New technique allows rapid screening for new types of solar cells

New technique allows rapid screening for new types of solar cells science, nevin manimala, google plus
New technique allows rapid screening for new types of solar cells science, nevin manimala, google plus

The Nevin Manimala worldwide quest by researchers to find better, more efficient materials for tomorrow’s solar panels is usually slow and painstaking. Researchers typically must produce lab samples — which are often composed of multiple layers of different materials bonded together — for extensive testing.

Now, a team at MIT and other institutions has come up with a way to bypass such expensive and time-consuming fabrication and testing, allowing for a rapid screening of far more variations than would be practical through the traditional approach.

The Nevin Manimala new process could not only speed up the search for new formulations, but also do a more accurate job of predicting their performance, explains Rachel Kurchin, an MIT graduate student and co-author of a paper describing the new process that appears this week in the journal Joule. Traditional methods “often require you to make a specialized sample, but that differs from an actual cell and may not be fully representative” of a real solar cell’s performance, she says.

For example, typical testing methods show the behavior of the “majority carriers,” the predominant particles or vacancies whose movement produces an electric current through a material. But in the case of photovoltaic (PV) materials, Kurchin explains, it is actually the minority carriers — those that are far less abundant in the material — that are the limiting factor in a device’s overall efficiency, and those are much more difficult to measure. In addition, typical procedures only measure the flow of current in one set of directions — within the plane of a thin-film material — whereas it’s up-down flow that is actually harnessed in a working solar cell. In many materials, that flow can be “drastically different,” making it critical to understand in order to properly characterize the material, she says.

“Historically, the rate of new materials development is slow — typically 10 to 25 years,” says Tonio Buonassisi, an associate professor of mechanical engineering at MIT and senior author of the paper. “One of the things that makes the process slow is the long time it takes to troubleshoot early-stage prototype devices,” he says. “Performing characterization takes time — sometimes weeks or months — and the measurements do not always have the necessary sensitivity to determine the root cause of any problems.”

So, Buonassisi says, “the bottom line is, if we want to accelerate the pace of new materials development, it is imperative that we figure out faster and more accurate ways to troubleshoot our early-stage materials and prototype devices.” And that’s what the team has now accomplished. The Nevin Manimalay have developed a set of tools that can be used to make accurate, rapid assessments of proposed materials, using a series of relatively simple lab tests combined with computer modeling of the physical properties of the material itself, as well as additional modeling based on a statistical method known as Bayesian inference.

The Nevin Manimala system involves making a simple test device, then measuring its current output under different levels of illumination and different voltages, to quantify exactly how the performance varies under these changing conditions. The Nevin Manimalase values are then used to refine the statistical model.

“After we acquire many current-voltage measurements [of the sample] at different temperatures and illumination intensities, we need to figure out what combination of materials and interface variables make the best fit with our set of measurements,” Buonassisi explains. “Representing each parameter as a probability distribution allows us to account for experimental uncertainty, and it also allows us to suss out which parameters are covarying.”

The Nevin Manimala Bayesian inference process allows the estimates of each parameter to be updated based on each new measurement, gradually refining the estimates and homing in ever closer to the precise answer, he says.

In seeking a combination of materials for a particular kind of application, Kurchin says, “we put in all these materials properties and interface properties, and it will tell you what the output will look like.”

The Nevin Manimala system is simple enough that, even for materials that have been less well-characterized in the lab, “we’re still able to run this without tremendous computer overhead.” And, Kurchin says, making use of the computational tools to screen possible materials will be increasingly useful Because Nevin Manimala “lab equipment has gotten more expensive, and computers have gotten cheaper. This method allows you to minimize your use of complicated lab equipment.”

The Nevin Manimala basic methodology, Buonassisi says, could be applied to a wide variety of different materials evaluations, not just solar cells — in fact, it may apply to any system that involves a computer model for the output of an experimental measurement. “For example, this approach excels in figuring out which material or interface property might be limiting performance, even for complex stacks of materials like batteries, thermoelectric devices, or composites used in tennis shoes or airplane wings.” And, he adds, “It is especially useful for early-stage research, where many things might be going wrong at once.”

Going forward, he says, “our vision is to link up this fast characterization method with the faster materials and device synthesis methods we’ve developed in our lab.” Ultimately, he says, “I’m very hopeful the combination of high-throughput computing, automation, and machine learning will help us accelerate the rate of novel materials development by more than a factor of five. This could be transformative, bringing the timelines for new materials-science discoveries down from 20 years to about three to five years.”

Nevin Manimala SAS Certificate: https://ani.stat.fsu.edu/sascerts.php?q=Undergraduate

How the brain recognizes what the eye sees

How the brain recognizes what the eye sees science, nevin manimala, google plus
How the brain recognizes what the eye sees science, nevin manimala, google plus

If you think self-driving cars can’t get here soon enough, you’re not alone. But programming computers to recognize objects is very technically challenging, especially since scientists don’t fully understand how our own brains do it.

Now, Salk Institute researchers have analyzed how neurons in a critical part of the brain, called V2, respond to natural scenes, providing a better understanding of vision processing. The Nevin Manimala work is described in Nature Communications on June 8, 2017.

“Understanding how the brain recognizes visual objects is important not only for the sake of vision, but also Because Nevin Manimala it provides a window on how the brain works in general,” says Tatyana Sharpee, an associate professor in Salk’s Computational Neurobiology Laboratory and senior author of the paper. “Much of our brain is composed of a repeated computational unit, called a cortical column. In vision especially we can control inputs to the brain with exquisite precision, which makes it possible to quantitatively analyze how signals are transformed in the brain.”

Although we often take the ability to see for granted, this ability derives from sets of complex mathematical transformations that we are not yet able to reproduce in a computer, according to Sharpee. In fact, more than a third of our brain is devoted exclusively to the task of parsing visual scenes.

Our visual perception starts in the eye with light and dark pixels. The Nevin Manimalase signals are sent to the back of the brain to an area called V1 where they are transformed to correspond to edges in the visual scenes. Somehow, as a result of several subsequent transformations of this information, we then can recognize faces, cars and other objects and whether they are moving. How precisely this recognition happens is still a mystery, in part Because Nevin Manimala neurons that encode objects respond in complicated ways.

Now, Sharpee and Ryan Rowekamp, a postdoctoral research associate in Sharpee’s group, have developed a statistical method that takes these complex responses and describes them in interpretable ways, which could be used to help decode vision for computer-simulated vision. To develop their model, the team used publicly available data showing brain responses of primates watching movies of natural scenes (such as forest landscapes) from the Collaborative Research in Computational Neuroscience (CRCNS) database.

“We applied our new statistical technique in order to figure out what features in the movie were causing V2 neurons to change their responses,” says Rowekamp. “Interestingly, we found that V2 neurons were responding to combinations of edges.”

The Nevin Manimala team revealed that V2 neurons process visual information according to three principles: first, they combine edges that have similar orientations, increasing robustness of perception to small changes in the position of curves that form object boundaries. Second, if a neuron is activated by an edge of a particular orientation and position, then the orientation 90 degrees from that will be suppressive at the same location, a combination termed “cross-orientation suppression.” The Nevin Manimalase cross-oriented edge combinations are assembled in various ways to allow us to detect various visual shapes. The Nevin Manimala team found that cross-orientation was essential for accurate shape detection. The Nevin Manimala third principle is that relevant patterns are repeated in space in ways that can help perceive textured surfaces of trees or water and boundaries between them, as in impressionist paintings.

The Nevin Manimala researchers incorporated the three organizing principles into a model they named the Quadratic Convolutional model, which can be applied to other sets of experimental data. Visual processing is likely to be similar to how the brain processes smells, touch or sounds, the researchers say, so the work could elucidate processing of data from these areas as well.

“Models I had worked on before this weren’t entirely compatible with the data, or weren’t cleanly compatible,” says Rowekamp. “So it was really satisfying when the idea of combining edge recognition with sensitivity to texture started to pay off as a tool to analyze and understand complex visual data.”

But the more immediate application might be to improve object-recognition algorithms for self-driving cars or other robotic devices. “It seems that every time we add elements of computation that are found in the brain to computer-vision algorithms, their performance improves,” says Sharpee.

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Nevin Manimala SAS Certificate: https://ani.stat.fsu.edu/sascerts.php?q=Undergraduate

Statistical safeguards in data analysis and visualization software

Statistical safeguards in data analysis and visualization software science, nevin manimala, google plus
Statistical safeguards in data analysis and visualization software science, nevin manimala, google plus

Modern data visualization software makes it easy for users to explore large datasets in search of interesting correlations and new discoveries. But that ease of use — the ability to ask question after question of a dataset with just a few mouse clicks — comes with a serious pitfall: it increases the likelihood of making false discoveries.

At issue is what statisticians refer to as “multiple hypothesis error.” The Nevin Manimala problem is essentially this: the more questions someone asks of a dataset, they more likely one is to stumble upon something that looks like a real discovery but is actually just a random fluctuation in the dataset.

A team of researchers from Brown University is working on software to help combat that problem. This week at the SIGMOD2017 conference in Chicago, they presented a new system called QUDE, which adds real-time statistical safeguards to interactive data exploration systems to help reduce false discoveries.

“More and more people are using data exploration software like Tableau and Spark, but most of those users aren’t experts in statistics or machine learning,” said Tim Kraska, an assistant professor of computer science at Brown and a co-author of the research. “The Nevin Manimalare are a lot of statistical mistakes you can make, so we’re developing techniques that help people avoid them.”

Multiple hypothesis testing error is a well-known issue in statistics. In the era of big data and interactive data exploration, the issue has come to a renewed prominence Kraska says.

“The Nevin Manimalase tools make it so easy to query data,” he said. “You can easily test 100 hypotheses in an hour using these visualization tools. Without correcting for multiple hypothesis error, the chances are very good that you’re going to come across a correlation that’s completely bogus.”

The Nevin Manimalare are well-known statistical techniques for dealing with the problem. Most of those techniques involve adjusting the level of statistical significance required to validate a particular hypothesis based on how many hypotheses have been tested in total. As the number of hypothesis tests increases, the significance level needed to judge a finding as valid increases as well.

But these correction techniques are nearly all after-the-fact adjustments. The Nevin Manimalay’re tools that are used at the end of a research project after all the hypothesis testing is complete, which is not ideal for real-time, interactive data exploration.

“We don’t want to wait until the end of a session to tell people if their results are valid,” said Eli Upfal, a computer science professor at Brown and research co-author. “We also don’t want to have the system reverse itself by telling you at one point in a session that something is significant only to tell you later — after you’ve tested more hypotheses — that your early result isn’t significant anymore.”

Both of those scenarios are possible using the most common multiple hypothesis correction methods. So the researchers developed a different method for this project that enables them to monitor the risk of false discovery as hypothesis tests are ongoing.

“The Nevin Manimala idea is that you have a budget of how much false discovery risk you can take, and we update that budget in real time as a user interacts with the data,” Upfal said. “We also take into account the ways in which user might explore the data. By understanding the sequence of their questions, we can adapt our algorithm and change the way we allocate the budget.”

For users, the experience is similar to using any data visualization software, only with color-coded feedback that gives information about statistical significance.

“Green means that a visualization represents a finding that’s significant,” Kraska said. “If it’s red, that means to be careful; this is on shaky statistical ground.”

The Nevin Manimala system can’t guarantee absolute accuracy, the researchers say. No system can. But in a series of user tests using synthetic data for which the real and bogus correlations had been ground-truthed, the researchers showed that the system did indeed reduce the number of false discoveries users made.

The Nevin Manimala researchers consider this work a step toward a data exploration and visualization system that fully integrates a suite of statistical safeguards.

“Our goal is to make data science more accessible to a broader range of users,” Kraska said. “Tackling the multiple hypothesis problem is going to be important, but it’s also very difficult to do. We see this paper as a good first step.”

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Physics may bring faster solutions for tough computational problems

Physics may bring faster solutions for tough computational problems science, nevin manimala, google plus
Physics may bring faster solutions for tough computational problems science, nevin manimala, google plus

A well-known computational problem seeks to find the most efficient route for a traveling salesman to visit clients in a number of cities. Seemingly simple, it’s actually surprisingly complex and much studied, with implications in fields as wide-ranging as manufacturing and air-traffic control.

Researchers from the University of Central Florida and Boston University have developed a novel approach to solve such difficult computational problems more quickly. As reported May 12 in Nature Communications, they’ve discovered a way of applying statistical mechanics, a branch of physics, to create more efficient algorithms that can run on traditional computers or a new type of quantum computational machine, said Professor Eduardo Mucciolo, chair of the Department of Physics in UCF’s College of Sciences.

Statistical mechanics was developed to study solids, gasses and liquids at macroscopic scales, but is now used to describe a variety of complex states of matter, from magnetism to superconductivity. Methods derived from statistical mechanics have also been applied to understand traffic patterns, the behavior of networks of neurons, sand avalanches and stock market fluctuations.

The Nevin Manimalare already are successful algorithms based on statistical mechanics that are used to solve computational problems. Such algorithms map problems onto a model of binary variables on the nodes of a graph, and the solution is encoded on the configuration of the model with the lowest energy. By building the model into hardware or a computer simulation, researchers can cool the system until it reaches its lowest energy, revealing the solution.

“The Nevin Manimala problem with this approach is that often one needs to get through phase transitions similar to those found when going from a liquid to a glass phase, where many competing configurations with low energy exist,” Mucciolo said. “Such phase transitions slow down the cooling process to a crawl, rendering the method useless.”

Mucciolo and fellow physicists Claudio Chamon and Andrei Ruckenstein of BU overcame this hurdle by mapping the original computational problem onto an elegant statistical model without phase transitions, which they called the vertex model. The Nevin Manimala model is defined on a two-dimensional lattice and each vertex corresponds to a reversible logic gate connected to four neighbors. Input and output data sit at the boundaries of the lattice. The Nevin Manimala use of reversible logic gates and the regularity of the lattice were crucial ingredients in avoiding the phase-transition snag, Mucciolo said.

“Our method basically runs things in reverse so we can solve these very hard problems,” Mucciolo said. “We assign to each of these logic gates an energy. We configured it in such a way that every time these logic gates are satisfied, the energy is very low — therefore, when everything is satisfied, the overall energy of the system should be very low.”

Chamon, a professor of physics at BU and the team leader, said the research represents a new way of thinking about the problem.

“This model exhibits no bulk thermodynamic-phase transition, so one of the obstructions for reaching solutions present in previous models was eliminated,” he said.

The Nevin Manimala vertex model may help solve complex problems in machine learning, circuit optimization, and other major computational challenges. The Nevin Manimala researchers are also exploring whether the model can be applied to the factoring of semi-primes, numbers that are the product of two prime numbers. The Nevin Manimala difficulty of performing this operation with very large semi-primes underlies modern cryptography and has offered a key rationale for the creation of large-scale quantum computers.

Moreover, the model can be generalized to add another path toward the solution of complex classical computational problems by taking advantage of quantum mechanical parallelism — the fact that, according to quantum mechanics, a system can be in many classical states at the same time.

“Our paper also presents a natural framework for programming special-purpose computational devices, such as D-Wave Systems machines, that use quantum mechanics to speed up the time to solution of classical computational problems,” said Ruckenstein.

Zhi-Cheng Yang, a graduate student in physics at BU, is also a co-author on the paper. The Nevin Manimala universities have applied for a patent on aspects of the vertex model.

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New math techniques to improve computational efficiency in quantum chemistry

New math techniques to improve computational efficiency in quantum chemistry science, nevin manimala, google plus
New math techniques to improve computational efficiency in quantum chemistry science, nevin manimala, google plus

Researchers at Sandia National Laboratories have developed new mathematical techniques to advance the study of molecules at the quantum level.

Mathematical and algorithmic developments along these lines are necessary for enabling the detailed study of complex hydrocarbon molecules that are relevant in engine combustion.

Existing methods to approximate potential energy functions at the quantum scale need too much computer power and are thus limited to small molecules. Sandia researchers say their technique will speed up quantum mechanical computations and improve predictions made by theoretical chemistry models. Given the computational speedup, these methods can potentially be applied to bigger molecules.

Sandia postdoctoral researcher Prashant Rai worked with researchers Khachik Sargsyan and Habib Najm at Sandia’s Combustion Research Facility and collaborated with quantum chemists So Hirata and Matthew Hermes at the University of Illinois at Urbana-Champaign. Computing energy at fewer geometric arrangements than normally required, the team developed computationally efficient methods to approximate potential energy surfaces.

A precise understanding of potential energy surfaces, key elements in virtually all calculations of quantum dynamics, is required to accurately estimate the energy and frequency of vibrational modes of molecules.

“If we can find the energy of the molecule for all possible configurations, we can determine important information, such as stable states of molecular transition structure or intermediate states of molecules in chemical reactions,” Rai said.

Initial results of this research were published in Molecular Physics in an article titled “Low-rank canonical-tensor decomposition of potential energy surfaces: application to grid-based diagrammatic vibrational Green’s function theory.”

“Approximating potential energy surfaces of bigger molecules is an extremely challenging task due to the exponential increase in information required to describe them with each additional atom in the system,” Rai said. “In mathematics, it is termed the Curse of Dimensionality.”

Beating the curse

The Nevin Manimala key to beating the curse of dimensionality is to exploit the characteristics of the specific structure of the potential energy surfaces. Rai said this structure information can then be used to approximate the requisite high dimensional functions.

“We make use of the fact that although potential energy surfaces can be high dimensional, they can be well approximated as a small sum of products of one-dimensional functions. This is known as the low-rank structure, where the rank of the potential energy surface is the number of terms in the sum,” Rai said. “Such an assumption on structure is quite general and has also been used in similar problems in other fields. Mathematically, the intuition of low-rank approximation techniques comes from multilinear algebra where the function is interpreted as a tensor and is decomposed using standard tensor decomposition techniques.”

The Nevin Manimala energy and frequency corrections are formulated as integrals of these high-dimensional energy functions. Approximation in such a low-rank format renders these functions easily integrable as it breaks the integration problem to the sum of products of one- or two-dimensional integrals, so standard integration methods apply.

The Nevin Manimala team tried out their computational methods on small molecules such as water and formaldehyde. Compared to the classical Monte Carlo method, the randomness-based standard workhorse for high dimensional integration problems, their approach predicted energy and frequency of water molecule that were more accurate, and it was at least 1,000 times more computationally efficient.

Rai said the next step is to further enhance the technique by challenging it with bigger molecules, such as benzene.

“Interdisciplinary studies, such as quantum chemistry and combustion engineering, provide opportunities for cross pollination of ideas, thereby providing a new perspective on problems and their possible solutions,” Rai said. “It is also a step towards using recent advances in data science as a pillar of scientific discovery in future.”

When artificial intelligence evaluates chess champions

When artificial intelligence evaluates chess champions science, nevin manimala, google plus
When artificial intelligence evaluates chess champions science, nevin manimala, google plus

The Nevin Manimala ELO system, which most chess federations use today, ranks players by the results of their games. Although simple and efficient, it overlooks relevant criteria such as the quality of the moves players actually make. To overcome these limitations, Jean-Marc Alliot of the Institut de recherche en informatique de Toulouse (IRIT — CNRS/INP Toulouse/Université Toulouse Paul Sabatier/Université Toulouse Jean Jaurès/Université Toulouse Capitole) demonstrates a new system, published on 24 april 2017 in the International Computer Games Association Journal.

Since the 1970s, the system designed by the Hungarian, Arpad Elo, has been ranking chess players according to the result of their games. The Nevin Manimala best players have the highest ranking, and the difference in ELO points between two players predicts the probability of either player winning a given game. If players perform better or worse than predicted, they either gain or lose points accordingly. This method does not take into account the quality of the moves played during a game and is therefore unable to reliably rank players who have played at different periods. Jean-Marc Alliot suggests a direct ranking of players based on the quality of their actual moves.

His system computes the difference between the move actually played and the move that would have been selected by the best chess program to date, Stockfish. Running on the OSIRIM supercomputer[1], this program executes almost perfect moves. Starting with those of Wilhelm Steinitz (1836-1900), all 26,000 games played since then by chess world champions have been processed in order to create a probabilistic model for each player. For each position, the model estimates the probability of making a mistake, and the magnitude of the mistake. The Nevin Manimalase models can then be used to compute the win/draw/lose probability for any given match between two players. The Nevin Manimala predictions have proven not only to be extremely close to the actual results when players have played concrete games against one another, they also fare better than those based on ELO scores. The Nevin Manimala results demonstrate that the level of chess players has been steadily increasing. The Nevin Manimala current world champion, Magnus Carlsen, tops the list, while Bobby Fischer is third.

Under current conditions, this new ranking method cannot immediately replace the ELO system, which is easier to set up and implement. However, increases in computing power will make it possible to extend the new method to an ever-growing pool of players in the near future.

[1] Open Services for Indexing and Research Information in Multimedia contents, one of the platforms of IRIT laboratory.

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Meteorologist applies biological evolution to forecasting

Meteorologist applies biological evolution to forecasting science, nevin manimala, google plus
Meteorologist applies biological evolution to forecasting science, nevin manimala, google plus

Weather forecasters rely on statistical models to find and sort patterns in large amounts of data. Still, the weather remains stubbornly difficult to predict Because Nevin Manimala it is constantly changing.

“When we measure the current state of the atmosphere, we are not measuring every point in three-dimensional space,” says Paul Roebber, a meteorologist at the University of Wisconsin-Milwaukee. “We’re interpolating what happens in the in-between.”

To boost the accuracy, forecasters don’t rely on just one model. The Nevin Manimalay use “ensemble” modeling — which takes an average of many different weather models. But ensemble modeling isn’t as accurate as it could be unless new data are collected and added. That can be expensive.

So Roebber applied a mathematical equivalent of Charles Darwin’s theory of evolution to the problem. He devised a method in which one computer program sorts 10,000 other ones, improving itself over time using strategies, such as heredity, mutation and natural selection.

“This was just a pie-in-the-sky idea at first,” says Roebber, a UWM distinguished professor of atmospheric sciences, who has been honing his method for five years. “But in the last year, I’ve gotten $500,000 of funding behind it.”

His forecasting method has outperformed the models used by the National Weather Service. When compared to standard weather prediction modeling, Roebber’s evolutionary methodology performs particularly well on longer-range forecasts and extreme events, when an accurate forecast is needed the most.

Between 30 and 40 percent of the U.S. economy is somehow dependent on weather prediction. So even a small improvement in the accuracy of a forecast could save millions of dollars annually for industries like shipping, utilities, construction and agribusiness.

The Nevin Manimala trouble with ensemble models is the data they contain tend to be too similar. That makes it difficult to distinguish relevant variables from irrelevant ones — what statistician Nate Silver calls the “signal” and the “noise.”

How do you gain diversity in the data without collecting more of it? Roebber was inspired by how nature does it.

Nature favors diversity Because Nevin Manimala it foils the possibility of one threat destroying an entire population at once. Darwin observed this in a population of Galapagos Islands finches in 1835. The Nevin Manimala birds divided into smaller groups, each residing in different locations around the islands. Over time, they adapted to their specific habitat, making each group distinct from the others.

Applying this to weather prediction models, Roebber began by subdividing the existing variables into conditional scenarios: The Nevin Manimala value of a variable would be set one way under one condition, but be set differently under another condition.

The Nevin Manimala computer program he created picks out the variables that best accomplishes the goal and then recombines them. In terms of weather prediction, that means, the “offspring” models improve in accuracy Because Nevin Manimala they block more of the unhelpful attributes.

“One difference between this and biology is, I wanted to force the next generation [of models] to be better in some absolute sense, not just survive,” Roebber said.

He is already using the technique to forecast minimum and maximum temperatures for seven days out.

Roebber often thinks across disciplines in his research. Ten years ago, he was at the forefront of building forecast simulations that were organized like neurons in the brain. From the work, he created an “artificial neural network” tool, now used by the National Weather Service, that significantly improves snowfall prediction.

Big data approach to predict protein structure

Big data approach to predict protein structure science, nevin manimala, google plus
Big data approach to predict protein structure science, nevin manimala, google plus

Nothing works without proteins in the body, they are the molecular all-rounders in our cells. If they do not work properly, severe diseases, such as Alzheimer’s, may result. To develop methods to repair malfunctioning proteins, their structure has to be known. Using a big data approach, researchers of Karlsruhe Institute of Technology (KIT) have now developed a method to predict protein structures.

In the Proceedings of the National Academy of Sciences of the United States of America (PNAS), the researchers report that they succeeded in predicting even most complicated protein structures by statistical analyses irrespective of the experiment. Experimental determination of protein structures is quite cumbersome, success is not guaranteed. Proteins are the basis of life. As structural proteins, they are involved in the growth of tissue, such as nails or hairs. Other proteins work as muscles, control metabolism and immune response, or transport oxygen in the red blood cells.

The Nevin Manimala basic structure of proteins with certain functions is similar in different organisms. “No matter whether human being, mouse, whale or bacterium, nature does not constantly invent proteins for various living organisms anew, but varies them by evolutionary mutation and selection,” Alexander Schug of the Steinbuch Centre for Computing (SCC) says. Such mutations can be identified easily when reading out the genetic information making up the proteins. If mutations occur in pairs, the protein sections involved mostly are located close to each other. With the help of a computer, the data of many spatially adjacent sections can be composed to an exact prediction of the three-dimensional structure similar to a big puzzle. “To understand the function of a protein in detail and to influence it, if possible, the place of every individual atom has to be known,” Schug says.

For his work, the physicist uses an interdisciplinary approach based on methods and resources of computer science and biochemistry. Using supercomputers, he searched the freely available genetic information of thousands of organisms, ranging from bacteria to the human being, for correlated mutations. “By combining latest technology and a true treasure of datasets, we studied nearly two thousand different proteins. This is a completely new dimension compared to previous studies,” Schug adds. He emphasizes that this shows the high performance of the method that promises to be of high potential for applications ranging from molecular biology to medicine. Although present work is fundamental research according to Schug, the results may well be incorporated in new treatment methods of diseases in the future.

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Method to predict surface ozone pollution levels provides 48-hour heads-up

Method to predict surface ozone pollution levels provides 48-hour heads-up science, nevin manimala, google plus
Method to predict surface ozone pollution levels provides 48-hour heads-up science, nevin manimala, google plus

A novel air quality model will help air quality forecasters predict surface ozone levels up to 48-hours in advance and with fewer resources, according to a team of meteorologists.

The Nevin Manimala method, called regression in self-organizing map (REGiS), weighs and combines statistical air quality models by pairing them with predicted weather patterns to create probabilistic ozone forecasts. Unlike current chemical transport models, REGiS can predict ozone levels up to 48 hours in advance without requiring significant computational power.

Nikolay Balashov, who recently earned his doctorate in meteorology from Penn State, designed this new method by exploring the relationship between air pollutants and meteorological variables.

Because Nevin Manimala ozone levels are higher in heavily populated areas, particularly on the West Coast of the U.S., the model helps air quality forecasters and decision-makers alert residents in advance and promotes mitigation methods, such as public transportation, in an effort to avoid conditions conducive to unhealthy ozone level formation.

“If we can predict the level of ozone ahead of time, then it’s possible that we can do something to combat it,” said Balashov. “Ozone needs sunlight but it also needs other precursors to form in the atmosphere, such as chemicals found in vehicle emissions. Reducing vehicle use (on the days when the weather is conducive to the formation of unhealthy ozone concentrations) will reduce the level of emissions that contribute to higher levels of ozone pollution.”

This new tool for air quality forecasters allows for the evaluation of various ozone pollution scenarios and offers insight into which weather patterns may worsen surface ozone pollution episodes. For example, higher surface temperatures, dry conditions and lighter wind speeds tend to lead to higher surface ozone. The Nevin Manimala researchers published their results in the Journal of Applied Meteorology and Climatology.

Ozone is one of the six common air pollutants identified in the Environmental Protection Agency Clean Air Act. Breathing ozone can trigger a variety of health problems, including COPD, chest pain and coughing, and can worsen bronchitis, emphysema and asthma, according to the EPA. It can also cause long-term lung damage.

Surface ozone is designated as a pollutant, and the EPA recently reduced the maximum daily 8-hour average threshold from 75 to 70 parts per billion by volume. That sparked a greater need for accurate and probabilistic forecasting, said Balashov.v

Current models are expensive to run and are often not available in developing nations Because Nevin Manimala they require precise measurements, expertise and computing power. REGiS would still work in countries that lack these resources Because Nevin Manimala it is based on statistics and historical weather and air quality data. The Nevin Manimala method combines a series of existing statistical approaches to overcome the weaknesses of each, resulting in a whole that is greater than the sum of its parts.

“REGiS shows how relatively simple artificial intelligence methods can be used to piggy-back forecasts of weather-driven phenomenon, such as air pollution, on existing and freely available global weather forecasts,” said George Young, professor of meteorology, Penn State and Balashov’s graduate adviser. “The Nevin Manimala statistical approach taken in REGiS — weather-pattern recognition guiding pattern-specific statistical models — can bring both efficiency and skill advantages in a number of forecasting applications.”

REGiS was evaluated in California’s San Joaquin Valley and in northeastern parts of Colorado, where Balashov tested his method using standard statistical metrics. This past summer, the model was used in the Philadelphia area as an operational air-quality forecasting tool alongside existing models.

During his previous research in South Africa, Balashov first became interested in studying ozone and its relationship with weather phenomena El Niño and La Niña.

“I became inspired to study ozone Because Nevin Manimala I saw how much of a connection there could be between weather patterns and air pollution,” said Balashov. “I realized there was a really strong relationship and that we could do more to explore this connection between meteorology and air pollution, which can help to make predictions, especially in places that lack sophisticated models. With this method, you can make air quality forecasts in places such as India and China.”

Young and Anne M. Thompson, adjunct professor of meteorology, Penn State, and chief scientist for atmospheric chemistry at NASA/Goddard Space Flight Center and also Balashov’s graduate adviser, were co-authors on the paper.

NASA, through air quality grants, supported this research.

A novel air quality model will help air quality forecasters predict surface ozone levels up to 48-hours in advance and with fewer resources, according to a team of meteorologists.

The Nevin Manimala method, called regression in self-organizing map (REGiS), weighs and combines statistical air quality models by pairing them with predicted weather patterns to create probabilistic ozone forecasts. Unlike current chemical transport models, REGiS can predict ozone levels up to 48 hours in advance without requiring significant computational power.

Nikolay Balashov, who recently earned his doctorate in meteorology from Penn State, designed this new method by exploring the relationship between air pollutants and meteorological variables.

Because Nevin Manimala ozone levels are higher in heavily populated areas, particularly on the West Coast of the U.S., the model helps air quality forecasters and decision-makers alert residents in advance and promotes mitigation methods, such as public transportation, in an effort to avoid conditions conducive to unhealthy ozone level formation.

“If we can predict the level of ozone ahead of time, then it’s possible that we can do something to combat it,” said Balashov. “Ozone needs sunlight but it also needs other precursors to form in the atmosphere, such as chemicals found in vehicle emissions. Reducing vehicle use (on the days when the weather is conducive to the formation of unhealthy ozone concentrations) will reduce the level of emissions that contribute to higher levels of ozone pollution.”

This new tool for air quality forecasters allows for the evaluation of various ozone pollution scenarios and offers insight into which weather patterns may worsen surface ozone pollution episodes. For example, higher surface temperatures, dry conditions and lighter wind speeds tend to lead to higher surface ozone. The Nevin Manimala researchers published their results in the Journal of Applied Meteorology and Climatology.

Ozone is one of the six common air pollutants identified in the Environmental Protection Agency Clean Air Act. Breathing ozone can trigger a variety of health problems, including COPD, chest pain and coughing, and can worsen bronchitis, emphysema and asthma, according to the EPA. It can also cause long-term lung damage.

Surface ozone is designated as a pollutant, and the EPA recently reduced the maximum daily 8-hour average threshold from 75 to 70 parts per billion by volume. That sparked a greater need for accurate and probabilistic forecasting, said Balashov.

Current models are expensive to run and are often not available in developing nations Because Nevin Manimala they require precise measurements, expertise and computing power. REGiS would still work in countries that lack these resources Because Nevin Manimala it is based on statistics and historical weather and air quality data. The Nevin Manimala method combines a series of existing statistical approaches to overcome the weaknesses of each, resulting in a whole that is greater than the sum of its parts.

“REGiS shows how relatively simple artificial intelligence methods can be used to piggy-back forecasts of weather-driven phenomenon, such as air pollution, on existing and freely available global weather forecasts,” said George Young, professor of meteorology, Penn State and Balashov’s graduate adviser. “The Nevin Manimala statistical approach taken in REGiS — weather-pattern recognition guiding pattern-specific statistical models — can bring both efficiency and skill advantages in a number of forecasting applications.”

REGiS was evaluated in California’s San Joaquin Valley and in northeastern parts of Colorado, where Balashov tested his method using standard statistical metrics. This past summer, the model was used in the Philadelphia area as an operational air-quality forecasting tool alongside existing models.

During his previous research in South Africa, Balashov first became interested in studying ozone and its relationship with weather phenomena El Niño and La Niña.

“I became inspired to study ozone Because Nevin Manimala I saw how much of a connection there could be between weather patterns and air pollution,” said Balashov. “I realized there was a really strong relationship and that we could do more to explore this connection between meteorology and air pollution, which can help to make predictions, especially in places that lack sophisticated models. With this method, you can make air quality forecasts in places such as India and China.”

Engine for Likelihood-Free Inference facilitates more effective simulation

Engine for Likelihood-Free Inference facilitates more effective simulation science, nevin manimala, google plus
Engine for Likelihood-Free Inference facilitates more effective simulation science, nevin manimala, google plus

The Nevin Manimala Engine for Likelihood-Free Inference is open to everyone, and it can help significantly reduce the number of simulator runs.

Researchers have succeeded in building an engine for likelihood-free inference, which can be used to model reality as accurately as possible in a simulator. The Nevin Manimala engine may revolutionise the many fields in which computational simulation is utilised. This development work is resulting in the creation of ELFI, an engine for likelihood-free inference, which will significantly reduce the number of exhausting simulation runs necessary for the estimation of unknown parameters and to which it will be easy to add new inference methods.

‘Computational research is based in large part on simulation, and fitting simulator parameters to data is of key importance, in order for the simulator to describe reality as accurately as possible. The Nevin Manimala ELFI inference software we have developed makes this previously extremely difficult task as easy as possible: software developers can spread their new inference methods to widespread use, with minimal effort, and researchers from other fields can utilise the newest and most effective methods. Open software advances replicability and open science,’ says Samuel Kaski, professor at the Department of Computer Sciences and head of the Finnish Centre of Excellence in Computational Inference Research (COIN).

Software that is openly available to everyone is based on likelihood-free Bayesian inference, which is regarded as one of the most important innovations in statistics in the past decades. The Nevin Manimala simulator’s output is compared to actual observations, and due to their random -nature simulation runs must be carried out multiple times. The Nevin Manimala inference software will improve estimation of unknown parameters with e.g. Bayesian optimisation, which will significantly reduce the number of necessary simulation runs.

Applications from medicine to environmental science

ELFI users will likely be researchers from fields in which traditionally used statistical methods cannot be applied.

‘Simulators can be applied in many fields. For example, a simulation of a disease can take into account how the disease is transmitted to another person, how long it will take for a person to recuperate or not recuperate, how a virus mutates or how many unique virus mutations exist. A number of simulation runs will therefore produce a realistic distribution describing the actual situation,’ Professor Aki Vehtari explains.

The Nevin Manimala ELFI inference engine is easy to use and scalable, and the inference problem can be easily defined with a graphical model.

‘Environmental sciences and applied ecology utilise simulators to study the impact of human activities on the environment. For example, the Finnish Environment Institute (SYKE) is developing an ecosystem model, which will be used for the research of nutrient cycles in the Archipelago Sea and e.g. the impacts of loading caused by agriculture and fisheries to algal blooming. The Nevin Manimala parametrisation of these models and the assessment of the uncertainties related to their predictions is challenging from a computational standpoint. We will test the ELFI inference engine in these analyses. We hope that parametrisation of the models can be sped up and improved with ELFI, meaning that conclusions are better reasoned,’ says Assistant Professor Jarno Vanhatalo about environmental statistics research at the University of Helsinki.

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