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)
Wednesday, October 25, 2023
Our favorite bittersweet symphonies may help us deal better with physical pain
Researchers found that listening to our preferred music reduces pain intensity and unpleasantness, knowledge which could optimize music-based pain therapies
Research has shown that music might be a drug-free way to lower humans’ pain perception. This decreased sensitivity to pain – also known as hypoalgesia – can occur when pain stimuli are disrupted between their point of input and where they are recognized as pain by the conscious mind. In a new study, researchers in Canada have examined what type of music helps to dampen pain perception.
“In our study, we show that favorite music chosen by study participants has a much larger effect on acute thermal pain reduction than unfamiliar relaxing music” said Darius Valevicius, a doctoral student at the Université de Montréal. The research was carried out at the Roy Pain Lab at McGill University and published in Frontiers in Pain Research. “We also found that emotional responses play a very strong role in predicting whether music will have an effect on pain.”
Everybody hurts (but less so when listening to favorite music)
To test which kind of music was most effective for reducing pain, participants received moderately painful thermal stimuli to the inner forearm, resulting in a sensation similar to a hot teacup being held against the skin. These stimuli were paired with music excerpts, each lasting approximately seven minutes.
Compared to control tracks or silence, listening to their favorite music strongly reduced pain intensity and unpleasantness in participants. Unfamiliar relaxing tracks did not have the same effect. “In addition, we used scrambled music, which mimics music in every way except its meaningful structure, and can therefore conclude that it is probably not just distraction or the presence of a sound stimulus that is causing the hypoalgesia,” Valevicius explained.
The researchers also examined if musical themes could modulate the pain-decreasing effects of favorite music. To do that, they interviewed participants about their emotional responses to their favorite music and assigned themes: energizing/activating, happy/cheerful, calming/relaxing, and moving/bittersweet. They discovered that different emotional themes differed in their ability to reduce pain.
“We found that reports of moving or bittersweet emotional experiences seem to result in lower ratings of pain unpleasantness, which was driven by more intense enjoyment of the music and more musical chills,” Valevicius said. Although it is not yet entirely understood what musical chills are, they seem to indicate a neurophysiological process that is effective at blocking pain signals. In some people, chills can manifest as a tingling sensation, shivers, or goosebumps.
Something for the pain
The researchers also pointed to limitations of their study, one of which is concerned with how long participants listen to music samples. For example, listening to relaxing music for longer might have stronger effects than the shorter tracks the participants listened to in this study. Questions which also need to be addressed in further research include if listening to favorite music is also effective with other, non-thermal pain stimuli, such as mechanical stimulation or chronic pain, the researchers said.
“Especially when it comes to the emotion themes in favorite music like moving/bittersweet, we are exploring new dimensions of the psychology of music listening that have not been well-studied, especially in the context of pain relief. As a result, the data we have available is limited, although the preliminary results are fairly strong,” Valevicius concluded.
S.J. & JESSIE E. QUINNEY COLLEGE OF NATURAL RESOURCES, UTAH STATE UNIVERSITY
IMAGE:
WITH ACCESS TO A MAMMOTH SET OF GLOBAL-SCALE CLIMATE DATA AND A NOVEL STRATEGY, A TEAM FROM UTAH STATE UNIVERSITY IDENTIFIED SEVERAL FACTORS TO HELP ANSWER A FUNDAMENTAL ECOLOGICAL QUESTION: WHAT AN ANIMAL EATS (AND HOW THAT INTERACTS WITH CLIMATE) SHAPES EARTH’S DIVERSITY.
Since the HMS Beagle arrived in the Galapagos with Charles Darwin to meet a fateful family of finches, ecologists have struggled to understand a particularly perplexing question: Why is there a ridiculous abundance of species some places on earth and a scarcity in others? What factors, exactly, drive animal diversity?
With access to a mammoth set of global-scale climate data and a novel strategy, a team from the Department of Watershed Sciences in Quinney College of Natural Resources and the Ecology Center identified several factors to help answer this fundamental ecological question. They discovered that what an animal eats (and how that interacts with climate) shapes Earth’s diversity.
“Historically studies looking at the distribution of species across Earth's latitudinal gradient have overlooked the role of trophic ecology — how what animals eat impacts where they are found,” said Trisha Atwood, author on the study from the Department of Watershed Sciences and the Ecology Center. “This new work shows that predators, omnivores and herbivores are not randomly scattered across the globe. There are patterns to where we find these groups of animals.”
Certain locations have an unexpected abundance of meat-eating predators — parts of Africa, Europe and Greenland. Herbivores are common in cooler areas, and omnivores tend to be more dominant in warm places. Two key factors emerged as crucial in shaping these patterns: precipitation and plant growth.
Precipitation patterns across time play a big role in determining where different groups of mammals thrive, Atwood said. Geographical areas where precipitation varies by season, without being too extreme, had the highest levels of mammal diversity.
“Keep in mind that we aren’t talking about the total amount of rain," said Jaron Adkins, lead author on the research. “If you imagine ecosystems around the world on a scale of precipitation and season, certain places in Utah and the Amazon rainforest fall on one end with low variability — they have steady levels of precipitation throughout the year. Other regions, like southern California, have really high variability, getting about 75 percent of the annual precipitation between December and March.”
But the sweet spot for predators and herbivores fell in a middle zone between the two extremes, he said. Places like Madagascar, where precipitation patterns had an equal split between a wet season and a dry one (six months each), had the ideal ecological cocktail for promoting conditions for these two groups. Omnivore diversity tends to thrive in places with very stable climates.
The second important factor connected with mammal diversity the work uncovered was a measure of the amount of plant growth in an area, measured as “gross primary productivity.”
“It makes intuitive sense for plant-eating animals to benefit from plant growth,” Adkins said.
But this measure actually impacted carnivores most, according to the research. The strong relationship between predators and plant growth highlights the importance of an abundance of plants on an entire food chain's structural integrity.
“It was surprising that this factor was more important for predators than omnivores and herbivores,” Atwood said. “Why this is remains a mystery.”
Although evolutionary processes are ultimately responsible for spurring differences in species, climate conditions can impact related factors — rates of evolutionary change, extinction and animal dispersal — influencing species and trait-based richness, according to the research.
Animal diversity is rapidly declining in many ecosystems around the world through habitat loss and climate change. This has negative consequences for ecosystems. Forecasting how climate change will disrupt animal systems going forward is extremely important, Atwood said, and this research is a first step in better managing future conditions for animals around the world.
“Animal diversity can act as an alarm system for the stability of ecosystems,” Atwood said. “Identifying the ecological mechanisms that help drive richness patterns provides insight for better managing and predicting how diversity could change under future climates.”
In addition to Adkins and Atwood, the research included seven authors currently or previously associated with the Department of Watershed Sciences and the Ecology Center: Edd Hammill, Umarfarooq Abdulwahab, John Draper, Marshall Wolf, Catherine McClure, Adrián González Ortiz and Emily Chavez.
CREDIT: DEPARTMENT OF MATERIALS ENGINEERING, GRADUATE SCHOOL OF ENGINEERING, THE UNIVERSITY OF TOKYO
Innovation Center of NanoMedicine (iCONM; Center Director: Kazunori Kataoka; Location: Kawasaki, Japan) has announced with The University of Tokyo that a group led by Prof. Takanori Ichiki, Research Director of iCONM (Professor, Department of Materials Engineering, Graduate School of Engineering, The University of Tokyo), proposed a new property evaluation method of nanoparticles’ shape anisotropy that solves long-standing issues in nanoparticle evaluation that date back to Einstein's time. The paper, titled " Analysis of Brownian motion trajectories of non-spherical nanoparticles using deep learning" was published online in the APL Machine Learning (Note1) dated on October 25, 2023.
In this era where new medical treatments and diagnostic technologies using extracellular vesicles and artificial nanoparticles are attracting attention, nanoparticles are useful materials in the medical, pharmaceutical, and industrial fields. From a materials perspective, it is necessary to evaluate the properties and agglomeration state of each nanoparticle and perform quality control, and progress is expected in nanoparticle evaluation technology that supports safety and reliability.
One way to evaluate nanoparticles in liquid is to analyze the trajectory of Brownian motion. Called NTA, it calculates the diameter of a particle using a theoretical formula discovered by Einstein over 100 years ago. Although it is used as a simple method to measure single particles from micro to nano size, there has been a long-standing problem that it cannot evaluate the shape of nanoparticles.
The trajectory of Brownian motion reflects the influence of particle shape, but it is difficult to actually measure extremely fast motion. Furthermore, even if the particle is non-spherical, conventional analysis methods are not accurate because they unconditionally assume that the particle is spherical and use the Stokes-Einstein equation for analysis. However, using deep learning, which is good at finding hidden correlations in large-scale data, it is possible to detect differences caused by differences in shape may be detected, even when measurement data is averaged or contains errors that cannot be separated.
Our research group succeeded in building a deep learning model that identifies shapes from measured Brownian motion trajectory data without changing the experimental method. In order to take into account not only the time-series changes in data but also the correlation with the surrounding environment, we integrated a 1-dimensional CNN model that is good at extracting local features through convolution and a bidirectional LSTM model that can accumulate temporal dynamics. Through trajectory analysis using the integrated model, we were able to achieve classification accuracy of approximately 80% on a single particle basis for two types of gold nanoparticles that are approximately the same size but have different shapes, which cannot be distinguished using conventional NTA alone.
Such high accuracy indicates that the shape classification of single nanoparticles in liquid using deep learning analysis has reached a practical level for the first time. Furthermore, in the paper, a calibration curve was created to determine the mixing ratio of a mixed solution of two types of nanoparticles (spherical and rod-shaped). Considering the shape types of nanoparticles available in the world, it is thought that this method can sufficiently detect the shape.
The novelty of this study
With conventional NTA methods, the particle shape cannot be directly observed, and the characteristic information obtained was limited. Although the trajectory of Brownian motion (time-series coordinate data) measured by the NTA device contains information on the shape of the nanoparticles, because the relaxation time is extremely short, it has been difficult to actually detect the shape anisotropy of nanoparticles. Furthermore, in conventional analysis methods, even if the particle is non-spherical, it is not accurate due to the shape factor not being applied, because it is assumed to be spherical and analyzed using the Stokes-Einstein equation. We aimed for a new method that anyone can implement, and were able to solve a long-standing problem in Brownian motion analysis by introducing deep learning, which is good at finding hidden correlations in large-scale data, into data analysis without changing simple experimental methods.
The future of this study
In this paper, we attempted to determine the shapes of two types of particles, but considering the types of shapes of commercially available nanoparticles, we think that this method can be used in practical applications such as the detection of foreign substances in homogeneous systems. Expansion of NTA will lead to applications not only in research but also in the industrial and industrial fields, such as evaluating the properties, agglomeration state, and uniformity of nanoparticles that are not necessarily spherical, and quality control. In particular, it is expected to be a solution for evaluating the properties of diverse biological nanoparticles such as extracellular vesicles in an environment similar to that of living organisms. It also has the potential to be an innovative approach in fundamental research on Brownian motion of non-spherical particles in liquid.
Note 1 APL Machine Learning (AML): APL Machine Learning from American Institute of Physics features, vibrant and timely research for two communities: researchers who use machine learning (ML) and data-driven approaches for physical sciences and related disciplines, and researchers from these disciplines who work on novel concepts, including materials, devices, systems, and algorithms relevant for the development of better ML and AI technologies. The journal also considers research that substantially describes quantitative models and theories, especially if the research is validated with experimental results.
The paper describing this presentation is as follows:
Hiroaki Fukuda, Hiromi Kuramochi, Yasushi Shibuta, and Takanori Ichiki, “Analysis of Brownian motion trajectories of non-spherical nanoparticles using deep learning”. APL Machine Learning,1 (2023), in press.
Note 2 Nanoparticle tracking Analysis (NTA): A method in which Brownian motion is recorded by dark-field imaging of the scattered light obtained by irradiating a laser beam on a nanoparticle suspension, and the particle size is determined from each trajectory using the Stokes-Einstein equation. It is characterized by a small amount of sample adjustment and no difficult operations.
Note 3 Brownian motion: Discovered by Robert Brown in 1827, this is a phenomenon in which fine particles suspended in a liquid or gas move irregularly. In 1905, Einstein discovered that the cause was irregular collisions of thermally moving medium (water, air, etc.) molecules, which led to experiments that confirmed the existence of atoms and molecules. Generally, Brownian motion is analyzed using the Stokes-Einstein equation, which is a combination of Stokes' law, which describes the forces acting on particles, and Einstein's equation.
Note 4 1-dimensional CNN (1 Dimensional Convolutional Neural Network): A standard deep learning model mainly used for image processing. For each characteristic, local feature values are extracted using a convolutional layer and compared across the board, which is then repeated to find the spatial features that stand out. Performing convolution along the time axis is also effective for analyzing time series data.
Note 5 LSTM (Long Short-Term Memory): It is also a standard model that is good at analyzing changes over time. The input/output/forget gate structure allows information in memory cells to change over time by selectively retaining relevant information and forgetting irrelevant information. It works similarly to human memory and can learn the characteristics of long-term time-series data, making it suitable for analyzing data whose values change over time.
Analysis of Brownian motion trajectories of non-spherical nanoparticles using deep learning
ARTICLE PUBLICATION DATE
24-Oct-2023
KERI's thermoelectric technology, key to space probes, attracting German attention
Development of 'High-efficiency multistage thermoelectric module' to boost nuclear battery performance with optimal material combinations considering temperature range
CREDIT: KOREA ELECTROTECHNOLOGY RESEARCH INSTITUTE
Drs. SuDong Park, Byungki Ryu, and Jaywan Chung of the Korea Electrotechnology Research Institute (KERI) developed a new thermoelectric efficiency formalism and a high-efficiency multistage thermoelectric power generator module. This innovation can boost nuclear battery performance, crucial for space probes, and has attracted attention from the German Aerospace Research Institute.
A Radioisotope Thermoelectric Generator (RTG), known as a thermoelectric-based nuclear battery, is a dependable power source that has been used in space probes, rovers, and other remote operations. In an RTG, radioisotopes like plutonium-238 and americium-241 decay within a sealed vessel, producing substantial heat—typically ranging from 400-700 degrees Celsius. The RTG captures this heat and directly converts the thermal energy to electrical energy in the cold environment of space.
The core components of RTG technology are the "Radioisotope Heat Unit (RHU)", which harnesses radioactive isotopes as a heating element, and the "thermoelectric power generator module" that converts this heat into electricity. While the development of the RHU is constrained by international restrictions, South Korea's thermoelectric module fabrication technology is considered to be globally competitive.
In RTGs, thermoelectric power modules are designed with a layered arrangement of thermoelectric materials, transitioning from the hot to the cold sides, each optimized for peak performance within specific temperature ranges. This multistage design is crucial given the inherent temperature dependence of thermoelectric material efficiency. Strategically positioning the top-performing materials based on temperature distribution is essential. KERI's landmark accomplishment is their world-class design, synthesis, and analysis of this highly effective layered thermoelectric module.
Initially, the research team identified the shortcomings and constraints of the 'dimensionless thermoelectric figure of merit (ZT)', a traditional metric conventionally used in academia to evaluate thermoelectric performance. They then successfully formulated a new thermoelectric efficiency formalism and equations that allow for precise efficiency predictions. Leveraging this formalism and the thermoelectric data held by KERI, they can predict the performance of thermoelectric power generator modules across more than 100 million potential thermoelectric semiconductor stack combinations. By utilizing the thermoelectric device design program, pykeri, this design and search process has been expedited by several hundred times compared to previous methods. This innovation marks a substantial leap forward from earlier approaches that depended on single-stage thermoelectric materials and the traditional metric.
The KERI research team successfully fabricated multistage thermoelectric modules, achieving an efficiency that surpasses traditional single-stage modules by over 3% when the hot side exceeds 500 degrees Celsius.
Additionally, their innovative fabrication method permits these modules to be comprised of two to four layers, all fitting compactly within a height of just a few millimeters. This advancement not only ensures heightened efficiency but also offers superior compactness and a lightweight design compared to previous methods. Such an internationally competitive milestone stands out prominently in the space auxiliary power market—particularly for small satellites and exploration rovers—garnering significant attention in the civilian commercial sector.
SuDong Park of KERI remarked, "We are the first institute in Korea to conduct thermoelectric power generation research and have a long history and abundant source technology and practical data." He further added, "This achievement is the culmination of convergence research that incorporates mathematics and physics into materials science."
“The module technology developed at KERI is excellent when compared internationally” said Pawel Ziolkowski, Deputy Head of a group of Thermoelectric Functional Materials and Systems, at the German Aerospace Center, adding that "The achieved level of technological maturity provides the best conditions for the development of new RTG-based energy systems for space exploration. This makes a significant contribution to an expanding scope of human space exploration."
The research team believes that this achievement has applications not only in the aerospace and defense sectors that utilize nuclear energy but also in various industries such as industrial waste heat recovery, cooling of communication equipment and optical devices and temperature control of electric vehicle batteries, and plans to strengthen cooperation with related organizations and companies.
Meanwhile, KERI is a government-funded research institute under the National Research Council of Science & Technology of the Ministry of Science and ICT.
Thermoelectric material performance measurement
CREDIT
Korea Electrotechnology Research Institute
TIME selects novel muon navigation system as one of year’s best inventions
The muometric wireless navigation system (MuWNS), which uses particles from space called cosmic-ray muons to enable navigation underwater and underground, has been selected as one of TIME’s 2023 Best Inventions. Invented by Professor Hiroyuki K.M. Tanaka from Muographix at the University of Tokyo, evidence of the system’s capability was first published by Tanaka and his collaborators in June 2023, and rapid improvements have taken place since then. The media company TIME announced its selection for this year’s Best Inventions list on Oct. 24, 2023. The yearly published list highlights 200 inventions which TIME recognizes as solving compelling problems in creative ways.
“It is quite an honor to be selected for the 2023 list’s Experimental category,” said Tanaka. “I started exploring the idea of a muon positioning system back in 2020 as an alternative to GPS, which for me was unreliable. Our initial muon positioning system was wired, but within just a couple of years, we evolved it into wireless MuWNS.”
Unlike GPS, which can be blocked or deflected by surfaces such as water or buildings, muons can penetrate all types of materials and even deep underground without being affected. Furthermore, the radio waves used for GPS can be tampered with while muons cannot. In a paper published in the journal iScience in June 2023, the team described the success of its first real world test of wireless MuWNS. Since then, ongoing work has led to the development of an even better technique.
“When I first invented MuWNS, I wanted to attain at least 1 meter accuracy. Even though we could get down to 2 meters, my goal was not achieved mainly because of the time-scale fluctuations which occur in the clocks used to calibrate the muons’ positions,” explained Tanaka. “However, I recently proposed an entirely new technique called MuWNS-V, with which we achieved centimeter-scale accuracy and which, in principle, could even enable millimeter-scale navigation. We have now carried out our first demonstrations with MuWNS-V and the results will be published in a Nature Reviews article in November later this year.”
With ongoing improvements in accuracy and portability, Tanaka has high hopes and a broad vision for the many potential applications of muon research and technology. This ranges from imaging inside natural phenomena such as volcanoes and cyclones; observing seafloor changes to detect tsunamis; creating highly precise clocks; monitoring the structural health of buildings; offering wireless security for an uncrackable cryptosystem; automated vehicle navigation indoors, underground and underwater; and search and rescue operations, to name a few.
“We have already started to develop an autonomous mobile robot called CosmoBOT that works underground. The idea is that by using a web-based system, it can be remotely operated by anyone from anywhere in the world. With further developments, we hope this technology can be applied to robots in logistics warehouses, rescue operations, port constructions, mining, etc. Furthermore, in principle, with next-level millimeter-scale accuracy, it may become possible to operate articulated robots for very delicate tasks, such as remote surgery,” said Tanaka. “I believe that muography will be an entire academic discipline, which will lead to irreplaceable technologies that will target people’s needs and help us understand the Earth in an unprecedented way.”
As muons pass first through the reference detectors above ground and then a receiver below ground – which could be in an autonomous vehicle or a miner’s phone, for example – the “time of flight” between them enables the receiver’s coordinates to be determined.
CREDIT
2021 Hiroyuki K.M. Tanaka/ Muographix
The media company TIME announced its selection for this year’s Best Inventions list on Oct. 24, 2023.
Press contact: Mrs. Nicola Burghall Public Relations Group, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan press-releases.adm@gs.mail.u-tokyo.ac.jp
About the University of Tokyo The University of Tokyo is Japan's leading university and one of the world's top research universities. The vast research output of some 6,000 researchers is published in the world's top journals across the arts and sciences. Our vibrant student body of around 15,000 undergraduate and 15,000 graduate students includes over 4,000 international students. Find out more at www.u-tokyo.ac.jp/en/ or follow us on Twitter at @UTokyo_News_en.
Lasers are a viable alternative to high-temperature roasting for smelting zinc
One of the leading causes of pollution in China is the non-ferrous metallurgical industry, which processes heavy metals for use in a variety of industries. In an effort to meet China’s goals of carbon neutrality before 2060, researchers are studying how to reduce carbon emissions in this vital industry. In a recently published paper, researchers propose smelting zinc with lasers instead of traditional high-temperature roasting and electrolysis.
“The zinc industry, with a total carbon dioxide emission of 33 million tons, is a major driver of greenhouse gas emissions. It is estimated that the electrolytic zinc industry in China produces around 6 million tons of solid hazardous waste each year, which causes environmental pollution and significant health risks,” said Ning Duan, a researcher at the State Key Laboratory of Pollution Control and Resources Reuse at the College of Environmental Science and Engineering at Tongji University in Shanghai, China. “We propose a new optical metallurgy method, which uses laser-induced photoreduction to decompose sphalerite and reduce metal ions to metal. Since it does not use high-temperature roasting, this method provides a new way to produce high-purity metal without the greenhouse gas emissions and heavy metal pollution caused by traditional zinc electrolysis.”
A material called sphalerite (ZnS) is the main source material for the electrolytic zinc industry. It is an ideal candidate for the proposed laser treatment because it responds well to light. Researchers then constructed an experimental apparatus to test the reaction of sphalerite to laser-induced decomposition. It included a laser, lens, vacuum chamber, and camera. The vacuum chamber prevented the interference of oxygen in the experiment.
Researchers completed Raman spectroscopy, XPS analysis, and EXAFS spectra analysis to determine how well the sphalerite responded to the laser. They also looked at the micromorphology of the damage caused by the laser using electron microscopy. When the laser interacts with the surface of the sphalerite, it produces a photochemical reduction and a photothermal reduction. The photothermal effect that occurs when the ultraviolet laser irradiates the sphalerite is strong, creating molecular vibration energy that converts into heat energy. This makes the surface temperature of the sphalerite rise, cracking ionic bonds, melting and vaporizing materials on the surface of the sphalerite, and producing zinc.
“This study demonstrated that zinc can be produced by decomposing sphalerite using laser irradiation under an inert argon gas atmosphere. This process does not require high temperatures and produces no greenhouse gases or pollutants. Our results are encouraging for the prospect of optical metallurgy,” said Duan.
Researchers also did an economic comparison between optical metallurgy and traditional metallurgy for zinc production to confirm that the proposed laser irradiation method is not cost prohibitive. They estimated that the annual operational costs are estimated to be at 1.9 billion RMB (261 million USD) for a traditional zinc metallurgy plant and 1.67 billion RMB (219 million USD) for optical zinc metallurgy. This economic analysis showed the feasibility of optical metallurgy as an alternative to traditional, high-temperature roasting techniques.
Looking ahead, researchers are hoping to find ways to bring optical metallurgy to scale. “The technology is currently in laboratorial level. A huge amount of further studies are needed to realize, for the optical zinc metallurgy technology, the pilot scale plant and final the full-scale plant,” said Duan.
Other contributors include Ying Chen, Linhua Jiang, Fuyuan Xu, Yao Wang, Wen Cheng, and Yanli Xu of the College of Environmental Science and Engineering at Tongji University; Guangbin Zhu of the School of Environmental Science and Engineering at Tianjin University; and Yong Liu at the School of Materials Science and Engineering at Anhui University of Science and Technology.
The National Natural Science Foundation of China and the Fundamental Research Funds for the Central University, Tongji University supported this research.
JOURNAL
Frontiers of Environmental Science & Engineering
METHOD OF RESEARCH
Experimental study
SUBJECT OF RESEARCH
Not applicable
ARTICLE TITLE
Direct generation of Zn metal using laser-induced ZnS to eradicate carbon emissions from electrolysis Zn production
A new method is proposed to improve the ocean observational network in the tropical western Pacific
IN FIGURES (A) AND (B), THE COLORS DEPICT THE UNCERTAINTY LEVELS BEFORE AND AFTER APPLYING OPTIMAL OBSERVATIONS. IN FIGURE (B), THE LABELED POINTS AND NUMBERS INDICATE THE POSITIONS OF MOORING SITES AND THEIR RESPECTIVE RANKINGS.
ENSO, short for the El Niño and Southern Oscillation, is the most influential interannual oscillation, and significantly impacts global climate. The Tropical Pacific Observation System (TPOS), including the moored buoys, plays an important role in understanding, monitoring, and forecasting the ENSO events. Unfortunately, many of the moorings in the western Pacific have deteriorated, hindering our ability to model and predict ENSO accurately. To address it, an international project called TPOS 2020 has been launched. In the project, the regional observation program undertaken by China urgently needs to design a buoy layout scheme in the western Pacific.
The study aims to enhance the prediction of ENSO by strategically deploying expensive and limited moored buoys in the most favorable locations. The research team, led by Hohai University in China, introduces an innovative approach for optimal long-term array design. Their new method allows the observed region/variables to differ from the prediction target, effectively surmounting the limitations of traditional techniques.
Based on the requirements of the TPOS 2020, the study applies the new approach to identify an optimal mooring array in the western Pacific. This optimal array considerably reduces the uncertainty associated with ENSO predictions, enhancing their accuracy and reliability. Given its efficacy, the proposed approach is expected to be widely used in establishing stationary observation networks.
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See the article:
A new ensemble-based targeted observational method and its application in TPOS 2020
