It’s possible that I shall make an ass of myself. But in that case one can always get out of it with a little dialectic. I have, of course, so worded my proposition as to be right either way (K.Marx, Letter to F.Engels on the Indian Mutiny)
There are two species of streamertail hummingbirds on the island of Jamaica, West Indies—one with red-billed males (Trochilus polytmus) and the other with black-billed males (T. scitulus). This is a puzzling situation, as many evolutionary biologists have argued that avian speciation is unlikely to occur on small oceanic islands. Caroline Duffie Judy and colleagues investigated the hybrid zone that separates the two species, which is as narrow as 3.2 km. The authors analyzed 186 Trochilus specimens from 12 sample locations across the island, using 6,451 single nucleotide polymorphisms (SNPs) and a piece of the mitochondrial control region to characterize genomic patterns. The two species are closely related, suggesting either a recent speciation event or extensive gene flow following secondary contact. Although a river separates the species, the waterway is unlikely to serve as a significant barrier to the birds. Instead, the authors propose that sexual selection is likely to play a key role in both driving and maintaining species differentiation. According to the authors, hybrids, which have mottled bill colors, may be less attractive to females of either species because their bills resemble those of juveniles—a mechanism that may contribute to the extremely stable hybrid zone.
Dueling male streamertail hummingbirds. The individual on the left is a confirmed hybrid (T. polytmus x scitulus).
Credit
Rosemary Lloyd / Macaulay Library at the Cornell Laboratory of Ornithology (ML508984951)
Red-billed Streamertail (Trochilus polytmus)
Credit
Sam Woods
Journal
PNAS Nexus
Article Title
Speciation with gene flow in an island endemic hummingbird
Article Publication Date
15-Apr-2025
Corn leads to improved performance in lithium-sulfur batteries
From left to right, PhD student Ying Guo, master's student Justin Zhong, Professors Jin Liu and Katie Zhong, and Postdoctoral Researcher Lulu Ren are studying the use of corn protein to improve lithium-sulfur batteries.
Lithium-sulfur batteries are lighter for the same amount of energy and more environmentally friendly than commonly used lithium-ion batteries, but their commercial adoption has been limited by technological hurdles that shorten their lifespan.
The WSU team’s research, published in the Journal of Power Sources, showed that a protective barrier made of corn protein, in combination with a commonly used plastic, significantly improved the performance of a button-sized lithium-sulfur battery. The researchers found that the battery could hold its charge over 500 cycles, a significant improvement over batteries without the protective corn barrier, known as a separator.
“This work demonstrated a simple and efficient approach to preparing a functional separator for enhancing the battery’s performance,” said Katie Zhong, professor in the School of Mechanical and Materials Engineering and a corresponding author on the paper. “The results are excellent.”
Lithium-sulfur batteries are considered a possible alternative to lithium-ion batteries for many applications. They theoretically contain a lot more energy, so using them in cars or airplanes would require much smaller and lighter batteries than current batteries. Furthermore, the lithium-sulfur battery uses sulfur for its cathode, which is abundantly available, cheap, and non-toxic, making it more environmentally friendly than current batteries. The cathode of a lithium-ion battery is made of metal oxides and include toxic heavy metals like cobalt or nickel.
In their proof-of-concept work, the researchers used corn protein as a cover for a separator in the middle of the battery to prevent both problems.
“Corn protein would make for a good battery material because it’s abundant, natural, and sustainable,” said Jin Liu, professor in the School of Mechanical and Materials Engineering and a corresponding author on the paper.
Graduate students Ying Guo, Pedaballi Sireesha and Chenxu Wang led the work.
The building blocks of the protein are amino acids, which reacted with the battery materials to improve the movement of lithium ions and inhibit the shuttle effect. Because protein is naturally folded on top of itself, the researchers added a small amount of flexible plastic to flatten it and improve its performance.
“The first thing we need to think about is how to open the protein, so we can use those interactions and manipulate the protein,” said Liu.
The researchers conducted both numerical studies and experiments to prove the battery’s success. They are conducting further studies on how the process worked, which amino acid interactions might be responsible, and how the protein structure might be optimized.
“A protein is a very complicated structure,” said Zhong. “We need to do further simulation studies to identify which amino acids in the protein structure can work best for solving the critical shuttle effect and dendrite problems.”
The researchers would like to collaborate with industry partners to study larger experimental batteries and to scale up the process. The work was funded by the U.S. Department of Agriculture.
Herndon, VA, March 25, 2025 -- About two million people in the United States lack access to running water or indoor plumbing in their homes. Another 30 million people live where drinking water systems violate safety rules. Water privatization -- the transfer of public water systems ownership and/or management to private companies -- has been proposed as a potential solution to provide more Americans with safe, clean drinking water. But opponents argue that private companies may prioritize profits over public needs.
To investigate how private vs. public water systems affect water quality and equal access to safe, clean water, researchers mapped the distribution of water system ownership, water system violations, and water injustice nationwide. Their findings are published in the journal Risk Analysis.
The study is the first to integrate geospatial mapping of water violations, social vulnerability, and, importantly, perceptions of water access in relation to public versus private ownership of water systems on a national scale.
“Policymakers can use our findings to identify and prioritize enforcement efforts in hotspots, make improvements in infrastructure, and implement policies that ensure affordable and safe drinking water - particularly for socially vulnerable communities,” says lead author Alex Segrè Cohen, assistant professor of science and risk communication at the University of Oregon. “We found that violations and risks of water injustice tend to cluster in specific areas or hotspots across the country.”
Here are some of the key findings:
• The top 10 counties with the highest ranking for water violations were in West Virginia, Pennsylvania, North Carolina, and Oklahoma.
• The highest number of violations reported by a single water system was a public system owned by a local government in Wyoming county, West Virginia.
• The top 10 counties with the highest ranking for water injustice were in Mississippi (8 out of the 10), South Dakota, and Texas.
• Hotspots of water injustice were more often located in areas with lower private system ownership. (This suggests that public water systems are not necessarily better at preventing violations, according to the authors).
• Living in a county with both high water injustice and a higher proportion of privatized water systems was associated with a greater concern or perception of vulnerability around water access and security - with concerns about water accessibility, safety, and reliability.
Water system violations include failures to comply with regulations under the Safe Drinking Water Act, including health-based violations such as exceeding maximum levels of contaminants, non-compliance with mandated water treatment techniques, and failure to follow monitoring schedules and communicate required information to customers.
The researchers define water injustice as the unequal access to safe and clean drinking water that disproportionately impacts low-income households and people of color.
They devised a county-level score based on the performance of local drinking water systems (based on data from the U.S. Environmental Protection Agency’s (EPA’s) Safe Drinking Water Information System (SDWIS) and community social vulnerability (using the U.S. Center for Disease Control’s (CDC’s) Environmental Justice Index). These data were merged with a nationally representative survey of U.S. residents (collected in 2019) that measured how people rated their access to drinking water and the quality and reliability of water systems in their area, among other water injustice indicators.
“Our results suggest that privatization alone is not a solution,” says Segrè Cohen. “The local context, such as regulatory enforcement, community vulnerability, and community priorities, matters in determining outcomes.”
About SRA The Society for Risk Analysis is a multidisciplinary, interdisciplinary, scholarly, international society that provides an open forum for all those interested in risk analysis. SRA was established in 1980. Since 1982, it has continuously published Risk Analysis: An International Journal, the leading scholarly journal in the field. For more information, visit www.sra.org.
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Journal
Risk Analysis
Method of Research
Imaging analysis
Subject of Research
Not applicable
Article Title
Mapping risks of water injustice and perceptions of privatized drinking water in the United States: A mixed methods approach
Article Publication Date
15-Apr-2025
Busted! Researchers revolutionize fraud detection with machine learning
FAU engineers offer promising solution for fraud detection in health care, finance and more
A new machine learning breakthrough outperforms traditional methods by reducing false positives and minimizing cases needing further inspection, crucial for sectors like Medicare and credit card fraud, where fast data processing is vital to preventing losses and improving efficiency.
Fraud is widespread in the United States and increasingly driven by technology. For example, 93% of credit card fraud now involves remote account access, not physical theft. In 2023, fraud losses surpassed $10 billion for the first time. The financial toll is staggering: credit card fraud costs $5 billion annually, affecting 60% of U.S. cardholders, while identity theft resulted in $16.4 billion in losses in 2021. Medicare fraud costs $60 billion each year, and government losses range from $233 billion to $521 billion annually, with improper payments totaling $2.7 trillion since 2003.
Machine learning plays a critical role in fraud detection by identifying patterns and anomalies in real-time. It analyzes large datasets to spot normal behavior and flag significant deviations, such as unusual transactions or account access. However, fraud detection is challenging because fraud cases are much rarer than normal ones, and the data is often messy or unlabeled.
To address these challenges, researchers from the College of Engineering and Computer Science at Florida Atlantic University have developed a novel method for generating binary class labels in highly imbalanced datasets, offering a promising solution for fraud detection in industries like health care and finance. This approach works without relying on labeled data, a key advantage in sectors where privacy concerns and the cost of labeling are significant obstacles.
The team tested their method on two real-world, large-scale datasets with severe class imbalance (less than 0.2%): European credit card transactions (more than 280,000 from September 2013) and Medicare Part D claims (more than 5 million from 2013 to 2019), both labeled as fraudulent or genuine. These datasets, with fraud cases far outnumbered by non-fraud cases, provide a real-world challenge ideal for testing fraud detection methods.
Results of the study, published in the Journal of Big Data, show that this new labeling method effectively addresses the challenge of labeling severely imbalanced data in an unsupervised framework. Additionally, and unlike traditional methods, this approach evaluated the newly generated fraud and non-fraud labels directly without the need of relying on a supervised classifier.
“The use of machine learning in fraud detection brings many advantages,” said Taghi Khoshgoftaar, Ph.D., senior author and Motorola Professor in the FAU Department of Electrical Engineering and Computer Science. “Machine learning algorithms can label data much faster than human annotation, significantly improving efficiency. Our method represents a major advancement in fraud detection, especially in highly imbalanced datasets. It reduces the workload by minimizing cases that require further inspection, which is crucial in sectors like Medicare and credit card fraud, where fast data processing is vital to prevent financial losses and enhance operational efficiency.”
The study shows the new method outperformed the widely-used Isolation Forest algorithm, providing a more efficient way to identify fraud while minimizing the need for further investigation. This confirms the method’s ability to generate reliable binary class labels for fraud detection, even in challenging datasets. It offers a scalable solution for detecting fraud without relying on costly and time-consuming labeled data, which requires significant manual expert input and is resource-intensive, especially for large datasets.
“Our method generates labels for both fraud or positive and non-fraud or negative instances, which are then refined to minimize the number of fraud labels,” said Mary Anne Walauskis, first author and a Ph.D. candidate in the FAU Department of Electrical Engineering and Computer Science. “By applying our method, we minimize false positives, or in other words, genuine instances marked as fraud, which is key to improving fraud detection.
This approach ensures that only the most confidently identified fraud cases are retained, enhancing accuracy and reducing unnecessary alarms, making fraud detection more efficient.”
The method combines two strategies: an ensemble of three unsupervised learning techniques using the SciKit-learn library and a percentile-gradient approach. The goal is to minimize false positives by focusing on the most confidently identified fraud cases. This is achieved by refining the labels and reducing errors in both the unsupervised methods (EUM) and the percentile-gradient approach (PGM).
The refined labels create a subset of confident labels that are highly likely to be accurate. These labels are then used to create confidence intervals and finalize the labeling, requiring minimal domain knowledge to select the number of positive instances.
“This innovative approach holds great promise for industries plagued by fraud, offering a more accessible and effective way to identify fraudulent activity and safeguard both financial and health care systems,” said Stella Batalama, Ph.D., dean of the College of Engineering and Computer Science. “Fraud’s impact goes beyond financial losses, including emotional distress, reputational damage and reduced trust in organizations. Health care fraud, in particular, undermines care quality and cost, while identity theft can cause severe stress. Addressing fraud is key to mitigating its broad societal impact.”
Looking ahead, the research team plans to enhance the method by automating the determination of the optimal number of positive instances, further improving efficiency and scalability for large-scale applications.
The current journal article, Unsupervised Label Generation for Severely Imbalanced Fraud Data, is an updated version of the researchers’ previous work, Confident Labels: A Novel Approach to New Class Labeling and Evaluation on Highly Imbalanced Data. The original paper was presented and published at the IEEE 36th International Conference on Tools with Artificial Intelligence (ICTAI) in November 2024, where it won the Best Student Paper Award. ICTAI, with an acceptance rate of about 25% from more than 400 submissions, is a prestigious conference.
- FAU -
About FAU’s College of Engineering and Computer Science:
The FAU College of Engineering and Computer Science is internationally recognized for cutting-edge research and education in the areas of computer science and artificial intelligence (AI), computer engineering, electrical engineering, biomedical engineering, civil, environmental and geomatics engineering, mechanical engineering, and ocean engineering. Research conducted by the faculty and their teams expose students to technology innovations that push the current state-of-the art of the disciplines. The College research efforts are supported by the National Science Foundation (NSF), the National Institutes of Health (NIH), the Department of Defense (DOD), the Department of Transportation (DOT), the Department of Education (DOEd), the State of Florida, and industry. The FAU College of Engineering and Computer Science offers degrees with a modern twist that bear specializations in areas of national priority such as AI, cybersecurity, internet-of-things, transportation and supply chain management, and data science. New degree programs include Master of Science in AI (first in Florida), Master of Science and Bachelor in Data Science and Analytics, and the new Professional Master of Science and Ph.D. in computer science for working professionals. For more information about the College, please visit eng.fau.edu.
About Florida Atlantic University: Florida Atlantic University, established in 1961, officially opened its doors in 1964 as the fifth public university in Florida. Today, Florida Atlantic serves more than 30,000 undergraduate and graduate students across six campuses located along the Southeast Florida coast. In recent years, the University has doubled its research expenditures and outpaced its peers in student achievement rates. Through the coexistence of access and excellence, Florida Atlantic embodies an innovative model where traditional achievement gaps vanish. Florida Atlantic is designated as a Hispanic-serving institution, ranked as a top public university by U.S. News & World Report, and holds the designation of “R1: Very High Research Spending and Doctorate Production” by the Carnegie Classification of Institutions of Higher Education. Florida Atlantic shares this status with less than 5% of the nearly 4,000 universities in the United States. For more information, visit www.fau.edu.
Sleep is a necessary part of people’s daily routine, but modern lifestyles and technology have ushered in an era of decreased rest time and subsequent fatigue. Further, the bedroom environment, such as light, sound, and temperature, is important for a good night's sleep, though this is often neglected in residential architecture.
In search of a conclusive remedy, common sleep studies use artificial light that is easy to control. Osaka Metropolitan University researchers, however, believe natural light could be more effective for re-creating actual living environments.
To test this, Graduate School of Human Life and Ecology student Xiaorui Wang and Professor Daisuke Matsushita led a team in examining whether introducing moderate light into the bedroom just before waking would improve morning wakefulness. Using light-shielding curtains and motorized closing devices, a comparative experiment was conducted on 19 participants under three conditions: natural light for 20 minutes before waking up (IA), natural light from dawn until waking up (IB), and no natural light before waking up (CC). After each session, participants’ sleepiness, alertness, and fatigue were measured with an electrocardiogram, electroencephalogram, and a survey.
Results revealed that participants were less sleepy in IA and IB conditions than in CC. In addition, IA was found to be one of the most effective methods for improving wakefulness, as too much light in IB caused adverse effects.
“In the future, we hope to control natural light in the sleep environment as it changes with the seasons and time of day, and to clarify how to introduce natural light that is suitable for a more comfortable awakening,” stated Professor Matsushita.
The findings were published in Building and Environment.
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About OMU
Established in Osaka as one of the largest public universities in Japan, Osaka Metropolitan University is committed to shaping the future of society through “Convergence of Knowledge” and the promotion of world-class research. For more research news, visit https://www.omu.ac.jp/en/ and follow us on social media: X, Facebook, Instagram, LinkedIn.
