Computers Math

New research from the Department of Sociology in Trinity College Dublin has found further evidence of a relationship between online engagement and mental wellbeing in teenagers. The study, published recently in the journal ‘Computers in Human Behaviour’, contributes to mounting international evidence on the dangers of high levels of digital media use.
Additionally, the researchers found that in today’s connected world low engagement with digital media is also associated with poor mental health outcomes for adolescents who spend less time online than their peers. This finding supports the ‘goldilocks’ hypothesis — that digital media use at moderate levels is not intrinsically harmful and there is a point between low and high use that is ‘just right’ for young people.
This is the first time the ‘goldilocks’ theory has been examined in Irish teenagers/young adults. It is also the first study to attempt the integration of both time and online behaviours when examining associations between digital media and mental wellbeing.
Professor Richard Layte, Professor of Sociology and co-author on the paper, said:
“Evidence is mounting internationally that online engagement among adolescents may be damaging for mental well-being but the evidence is mixed. Our work provides fresh insights on the impact of digital engagement at the age of 17/18 and the results provide worrying evidence of real harms that require urgent action.”
“There is a simple narrative out there that more is worse. It is important to emphasise that online engagement is now a normal channel of social participation and non-use has consequences. Our findings also raise the possibility that moderate use is important in today’s digital world and that low levels of online engagement carries its own risks. Now the questions for researchers are how much is too much and how little is too little?”
The research, drawing on longitudinal data from the Growing Up in Ireland study, looked at the association between adolescent use of online engagement and mental wellbeing in over 6,000 young people between the age of 13 and again at the age of 17/18. More

A team led by University of Minnesota Twin Cities researchers has discovered how subtle structural changes in strontium titanate, a metal oxide semiconductor, can alter the material’s electrical resistance and affect its superconducting properties.
The research can help guide future experiments and materials design related to superconductivity and the creation of more efficient semiconductors for various electronic device applications.
The study is published in Science Advances, a peer-reviewed, multidisciplinary scientific journal published by the American Association for the Advancement of Science.
Strontium titanate has been on scientists’ radar for the past 60 years because it displays many interesting properties. For one, it becomes a superconductor, i.e. conducts electricity smoothly without resistance, at low temperatures and low concentrations of electrons. It also undergoes a structure change at 110 Kelvin (-262 degrees Fahrenheit), meaning the atoms in its crystalline structure change their arrangement. However, scientists are still debating what exactly causes superconductivity in this material on the microscopic level or what happens when its structure changes.
In this study, the University of Minnesota-led team was able to shine some light on these issues.
Using a combination of materials synthesis, analysis, and theoretical modeling, the researchers found that the structural change within strontium titanate directly affects how electric current flows through the material. They also revealed how small changes in the concentrations of electrons in the material affect its superconductivity. These insights will ultimately inform future research on this material, including research on its unique superconducting properties. More

One of the most tedious, daunting tasks for undergraduate assistants in university research labs involves looking hours on end through a microscope at samples of material, trying to find monolayers.
These two-dimensional materials — less than 1/100,000th the width of a human hair — are highly sought for use in electronics, photonics, and optoelectronic devices because of their unique properties.
“Research labs hire armies of undergraduates to do nothing but look for monolayers,” says Jaime Cardenas, an assistant professor of optics at the University of Rochester. “It’s very tedious, and if you get tired, you might miss some of the monolayers or you might start making misidentifications.”
Even after all that work, the labs then must doublecheck the materials with expensive Raman spectroscopy or atomic force microscopy.
Jesús Sánchez Juárez, a PhD student in the Cardenas Lab, has made life a whole lot easier for those undergraduates, their research labs, and companies that encounter similar difficulties in detecting monolayers.
The breakthrough technology, an automated scanning device described in Optical Materials Express, can detect monolayers with 99.9 percent accuracy — surpassing any other method to date. More

Among several infectious disease terms to enter the public lexicon, superspreading events continue to make headlines years after the first cases of the COVID-19 pandemic. How features of the SARS-CoV2 virus lead to some events becoming superspreading events while leaving others relatively benign remains unresolved.
In Physics of Fluids, by AIP Publishing, researchers in Canada and the United States created a model to connect what biologists have learned about COVID-19 superspreading with how such events have occurred in the real world. They use real-world occupancy data from more than 100,000 places where people gather across 10 U.S. cities to test several features ranging from viral loads to the occupancy and ventilation of social contact settings.
They found that 80% of infections occurring at superspreading events arose from only 4% of those who were carrying the virus into the event, called index cases. The top feature driving the wide variability in superspreading events was the number of viral particles found in index cases, followed by the overall occupancy in social contact settings.
The researchers’ methods take aim at the curious observations that the variability between infection events is higher than one would expect, a situation called overdispersion.
“It is now well known that COVID-19 is airborne, and that is probably the dominant pathway of transmission,” said author Swetaprovo Chaudhuri. “This paper connects indoor airborne transmission to the evolution of the infection distribution on a population scale and shows the physics of airborne transmission is consistent with the mathematics of overdispersion.”
The group’s model draws on numerical simulations and research by others on viral loads and the number of virus-laden aerosols ejected by people, as well as data on the occupancy of a restaurant or area from SafeGraph, a company that generates such data from anonymized cell phone signals.
“While there are uncertainties and unknowns, it appears it is rather hard to prevent a superspreading event if the person carrying high viral load happens to be in a crowded place,” Chaudhuri said.
Chaudhuri said the findings not only underscore the importance of efforts to curb the spread of the virus but also help describe how integral properly planning can be for each situation.
“To mitigate such superspreading events, vaccination, ventilation, filtration, mask wearing, reduced occupancy — all are required,” he said. “However, putting them in place is not enough, knowing what size, type, parameters can mitigate risk to certain acceptable levels is important.”
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Materials provided by American Institute of Physics. Note: Content may be edited for style and length. More

A new technique developed by the National Center for Atmospheric Research (NCAR) uses artificial intelligence to efficiently update the vegetation maps that are relied on by wildfire computer models to accurately predict fire behavior and spread.
In a recent study, scientists demonstrated the method using the 2020 East Troublesome Fire in Colorado, which burned through land that was mischaracterized in fuel inventories as being healthy forest. In fact the fire, which grew explosively, scorched a landscape that had recently been ravaged by pine beetles and windstorms, leaving significant swaths of dead and downed timber.
The research team compared simulations of the fire generated by a state-of-the-art wildfire behavior model developed at NCAR using both the standard fuel inventory for the area and one that was updated with artificial intelligence (AI). The simulations that used the AI-updated fuels did a significantly better job of predicting the area burned by the fire, which ultimately grew to more than 190,000 acres of land on both sides of the continental divide.
“One of our main challenges in wildfire modeling has been to get accurate input, including fuel data,” said NCAR scientist and lead author Amy DeCastro. “In this study, we show that the combined use of machine learning and satellite imagery provides a viable solution.”
The research was funded by the U.S. National Science Foundation, which is NCAR’s sponsor. The modeling simulations were run at the NCAR-Wyoming Supercomputing Center on the Cheyenne system.
Using satellites to account for pine beetle damage
For a model to accurately simulate a wildfire, it requires detailed information about the current conditions. This includes the local weather and terrain as well as the characteristics of the plant matter that provides fuel for the flames — what’s actually available to burn and what condition it’s in. Is it dead or alive? Is it moist or dry? What type of vegetation is it? How much is there? How deep is the fuel layered on the ground? More

In recent years Alzheimer’s disease has been on the rise throughout the world and is rarely diagnosed at an early stage when it can still be effectively controlled. Using artificial intelligence, KTU researchers conducted a study to identify whether human-computer interfaces could be adapted for people with memory impairments to recognise a visible object in front of them.
Rytis Maskeliūnas, a researcher at the Department of Multimedia Engineering at Kaunas University of Technology (KTU), considers that the classification of information visible on the face is a daily human function: “While communicating, the face “tells” us the context of the conversation, especially from an emotional point of view, but can we identify visual stimuli based on brain signals?”
The visual processing of the human face is complex. Information such as a person’s identity or emotional state can be perceived by us, analysing the faces. The aim of the study was to analyse a person’s ability to process contextual information from the face and detect how a person responds to it.
Face can indicate the first symptoms of the disease
According to Maskeliūnas, many studies demonstrate that brain diseases can potentially be analysed by examining facial muscle and eye movements since degenerative brain disorders affect not only memory and cognitive functions, but also the cranial nervous system associated with the above facial (especially eye) movements.
Dovilė Komolovaitė, a graduate of KTU Faculty of Mathematics and Natural Sciences, who co-authored the study, shared that the research has clarified whether a patient with Alzheimer’s disease visually processes visible faces in the brain in the same way as individuals without the disease. More

Combinatorial optimization problems are at the root of many industrial processes and solving them is key to a more sustainable and efficient future. Ising machines can solve certain combinatorial optimization problems, but their efficiency could be improved with multi-spin flips. Researchers have now tackled this difficult problem by developing a merge algorithm that disguises a multi-spin flip as a simpler, single-spin flip. This technology provides optimal solutions to hard computational problems in a shorter time.
In a rapidly developing world, industries are always trying to optimize their operations and resources. Combinatorial optimization using an Ising machine helps solve certain operational problems, like mapping the most efficient route for a multi-city tour or optimizing delivery of resources. Ising machines operate by mapping the solution space to a spin configuration space and solving the associated spin problem instead. These machines have a wide range of applications in both academia and industry, tackling problems in machine learning, material design, portfolio optimization, logistics, and drug discovery. For larger problems, however, it is still difficult to obtain the optimal solution in a feasible amount of time.
Now, while Ising machines can be optimized by integrating multi-spin flips into their hardware, this is a challenging task because it essentially means completely overhauling the software of traditional Ising machines by changing their basic operation. But a team of researchers from the Department of Computer Science and Communications Engineering, Waseda University — consisting of Assistant Professor Tatsuhiko Shirai and Professor Nozomu Togawa — has provided a novel solution to this long-standing problem.
In their paper, which was published in IEEE Transactions on Computerson 27 May 2022, they engineered a feasible multi-spin flip algorithm by deforming the Hamiltonian (which is an energy function of the Ising model). “We have developed a hybrid algorithm that takes an infeasible multi-spin flip and expresses it in the form of a feasible single-spin flip instead. This algorithm is proposed along with our merge process, in which the original Hamiltonian of a difficult combinatorial problem is deformed into a new Hamiltonian, a problem that the hardware of a traditional Ising machine can easily solve,” explains Tatsuhiko Shirai.
The newly-developed hybrid Ising processes are fully compatible with current methods and hardware, reducing the challenges to their widespread application. “We applied the hybrid merge process to several common examples of difficult combinatorial optimization problems. Our algorithm shows superior performance in all instances. It reduces residual energy and reaches more optimal results in shorter time — it really is a win-win,” states Nozomu Togawa.
Their work will allow industries to solve new complex optimization problems and help tackle climate change-related issues such as increased energy demand, food shortage, and the realization of sustainable development goals (SDGs). “For example, we could use this to optimize shipping and delivery planning problems in industries to increase their efficiency while reducing carbon dioxide emissions,” Tatsuhiko Shirai adds.
This new technology directly increases the number of applications where the Ising machine can be feasibly used to produce solutions. As a result, the Ising machine method can be increasingly used across machine learning and optimization science. The team’s technology not only improves the performance of existing Ising machines, but also provides a blueprint to the development of new Ising machine architectures in the near future. With the merge algorithm driving Ising machines further into new uncharted territories, the future of optimization, and thus sustainability practices, looks bright.
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Materials provided by Waseda University. Note: Content may be edited for style and length. More

In today’s medicine, doctors define and diagnose most diseases on the basis of symptoms. However, that does not necessarily mean that the illnesses of patients with similar symptoms will have identical causes or demonstrate the same molecular changes. In biomedicine, one often speaks of the molecular mechanisms of a disease. This refers to changes in the regulation of genes, proteins or metabolic pathways at the onset of illness. The goal of stratified medicine is to classify patients into various subtypes at the molecular level in order to provide more targeted treatments.
To extract disease subtypes from large pools of patient data, new machine learning algorithms can help. They are designed to independently recognize patterns and correlations in extensive clinical measurements. The LipiTUM junior research group, headed by Dr. Josch Konstantin Pauling of the Chair for Experimental Bioinformatics has developed an algorithm for this purpose.
Complex analysis via automated web tool
Their method combines the results of existing algorithms to obtain more precise and robust predictions of clinical subtypes. This unifies the characteristics and advantages of each algorithm and eliminates their time-consuming adjustment. “This makes it much easier to apply the analysis in clinical research,” reports Dr. Pauling. “For that reason, we have developed a web-based tool that permits online analysis of molecular clinical data by practitioners without prior knowledge of bioinformatics.”
On the website (https://exbio.wzw.tum.de/mosbi/), researchers can submit their data for automated analysis and use the results to interpret their studies. “Another important aspect for us was the visualization of the results. Previous approaches were not capable of generating intuitive visualizations of relationships between patient groups, clinical factors and molecular signatures. This will change with the web-based visualization produced by our MoSBi tool,” says Tim Rose, a scientist at the TUM School of Life Sciences. MoSBi stands for “Molecular Signatures using Biclustering.” “Biclustering” is the name of the technology used by the algorithm.
Application for clinically relevant questions
With the tool, researchers can now, for example, represent data from cancer studies and simulations for various scenarios. They have already demonstrated the potential of their method in a large-scale clinical study. In a cooperative study conducted with researchers from the Max Planck Institute in Dresden, the Technical University of Dresden and the Kiel University Clinic, they studied the change in lipid metabolism in the liver of patients with non-alcoholic fatty liver disease (NAFLD).
This widespread disease is associated with obesity and diabetes. It develops from the non-alcoholic fatty liver (NAFL), in which lipids are deposited in liver cells, to non-alcoholic steatohepatitis (NASH), in which the liver becomes further inflamed, to liver cirrhosis and the formation of tumors. Apart from dietary adjustments, no treatments have been found to date. Because the disease is characterized and diagnosed by the accumulation of various lipids in the liver, it is important to understand their molecular composition.
Biomarkers for liver disease
Using the MoSBi methods, the researchers were able to demonstrate the heterogeneity of the livers of patients in the NAFL stage at the molecular level. “From a molecular standpoint, the liver cells of many NAFL patients were almost identical to those of NASH patients, while others were still largely similar to healthy patients. We could also confirm our predictions using clinical data,” says Dr. Pauling. “We were then able to identify two potential lipid biomarkers for disease progression.” This is important for early recognition of the disease and its progression and the development of targeted treatments.
The research group is already working on further applications of their method to gain a better understanding of other diseases. “In the future algorithms will play an even greater role in biomedical research than they already do today. They can make it significantly easier to detect complex mechanisms and find more targeted treatment approaches,” says Dr. Pauling.
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Materials provided by Technical University of Munich (TUM). Note: Content may be edited for style and length. More