Study: What we learned from record-breaking 2021 heat wave and what we can expect in the future

Portland State University

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The deadly, record-breaking heat wave that hit the Pacific Northwest in June 2021 continues to be the subject of intense interest among scientists, policy makers and the public. A new study from some of the region's top climate scientists synthesized more than 70 publications addressing the causes and consequences of the extreme heat wave and the potential for similar high-heat events to happen in the future.

"It's still the event of interest for anyone who studies heat waves or the atmospheric patterns that cause them," says Paul Loikith, associate professor of geography in Portland State's School of Earth, Environment and Society and a co-author of the study.

Researchers in the Pacific Northwest and around the world agreed that the heat wave was caused by a rare and complex combination of meteorological factors. The primary driver was a persistent, extraordinarily strong ridge of high pressure — often called a "heat dome" — that trapped hot air over the region. Other contributing factors included moisture from the tropical Pacific Ocean, high solar radiation, low pressure offshore, sinking air over land and unusually dry soils.

Loikith says there's no consensus that ridges of high pressure similar to the one that drove this event will become significantly more common, but as the climate continues to warm, the temperatures experienced during the 2021 heat wave, such as 108, 112 and 116 degrees Fahrenheit over three consecutive days in Portland, will become more frequent.

"Essentially, you don't need a high pressure ridge of that magnitude to create a heat wave of that magnitude," Loikith said. "As you get into a warmer climate, you could have a weaker feature in the atmosphere lead to the same temperatures because the overall background climate is getting hotter."

He says the likelihood of reaching 116 degrees Fahrenheit in Portland again is increasing over time, but the probability is still fairly low.

"By the end of the 21st century, a heat wave of this magnitude potentially could be experienced once a decade or maybe even more frequently under a higher emissions scenario," Loikith said.

Whether the region experiences another heat wave of equal magnitude this summer or next summer can't be predicted more than one or two weeks in advance.

"Portland summers have warmed by a lot over the last 80 years — four, even five degrees Fahrenheit — but the 2021 event was almost 40 degrees Fahrenheit above average," Loikith said. "Putting that into context, we're seeing this steady, gradual warming. We will still have some cooler summers and warmer summers. The cooler summers are warmer than cooler summers were in the past, and the warmer summers are warmer than the warm summers in the past."

The researchers say that there is still much to learn about the atmospheric drivers and long-term impacts of these extreme weather events.

The 2021 heat wave had compound effects on human health and ecosystems. Mortality, heat-induced illness and the number of visits to emergency departments were anomalously high, with the greatest impact among older adults, individuals living alone, those with lower incomes, and those without working air conditioning. Browning or scorching of tree leaves and needles following the heat wave was extensive, although the extent of long-term tree mortality is not yet clear. 

Following the heat wave, Oregon, Washington and British Columbia established new initiatives to reduce the risk of heat-related illness, including workplace regulations and new programs to provide cooling devices to populations at greatest risk, but the researchers anticipate it will be several years before the effectiveness of these new interventions is known.

"This is an example of why we need to study these things so that we can better understand them and better predict what their likelihood is going to be in the future," Loikith said. "The studies that we reviewed in this paper help us understand things as basic as atmospheric theory, like things that we still don't fully understand about the atmosphere, all the way to impacts on ecosystems and on people and everything in between. We are still learning, and that's making us more prepared."

The study was published in the Bulletin of the American Meteorological Society. Loikith was joined in the study by Oregon State University's Erica Fleishman, director of the Oregon Climate Change Research Institute and lead author; David Rupp, an associate professor and researcher with OCCRI; Larry O'Neill, Oregon's state climatologist; and Karen Bumbaco, Washington's deputy state climatologist.

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Journal

Bulletin of the American Meteorological Society

DOI

10.1175/BAMS-D-24-0188.1 

Method of Research

Literature review

Article Title

Synthesis of Publications on the Anomalous June 2021 Heat Wave in the Pacific Northwest of the United States and Canada

 

Illegal shark product trade evident in Australia and New Zealand

University of Adelaide

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A bull shark jaw.

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Credit: Josephine Lingard

Research from the University of Adelaide’s School of Biological Sciences and Wildlife Crime Research Hub has highlighted evidence of shark products entering both Australia and Aotearoa/New Zealand, including clear patterns in flows between the two countries.

According to the study, published in Pacific Conservation Biology, the products identified were carried in personal luggage and postage, likely transported for personal use, as trophies, or for resale or consumption.

Most products seized upon entry to Australia came from Asia, and the most seized commodity was fin products. Trophy items, such as preserved specimens, were more likely to originate from the United States of America.

“Over one third of chondrichthyan species, which includes sharks and shark-like rays, are currently threatened with extinction, with all threatened shark species also overfished,” says Josephine Lingard, a PhD candidate at the University of Adelaide.

“Shark species are widely sought after for fins, and many used for shark fin soup, a delicacy and status symbol primarily consumed in Southeast Asia.

“While the global trade in shark meat has been steadily increasing since the early 2000s, the trade of legally collected shark fins – where sharks are brought to land with fins still attached to the body – has been decreasing.”

Asia was also the most common region of origin for products entering Aotearoa/New Zealand, but Oceania followed closely, with Australia being the most dominant country of origin in both passenger and mail seizures.

“We did not expect Australia to be a dominant country of origin for seizures in Aotearoa/New Zealand, given Australia showed a decline in the number of seizures over time and Aotearoa/New Zealand’s seizures increased,” says Lingard.

“However, we suspect the occurrence of seizures from Australia may be due to Aotearoa/New Zealand’s geographic position and international flight connections.

“But it also may be the case that sharks are potentially being caught and products processed and/or purchased in Australia and taken to Aotearoa/New Zealand, or simply that Australia is listed as the country of origin but is merely a stopover location for passengers travelling from elsewhere.”

Lingard’s study drew on border seizure data from Australia and Aotearoa/New Zealand to investigate where shark products originated from and whether there were country-specific differences in the products traded through time.

The study also found there was inconsistent data on the species of shark used in intercepted products, meaning the impact of these products on threatened and endangered species is unclear.

“Less than 1 per cent of seizures from both countries contained species-specific information, but 14 of the 18 seized species that were identified were listed on the Convention on International Trade in Endangered Species,” says Lingard.

“The lack of species information across the datasets we reviewed matches general shark fisheries data where species are often grouped using harmonised system codes, which impedes conservation management of species and makes the monitoring of threatened species increasingly difficult.

"Increased efforts to investigate and record accurate species information across wildlife seizures will greatly assist understanding the patterns and drivers of the illegal wildlife trade, and help deliver real-world actions to help conserve threatened species.”

The University of Adelaide and the University of South Australia are joining forces to become Australia’s new major university – Adelaide University. Adelaide University will open its doors in January 2026. Find out more on the Adelaide University website.

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DOI

10.1071/PC25006 

 

New model extracts sentence-level proof to verify events, boosting fact-checking accuracy for journalists, legal teams, and policymakers

Boosting factuality accuracy by 2.5 points and exact-match by nearly 5 on standard benchmarks

Higher Education Press

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Imagine reading a long article or a thick legal contract and knowing, with confidence, exactly which lines prove that an event happened—or did not happen. That is now possible thanks to a research team at Soochow University. They have built a new neural network that not only determines if an event described in a document is real but also highlights the exact sentences that led it to that conclusion. In head-to-head comparisons with earlier approaches, this new model improved overall fact-checking accuracy by 2.5 points and exact-match accuracy by almost 5 points on a standard benchmark.

“We aimed to open the black box of AI decision-making,” says Prof. Zhong Qian, the lead researcher. “By showing exactly which sentences support our model’s verdict, we make its reasoning as clear as stepping through a well-explained proof.”

Why This Makes a Difference

In our fast-paced digital world, false or misleading claims can spread rapidly. Journalists racing to cover breaking stories need tools that do not simply raise a flag but also explain their reasoning. Legal teams reviewing lengthy contracts cannot afford to miss a single misleading clause. This model’s ability to pinpoint the precise text that supports—or contradicts—an event’s truth helps professionals across fields see exactly why a claim stands or falls. It is a step toward AI systems that feel less like inscrutable black boxes and more like transparent partners.

A Tool for Many Fields

This innovation goes far beyond the newsroom. In the media, it could slash the time needed to verify eyewitness accounts or viral online claims by highlighting the most telling sentences. In law, it could breeze through pages of dense text to mark where a statement is grounded in fact or where it may be speculative. Even scientists and developers of future AI systems will benefit from a clear example of how to strike a balance between accuracy and interpretability. In every case, the result is greater trust and faster, more reliable decision-making.

What the Tests Showed

When the team applied their model to an English corpus used by researchers worldwide, it achieved a factual-accuracy score of 66.9%—up from 64.4%—while exact matches rose to 42.9%, nearly five points higher than the previous best pipeline approach. The gains were most dramatic in cases involving speculative language or outright negatives—areas where earlier models often struggled. The model even maintained its edge when tested on a Chinese version of the same dataset, demonstrating its ability to adapt across languages.

How It Comes Together

At the heart of the new approach is a method of examining a document from multiple angles simultaneously. The system builds a web of connections among words, sentences, and special cues such as “not” or “perhaps.” It then homes in on the exact stretch of text that carries the weight of the truth decision. Finally, it blends those pinpointed clues with the overall story of the document to arrive at a final verdict. The result is both precise and coherent, with no loose ends.

This work appears in the June 2025 issue of Frontiers of Computer Science. The authors plan to share their code and detailed annotations, allowing others to build upon their outcomes. As AI becomes a crucial part of our daily lives, innovations like this one promise to keep machines honest, transparent, and valuable—whether we are checking the latest news, reviewing a contract, or simply reading for pleasure.

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Journal

Frontiers of Computer Science

DOI

10.1007/s11704-024-3809-6 

Method of Research

Experimental study

Subject of Research

Not applicable

Article Title

A multi-view heterogeneous and extractive graph attention network for evidential document-level event factuality identification

 

Bond behavior of FRP bars in concrete under reversed cyclic loading: an experimental study

ELSP

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The study investigates the cyclic bond behavior of FRP bars from four key parameters—bar diameter, embedment length, concrete strength, and rib geometry—leading to the development of a unified bond stress–slip constitutive model and hysteresis framework. Validated through systematic pull-out tests, the model accurately captures interfacial degradation mechanisms under cyclic loading and provides a reliable foundation for simulating FRP-concrete interaction in seismic applications, enhancing the precision of performance-based structural design.

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Credit: Bo Li/Beijing University of Technology, Dong Li/Beijing University of Technology, Fengjuan Chen/Beijing University of Technology, Liu Jin/Beijing University of Technology, Xiuli Du/Beijing University of Technology

Published in Smart Construction, this study investigates the cyclic bond behavior of fiber reinforced polymer (FRP) bars—an area vital to seismic design yet previously underexplored. By examining carbon (CFRP), glass (GFRP), and basalt (BFRP) fiber reinforced polymer bars under reversed cyclic loading, the research quantifies how bar diameter, embedment length, concrete strength, and rib geometry influence initial bond stiffness, unloading strength, frictional resistance, and energy dissipation. A unified bond stress–slip constitutive model and hysteresis framework are developed to capture interfacial degradation mechanisms under cyclic loads. These contributions offer key insights for improving the seismic performance and reliability of FRP-reinforced concrete structures in earthquake-prone regions.

This study uses physical test methods to systematically study the evolution of bonding performance of different types of fiber reinforced polymer (FRP) bars under cyclic loading. Through positive and negative cyclic pull-out tests on three types of FRP bars, carbon fiber (CFRP), glass fiber (GFRP) and basalt fiber (BFRP), the effects of bar diameter, anchorage length, concrete strength and surface rib shape on bonding stiffness, unloading strength, friction and energy dissipation capacity are comprehensively analyzed.

1. Experimental design

1. Material type: Three representative FRP bars, CFRP, GFRP and BFRP, are selected to study the differences in their bonding behavior under cyclic loading.
2. Control parameters: Multiple groups of bar diameter, anchorage length, concrete strength grade and rib shape feature combinations are set to systematically examine the effects of various factors on bonding performance.
3. Loading method: Apply positive and negative cyclic displacement loading to obtain a complete load-slip hysteresis curve to characterize the degradation characteristics of bonding performance.

2. Key Parameter Analysis

The effects of key influencing parameters—bar diameter, embedment length, concrete compressive strength, and surface rib geometry—were investigated through reversed cyclic pull-out tests to evaluate the bond behavior between FRP bars and concrete. Bond performance indicators such as initial stiffness, unloading strength, frictional resistance, and energy dissipation were analyzed. Main findings include:

1. Bar diameter: An increase in diameter generally reduces bond stress due to a lower specific surface area, resulting in decreased frictional resistance and energy dissipation under cyclic loading.
2. Embedment length: Greater embedment length enhances anchorage capacity and improves unloading stiffness, but after a threshold, the bond performance gain becomes marginal.
3. Concrete compressive strength: Higher concrete strength improves initial stiffness and peak bond strength, while also delaying interface degradation during cyclic loading.
4. Rib geometry: Well-defined surface ribs significantly enhance mechanical interlock, improving cyclic bond performance; however, overly aggressive ribs may lead to stress concentration and early interface damage.

3. Constitutive Model and Hysteresis Framework

This study reveals that the bond–slip behavior between FRP bars and concrete under cyclic loading is governed by complex interfacial degradation mechanisms, including frictional loss, stiffness reduction, and progressive slip accumulation. Through systematic reversed cyclic pull-out testing, the evolution of bond performance across different FRP types and influencing parameters was quantified.

To capture these mechanisms, a unified bond stress–slip constitutive model was proposed, incorporating distinct loading, unloading, and reloading branches. The model reflects nonlinearity in initial stiffness, residual strength after unloading, and energy dissipation via slip-dependent degradation rules. A corresponding hysteresis framework was developed to describe the full cyclic response, including pinching effects and strength decay over multiple load cycles.

The proposed model significantly improves prediction accuracy for bond behavior under seismic-like loading and serves as a foundational tool for nonlinear simulation of FRP-reinforced concrete structures. It also lays the groundwork for integrating interfacial damage mechanics into performance-based seismic design. Future work will focus on extending the model to full-scale structural elements and validating it against dynamic loading conditions.

This paper “ Bond behavior of FRP bars in concrete under reversed cyclic loading: an experimental study” was published in Smart Construction.

Li B, Li D, Chen F, Jin L, Du X. Bond behavior of FRP bars in concrete under reversed cyclic loading: an experimental study. Smart Constr. 2025(2): 0013, https: //doi. org/ 10. 55092/ sc20250013.

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Journal

Smart Construction

DOI

10.55092/sc20250013 

Method of Research

Experimental study

Subject of Research

Not applicable

Article Title

Bond behavior of FRP bars in concrete under reversed cyclic loading: an experimental study

21ST CENTURY ALCHEMY

Researchers uncover new mechanism of ion transport in nanofiltration membranes

Chinese Academy of Sciences Headquarters

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Preference of Negatively Charged Membranes in Magnesium and Lithium Separation by Nanofiltration

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Credit: LIU Lulu

A research team led by Prof. WAN Yinhua from the Institute of Process Engineering of the Chinese Academy of Sciences has uncovered a surprising new mechanism that fundamentally alters our understanding of ion transport in nanofiltration (NF) membranes and provides critical insights into improving lithium recovery from high-magnesium brines.

The findings were published in Nature Communications on July 1.

For years, scientists believed that positively charged NF membranes should be more effective at separation of lithium (Li⁺) and magnesium (Mg²⁺) ions than negatively charged membranes based on the principles of co-ion competition and Donnan equilibrium, which describe how ions are repelled by a like-charged membrane. These theories suggested that Li⁺, being smaller and singly charged, would pass through a positively charged membrane more easily than Mg²⁺, which is larger and carry a double charge.

Yet, in practice, negatively charged NF membranes often exhibit superior Li⁺/Mg²⁺ selectivity—an observation that existing theories failed to fully explain.

To understand this inconsistency, the researchers combined molecular dynamics simulations with experimental measurements to examine ion transport dynamics in mixed salt systems. They found that highly negatively charged NF membranes with small pore sizes simultaneously achieved high Mg²⁺ rejection (>90%) and remarkably low Li⁺ rejection (–53.2%), indicating an unusual selectivity mechanism.

To explain this phenomenon, the researchers proposed a "counter-ion competition mechanism." Under this mechanism, strongly hydrated Mg²⁺ ions accumulate near the membrane surface due to charge attraction, promoting dehydration of the weakly hydrated Li⁺ ions at the pore entrance. This dehydration reduces the size of Li⁺ and strengthens its electrostatic interaction with the membrane, ultimately enhancing its transport across the membrane.

"Our findings provide a mechanistic foundation for designing next-generation NF membranes with tailored ion selectivity," said Prof. LUO Jianquan, corresponding author of the study. "This work not only advances the theoretical understanding of the NF process but also opens up new possibilities for efficient lithium extraction from challenging brine resources."

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Journal

Nature Communications

DOI

10.1038/s41467-025-61336-6 

Method of Research

Experimental study

Subject of Research

Not applicable

Article Title

Preference of negatively charged membranes in magnesium and lithium separation by nanofiltration

Article Publication Date

1-Jul-2025

 

Multi-dimensional data interpretation breakthrough enables non-invasive defective filter identification in water treatment facilities

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The multi-dimensional data interpretation framework integrates upside-down 3D laser scanning, SCADA sensor data analysis, and CFD simulation validation, tested on actual water treatment filters during backwash operations. The framework features non-invasive detection through geometric feature analysis and time-series pattern recognition, achieving reliable identification of subsurface structural defects. This system could be applied in smart water treatment facilities for automated filter health monitoring, predictive maintenance, and optimized backwash operations.

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Credit: Pengkun Liu/Carnegie Mellon University, Jinghua Xiao/Circular Water Solution LLC, Pingbo Tang/Carnegie Mellon University,

Researchers have developed a revolutionary non-invasive method combining 3D laser scanning technology and sensor data time-series analysis for identifying defective water treatment filters, achieving significant reductions in inspection time and labor costs while eliminating operational disruptions. Published in Smart Construction, this breakthrough has the potential to transform water treatment facility maintenance, ensuring safer and more efficient water processing by detecting subsurface structural defects through surface geometric changes and operational anomalies.

Water treatment filters serve as the final physical barrier for removing suspended solids and pathogens, making their structural integrity crucial for effective water treatment and public health protection. However, traditional filter inspection methods require manual disruption of filter media, are time-consuming and labor-intensive, involving at least two personnel for several hours, and risk overlooking defects due to limited sampling areas. Addressing this challenge, Dr. Pengkun Liu and Professor Pingbo Tang from Carnegie Mellon University, in collaboration with researchers from Circular Water Solution LLC, has developed a state-of-the-art multi-dimensional data interpretation framework that revolutionizes water treatment filter monitoring without operational interruptions.

"This framework design marks a critical advancement in water treatment facility maintenance," explains Professor Pingbo Tang. "Our central hypothesis is that when subsurface structural irregularities disrupt normal backwash operations and flow patterns, they produce both visible surface deformations and distinctive anomalies in sensor data, enabling non-invasive defect detection."

The newly developed framework utilizes upside-down installed 3D laser scanners to capture high-precision geometric changes on filter media surfaces before and after backwash processes. Lead researcher Pengkun Liu emphasizes, "The integration of geometric feature analysis—including roughness, curvature, omnivariance, and planarity—with time-series sensor data significantly enhances detection accuracy while eliminating the need for invasive media disruption."

A major challenge in filter inspection has been the inability to detect subsurface defects like uneven gravel support beds, mud ball formation, or underdrain blockages without disrupting operations. The team addressed this by developing a comprehensive four-module architecture: data acquisition through 3D laser scanning and SCADA sensor monitoring, geometric analysis using advanced clustering methods, time-series analysis of operational parameters, and fusion diagnosis validated by computational fluid dynamics simulations.

The framework, tested extensively at the Shenango Water Treatment Plant in Pennsylvania across six filter units, underwent rigorous validation over multiple operational cycles. Led by Jinghua Xiao from Circular Water Solution, the team demonstrated that the system effectively identifies abnormal filters through surface elevation irregularities, geometric feature variations, and operational parameter anomalies. Their measurements confirmed that defective filters exhibit distinctive patterns: surface elevation differences, higher geometric feature values, longer backwash durations, elevated turbidity levels, and reduced water production rates.

In practical applications, the framework successfully identified Filter 2 as defective, showing consistent surface bulging patterns across multiple scans, abnormal geometric characteristics, and suboptimal operational performance. These findings were validated through CFD simulations, which confirmed that subsurface defects like mud balls or dead zones disrupt uniform flow distribution from bottom drainage pipes, causing uneven surface deformations. The repeatability tests demonstrated high consistency in defect detection, proving the system's reliability in real-world water treatment settings.

"This framework has the potential to significantly impact the development of smart water treatment systems and infrastructure monitoring applications," says co-researcher Jinghua Xiao. "Its non-invasive nature and comprehensive multi-modal approach could lead to safer, more cost-effective, and more reliable maintenance practices for water treatment facilities worldwide."

In addition to water treatment applications, the techniques developed in this framework could inspire innovations in other infrastructure monitoring settings requiring precise subsurface defect detection, such as advanced pipeline inspection, industrial filtration systems, and civil infrastructure health monitoring.

The multi-dimensional data interpretation framework achieves exceptional performance through its innovative combination of 3D geometric analysis and sensor data fusion. It operates effectively with millimeter-level precision in surface change detection and real-time operational parameter monitoring, offering both geometric and time-series anomaly detection capabilities. "This approach establishes quantitative relationships between surface irregularities and subsurface defects, setting a new standard for non-invasive infrastructure inspection," notes Professor Pingbo Tang.

While the team acknowledges the need for expanding the dataset to more water treatment facilities and developing real-time monitoring systems, this study represents a critical step toward more efficient, safer, and more reliable water treatment operations. Future research directions include integrating 3D LiDAR sensors directly into filter structures for continuous monitoring and developing adaptive backwash control systems based on real-time surface geometry analysis.

This paper ”Multi-dimensional data interpretation for defective filter identification” was published in Smart Construction.
Liu P, Xiao J, Tang P. Multi-dimensional data interpretation for defective filter identification. Smart Constr. 2025(2):0014, https://doi.org/10.55092/sc20250014.

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Journal

Smart Construction

DOI

10.55092/sc20250014 

Method of Research

Experimental study

Subject of Research

Not applicable

Article Title

Multi-dimensional data interpretation for defective filter identification

Article Publication Date

20-Jun-2025