, Kate L.J. Simister, G. Barry Baker, Georgie K. Hedley, Kerry-Jayne Wilson, Douglas R.D. Rands

Marine Ornithology

The density and distribution of Westland Petrel burrows was assessed over a 12-year period (2007–2019). During that time, burrow density increased while occupancy remained stable, commensurate with an annual population growth rate of 1.022 (95% confidence interval: 0.971–1.076), as estimated using mark recapture data. From our surveys, we estimated a 2019 baseline population of ~6200 breeding pairs and a world population of 13 800–17600 individuals, covering around 95% of the population. Transects were conducted to establish the location and density of 17 petrel sub-colonies in rugged, untracked terrain in Paparoa National Park, West Coast, New Zealand. Major storms in 2014–2018 …

Wedge-Tailed Shearwater Ardenna Pacifica Fallout Patterns Inform Targeted Management, Brooke Friswold, Keith Swindle, David Hyrenbach, Melissa R. Price

Marine Ornithology

Seabird fledglings are often attracted to artificial, bright lights, leading to their grounding. This phenomenon is termed “fallout” and is associated with an increased risk of mortality from land-based threats. This study evaluated temporal trends and spatial factors, such as fallout clustering near lights and proximity to colonies, to inform targeted management actions. Standardized surveys were conducted from 2002 to 2010 for Wedge-tailed Shearwater Ardenna pacifica (WTSH) fallout on the island of O‘ahu, Hawai‘i, USA. First, yearly fallout counts along the transect showed a two-year cycle and identified 25 November as the date with the highest fallout across years. Second, …

Seabird Migration At Cabo Carvoeiro (Peniche, Portugal) In Autumn 2015, Johan Elmberg, Erik Hirschfeld, Helder Cardoso, Rebecca Hessel

Marine Ornithology

Land-based counts at Cabo Carvoeiro (Peniche, Portugal), made between 15 August and 15 November 2015 (effort 90 d, 517 h), tallied 302469 birds, most of which (99.98%) were southbound. Although 65 species were observed, four species contributed to 91% of the total: 207608 Northern Gannet Morus bassanus, 32281 Cory’s Shearwater Calonectris borealis, 16086 Manx Shearwater Puffinus puffinus, and 15222 Balearic Shearwater Puffinus mauretanicus. Passage as a whole increased throughout the period, mainly due to a gradual intensification of Northern Gannet migration. More than 67 000 southbound procellariforms of 12 species were recorded, as were 6183 Stercorariidae of four species. Daily …

Population Status Of The Kermadec Petrel (Pterodroma Neglecta Juana) At San Ambrosio Island, Chile, Manuel Marín, Rodrigo González, Sergio Trucco

Marine Ornithology

We visited San Ambrosio Island off northern Chile (26°21′S, 079°47′W) on 10–12 December 2019. We found a large population of Kermadec Petrel Pterodroma neglecta juana that appeared to be healthy. In the area that we could survey, we estimated at least 22686 breeding pairs, which is an underestimate of the entire population as we could not access some steep areas. Introduced mammals, which have since been eradicated, caused a loss of island vegetation that might be a drawback for this population, as shading from the sun appears to be important for nest-site selection. Egg-laying started by early December. We assessed …

Foraging Ranges Of Humboldt Penguins Spheniscus Humboldti From Tilgo Island: The Critical Need For Protecting A Unique Marine Habitat, René Quispe, Miriam Lerma, Nicolás Luna, Matías Portflitt-Toro, Juan Serratosa, Guillermo Luna-Jorquera

Marine Ornithology

The largest population of Humboldt Penguins resides in a fertile archipelago of the north-central coast of Chile, formed by eight islands in proximity to upwelling centers of the Humboldt Current System. However, five of these islands lack legal protection. Here, we report the results of breeding Humboldt Penguins tracked while foraging from Tilgo Island. The average and maximum foraging radii around the colony were 22 km and 43 km, respectively. Our data indicate that trip ranges overlap areas proposed for industrial projects. Because Humboldt Penguins are sentinels of local ecosystem health, this underscores the value of expanding conservation zones in …

Records Of White Tern Gygis Alba In Panama And Potential Connections To El Niño Events, Daniel Buitrago-Rosas, Jorge L. Medina, Pedro L. Castillo-Caballero

Marine Ornithology

In Panama, the White Tern Gygis alba is a vagrant seabird that has been reported very rarely. Its first record was in 1983, during a very strong El Niño Southern Oscillation (ENSO) event that generated a massive invasion of unusual seabirds from South America into the Panama Bay. Unlike other vagrant seabirds, the White Tern remained unrecorded again until 2016. This note reports the second, and first confirmed, occurrence of White Tern to Panama, along with separate confirmed sightings in 2019. We briefly review these records and their potential connections to ENSO events.

Retracted: A Quantum Convolutional Neural Network Approach For Early And Accurate Diagnosis Of Parkinson's Disease, Aiesha Mahmoud Ibrahim, Mazin Abed Mohammed, Omar Al-Boridi

Iraqi Journal for Computer Science and Mathematics

Parkinson's disease (PD) is a progressive neurological disorder that primarily affects individuals over the age of 55. It is characterized by a range of motor and non-motor symptoms that can significantly impact various aspects of daily life. Despite notable advancements in medical science, there is currently no permanent cure or definitive treatment for PD. This therapeutic gap underscores the critical importance of early diagnosis, which remains a major focus of ongoing research. Due to the disease's gradual progression, PD symptoms may take years to fully develop, making early detection essential for improving patient outcomes and quality of life. Moreover, the …

The Future Of Intelligent Industrial Systems: Plc, Node-Red, And Iot/Iiot, Firas Ahmed Hussein, Mohammad Tariq Yaseen, Mohammed Obaid Mustafa

AUIQ Technical Engineering Science

Integrating Programmable Logic Controllers (PLCs) with Node-RED and IoT/IIoT has emerged as a transformative technique for developing intelligent industrial systems as industrial automation improves. This narrative review brings together more than 70 research sources on industrial PLC, Node-RED, IoT, and IIoT applications. Case studies, experimental implementations, and industry reports are used to discover trends, challenges, and opportunities. The review focuses on three main topics: PLCs in modern industrial systems and their evolution and integration with IoT/IIoT; Node-RED as a middleware for industrial automation and its ability to connect PLCs to cloud and edge computing; and IIoT and smart manufacturing and …

Using Images From A Simulated 3d Environment For Llm Fine-Tuning For Image And Video Understanding Tasks, Ágoston Weisz

Defensive Publications Series

Large language models (LLMs) are capable of answering questions with reference to images or videos. However, LLMs sometimes get spatial relationships wrong. A challenge for training LLMs for 3D scene understanding is the lack of suitably large image and video datasets. This disclosure describes the use of a simulated three-dimensional (3D) environment to obtain two-dimensional (2D) images or videos of the environment from different perspectives. The point is moved in the simulated 3D space and a stream of 2D images is obtained from the different positions. The simulated 3D environment can be used to model various scenarios where the LLM …

Real-Time Context-Aware Assistance For Non-Native Language Speakers, Jaime Guajardo, Sriratana (Daowz) Sutasirisap

Defensive Publications Series

When two or more non-native language speakers or learners converse, they may often encounter words or grammatical structures they do not understand. In the absence of a native speaker, learners tend to pause their conversation to look up unfamiliar words in a dictionary, causing disruption to their conversation flow. This disclosure describes techniques that assist non-native language speakers or learners during real-time conversations without disrupting conversational flow. With user permission, an ongoing conversation is analyzed in real time by a large language model (LLM) based language assistant to identify non-idiomatic speech and grammar, vocabulary, and/or pronunciation errors. When a user …

Detecting Android Malware Based On Static Analysis Using Classification And Modified Clustering Techniques, Abdullah Allawi Al-Sraratee, Ahmed Habeeb Al-Azawei

Journal of Intelligent Informatics, Networking, and Cybersecurity

Because Android malware harms internet security, prior research proposes several different approaches to detect it accurately. However, such proposed models depend on numerous number of features to attain high accuracy. This could lead to high computation cost and potential overfitting. Furthermore, manual data labeling is labor-intensive, requiring significant human effort and skills. This research aims to: 1) extend previous literature on Android malware detection, 2) improve the accuracy of Android malware detection based on a low number of features, and 3) modify a clustering technique to group data into two different clusters to address the issue of unlabeled data. To …

An Exploration Of Response Bias And Their Impact On Self-Report Psychopathology: The Mmpi-A Response Bias Measures, Ashley Messinger

ETD Collection

No abstract provided.

Guinea: A Mining Boom In Defiance Of Standards, Foromo Frédéric Loua

Maryland Journal of International Law

No abstract provided.

Real-Time Fraud Detection Using Big Data, Abdulla Matar Alketbi

Theses

In today’s digital world, fraud detection has become an important part of financial security. This study explores and compares the performance of different machine learning models in identifying fraudulent transactions using the IEEE-CIS Fraud Detection dataset. Techniques such as Random Forest, Gradient Boosting, Deep Neural Networks, and Logistic Regression were evaluated. The dataset was pre-processed using SMOTE to balance the classes and improve model sensitivity to fraud cases. Each performance of the model was assessed using accuracy, precision, recall, and F1-score. The Random Forest model achieved the highest overall performance with an F1-score of 99.23

John Fakla V. Matthew Geist

2025 Decisions

USDC for the District of New Jersey

Recent Advances In Modelling Of Frost Formation For Mechanical Systems, Yong Tao

Mechanical Engineering Faculty Publications

The physics underlying frost and ice formation has been extensively studied over the past few decades, with significant contributions to our understanding of this phenomenon. These insights have primarily been applied to engineering systems with refrigerated surfaces, such as refrigerators, freezers and heat pumps of various sizes. Despite considerable progress, the dynamic and complex mechanisms governing frost and ice formation remain an active area of research, as competing factors continue to challenge predictive accuracy. The increasing interest from stakeholders in reducing energy consumption and carbon footprints in mechanical systems further underscores the importance of advancing modelling and simulation capabilities in …

Investigating The Capacity Of Tdp-43 Low Complexity Domain To Target Protein Aggregates In Yeast, Nicholas J. Grahame

Biology Summer Fellows

Utilizing protein TDP-43 and fluorescent proteins to:

Investigate TDP-43’s LCD region in protein aggregates. Use fluorescent protein tagging to visualize co-localization and aggregation. Leverage yeast as a model system for studying LLPS and aggregation.

Can we target chaperone proteins to TDP-43 LCD region?

A Predictive Framework For Early Detection And Personalised Monitoring Of Parkinson’S Disease Using Artificial Intelligence And Large Language Models, Priyadharshini S

Theses and Dissertations

Parkinson’s Disease (PD) is a multifaceted and progressive neurodegenerative disorder that presents a spectrum of motor and non-motor symptoms. Early and accurate diagnosis is essential for effective disease management and improved patient outcomes, yet remains clinically challenging due to symptom overlap and diagnostic limitations. This thesis proposes a comprehensive and interpretable artificial intelligence (AI)-driven diagnostic framework that aims to transform the early detection, personalised monitoring, and treatment recommendation process for PD. The proposed solution integrates deep learning, radiomics, evolutionary optimisation, and large language models (LLMs), ensuring a highly accurate and clinically adaptable system.

The research begins by analysing T2-weighted 3D …

Positive Social Emotional Influences Experienced By College-Educated Black Men During Their K–12 Education In U.S. Public Schools, Lyzanethia Howard

Walden Dissertations and Doctoral Studies

Although negative experiences and unfair factors in the K–12 educational experiences of Black males are well-researched, very little research is found on the positive contributions toward their educational success. As some Black males successfully complete K–12 schooling and advance to the college level, it is imperative to understand the factors that positively contributed to their educational advancement. Guided by Yosso’s theory of community cultural wealth, this basic qualitative study explored six aspects of cultural capital—the aspirational, familial, linguistic, resistant, navigational, and social capital—that contributed to the educational success and advancement of Black males during their journeys through the U.S. K–12 …

Governmental Suppression Of Speech On The Internet: Reflections On The Murthy Decision, Russell L. Weaver

Louisiana Law Review

No abstract provided.

Bondholders Motion To Dismiss

Catholic Dioceses in Bankruptcy

No abstract provided.

Limitations Of Using Large Language Models For Automated Essay Scoring, Thomas A. Fink

Theses

Background: Automated essay scoring (AES) is a challenging deep learning problem. The two most widely used methods for predicting essay quality scores, supervised learning-based and LLM-based, have their own limitations. Although supervised learning-based methods are more accurate, they only predict a score and do not offer descriptive feedback to students. On the other hand, LLM-based methods can offer rubric-guided feedback but are known to be less accurate.

Methods: This work focuses on improving the accuracy of state-of-the-art LLM-based AES methods. We began by thoroughly investigating why these methods were performing poorly for certain datasets and certain examples. This led us …

Predicting Sleep And Sleep Stage In Children Using Actigraphy And Heartrate Via A Long Short-Term Memory Deep Learning Algorithm: A Performance Evaluation, Robert Weaver Med, Phd, James White, Olivia Finnegan, Hongpeng Yang, Zifei Zhong, Keagan Kiely, Catherine Jones, Yan Tong, Srihari Nelakuditi, Rahul Ghosal, David E. Brown, Russell R. Pate Ph.D., Gregory J. Welk, Massimiliano De Zambotti, Yuan Wang, Sarah Burkart, Elizabeth L. Adams Phd, Bridget Armstrong, Michael Beets Med, Mph, Phd

Faculty Publications

Children's ambulatory sleep is commonly measured via actigraphy. However, traditional actigraphy measured sleep (e.g., Sadeh algorithm) struggles to predict wake (i.e., specificity, values typically < 70) and cannot predict sleep stages. Long short-term memory (LSTM) is a machine learning algorithm that may address these deficiencies. This study evaluated the agreement of LSTM sleep estimates from actigraphy and heartrate (HR) data with polysomnography (PSG). Children (N = 238, 5–12 years,52.8% male, 50% Black 31.9% White) participated in an overnight laboratory polysomnography. Participants were referred be-cause of suspected sleep disruptions. Children wore an ActiGraph GT9X accelerometer and two of three consumer wearables(i.e., Apple Watch Series 7, Fitbit Sense, Garmin Vivoactive 4) on their non-dominant wrist during the polysomnogram. LSTM estimated sleep versus wake and sleep stage (wake, not-REM, REM) using raw actigraphy and HR data for each 30-s epoch. Logistic regression and random forest were also estimated as a benchmark for performance with which to compare the LSTM results. A 10-fold cross-validation technique was employed, and confusion matrices were constructed. Sensitivity and specificity were calculated to assess the agreement between research-grade and consumer wearables with the criterion polysomnography. For sleep versus wake classification, LSTM outperformed logistic regression and random forest with accuracy ranging from 94.1to 95.1, sensitivity ranging from 94.9 to 95.9 across different devices, and specificity ranging from 84.5 to 89.6. The addition of HR improved the prediction of sleep stages but not binary sleep versus wake. LSTM is promising for predicting sleep and sleep staging from actigraphy data, and HR may improve sleep stage prediction.