Computers Math

Although solar cells are typically designed to turn light into power, researchers have shown that they can also be used to achieve underwater wireless optical communication with high data rates. The new approach — which used an array of series-connected solar cells as detectors — could offer a cost-effective, low-energy way to transmit data underwater.
“There is a critical need for efficient underwater communication to meet the increasing demands of underwater data exchange in worldwide ocean protection activities,” said research team leader Jing Xu from Zhejiang University in China. For example, in coral reef conservation efforts, data links are necessary to transmit data from divers, manned submarines, underwater sensors and unmanned autonomous underwater vehicles to surface ships supporting their work.
In the Optica Publishing Group journal Optics Letters, Xu and colleagues report on laboratory experiments in which they used an array of commercially available solar cells to create an optimized lens-free system for high-speed optical detection underwater. Solar cells offer a much larger detection area than the photodiodes traditionally used as detectors in wireless optical communication.
“To the best of our knowledge, we demonstrated the highest bandwidth ever achieved for a commercial silicon solar panel-based optical communication system with a large detection area,” said Xu. “This type of system could even allow data exchange and power generation with one device.”
Optimizing solar cells for communication
Compared to using radio or acoustic waves, light-based underwater wireless communication exhibits higher speed, lower latency and requires less power. However, most long-distance high-speed optical systems are not practical for underwater implementation because they require strict alignment between the transmitter emitting the light and the receiver that detects the incoming light signal. More

University of Wisconsin-Madison physicists have made one of the highest performance atomic clocks ever, they announced Feb. 16 in the journal Nature.
Their instrument, known as an optical lattice atomic clock, can measure differences in time to a precision equivalent to losing just one second every 300 billion years and is the first example of a “multiplexed” optical clock, where six separate clocks can exist in the same environment. Its design allows the team to test ways to search for gravitational waves, attempt to detect dark matter, and discover new physics with clocks.
“Optical lattice clocks are already the best clocks in the world, and here we get this level of performance that no one has seen before,” says Shimon Kolkowitz, a UW-Madison physics professor and senior author of the study. “We’re working to both improve their performance and to develop emerging applications that are enabled by this improved performance.”
Atomic clocks are so precise because they take advantage of a fundamental property of atoms: when an electron changes energy levels, it absorbs or emits light with a frequency that is identical for all atoms of a particular element. Optical atomic clocks keep time by using a laser that is tuned to precisely match this frequency, and they require some of the world’s most sophisticated lasers to keep accurate time.
By comparison, Kolkowitz’s group has “a relatively lousy laser,” he says, so they knew that any clock they built would not be the most accurate or precise on its own. But they also knew that many downstream applications of optical clocks will require portable, commercially available lasers like theirs. Designing a clock that could use average lasers would be a boon.
In their new study, they created a multiplexed clock, where strontium atoms can be separated into multiple clocks arranged in a line in the same vacuum chamber. Using just one atomic clock, the team found that their laser was only reliably able to excite electrons in the same number of atoms for one-tenth of a second. More

Researchers from the RIKEN Guardian Robot Project in Japan have made an android child named Nikola that successfully conveys six basic emotions. The new study, published in Frontiers in Psychology, tested how well people could identify six facial expressions — happiness, sadness, fear, anger, surprise, and disgust — which were generated by moving “muscles” in Nikola’s face. This is the first time that the quality of android-expressed emotion has been tested and verified for these six emotions.
Rosie the robot maid was considered science fiction when she debuted on the Jetson’s cartoon over 50 years ago. Although the reality of the helpful robot is currently more science and less fiction, there are still many challenges that need to be met, including being able to detect and express emotions. The recent study led by Wataru Sato from the RIKEN Guardian Robot Project focused on building a humanoid robot, or android, that can use its face to express a variety of emotions. The result is Nikola, an android head that looks like a hairless boy.
Inside Nikola’s face are 29 pneumatic actuators that control the movements of artificial muscles. Another 6 actuators control head and eyeball movements. Pneumatic actuators are controlled by air pressure, which makes the movements silent and smooth. The team placed the actuators based on the Facial Action Coding System (FACS), which has been used extensively to study facial expressions. Past research has identified numerous facial action units — such as ‘cheek raiser’ and ‘lip pucker’ — that comprise typical emotions such as happiness or disgust, and the researchers incorporated these action units in Nikola’s design.
Typically, studies of emotions, particularly how people react to emotions, have a problem. It is difficult to do a properly controlled experiment with live people interacting, but at the same time, looking at photos or videos of people is less natural, and reactions aren’t the same. “The hope is that with androids like Nikola, we can have our cake and eat it too,” says Sato. “We can control every aspect of Nikola’s behavior, and at the same time study live interactions.” The first step was to see if Nikola’s facial expressions were understandable.
A person certified in FACS scoring was able to identify each facial action unit, indicating that Nikola’s facial movements accurately resemble those of a real human. A second test showed that everyday people could recognize the six prototypical emotions — happiness, sadness, fear, anger, surprise, and disgust — in Nikola’s face, albeit to varying accuracies. This is because Nikola’s silicone skin is less elastic than real human skin and cannot form wrinkles very well. Thus, emotions like disgust were harder to identify because the action unit for nose wrinkling could not be included.
“In the short term, androids like Nikola can be important research tools for social psychology or even social neuroscience,” says Sato. “Compared with human confederates, androids are good at controlling behaviors and can facilitate rigorous empirical investigation of human social interactions.” As an example, the researchers asked people to rate the naturalness of Nikola’s emotions as the speed of his facial movements was systematically controlled. They researchers found that the most natural speed was slower for some emotions like sadness than it was for others like surprise.
While Nikola still lacks a body, the ultimate goal of the Guardian Robot Project is to build an android that can assist people, particularly those which physical needs who might live alone. “Androids that can emotionally communicate with us will be useful in a wide range of real-life situations, such as caring for older people, and can promote human wellbeing,” says Sato.
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Researchers from The University of Tokyo calculated the optimal search strategy for organisms that employ run-and-tumble motion when looking for a food’s odor. They determined that the chemotaxis demonstrated by E. coli closely resembles this system when accounting for the costs of control and the noise of the environment. This work may lead to new methods of designing chemical-seeking drones or nanobots.
The aroma of a favorite dessert can tempt almost anyone to follow the scent. By moving in the direction of increasing smell, one can often locate the desired confection. It turns out that even simple organisms, like the single-celled E. coli bacterium, can use a similar method to detect and move toward food. Now, researchers have developed a theoretical model for the best possible search strategy when searching for source of the scent, which may help in the design of new drones or nanobots that can find their own way to a chemical target.
Scientists from the Institute of Industrial Science, The University of Tokyo have studied the odor-searching strategy used by organisms ranging from bacteria to multicellular eukaryotes, which perform “chemotaxis.” Chemotaxis is the process of attraction in the direction of a chemical gradient, and it takes several forms. E. coli bacteria use the common approach called “run-and-tumble,” in which periods of forward swimming are interrupted by rotations that randomly change the direction of motion. Although linear control theory has become part of the established practice of engineering, it does not suffice to handle the nonlinearity and large noise seen in biological systems. A more tailored theory is needed to better understand this phenomenon.
The research team used stochastic optimal control theory to find the best possible fully nonlinear sensing and control strategy of run-and-tumble motion in environments with noisy chemical gradients. They modeled the internal control using a partially observable Markov decision process. In this framework, agents cannot directly observe the correct solution, but they can update their beliefs by sensing their environment.
To make the model as realistic as possible, the researchers included a control cost that represents the physical limitations of regulating when tumbling occurs. “The correspondence between our optimal solution and biochemical bacterial models demonstrates the applicability of our theoretical framework to the understanding of biological search systems,” says first author Kento Nakamura. The primary way that organisms control their motion and progressively move toward a target is by inhibiting tumbling when sensing that the chemical concentration is increasing along their current direction.
This work opens the way for new kinds of autonomous pathfinding algorithms that can be employed to find specific targets, even if their exact locations are unknown. “Understanding the internal control mechanisms of biological organisms would be helpful when designing biomimetic robots that can take advantage of these systems,” says senior author Tetsuya J. Kobayashi.
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Simulations have led to the fabrication of a polymer-DNA gel that could be used in tissue regeneration and robotics.
Scientists in Japan have made a tuneable, elastic and temperature-sensitive gel by using complementary DNA strands to connect star-shaped polymer molecules together. The gel, and the method used to develop it, could lead to advances in tissue regeneration, drug delivery and soft robotics. Xiang Li at Hokkaido University led the team of researchers who reported their findings in the journal Polymer Science.
Scientists have long been looking for better ways to develop gels that can be used in a variety of applications, including in the fields of medicine and engineering. Ideally, such gels need to be predictable in their behaviour, self-healing and durable enough for the rigorous jobs they are intended for.
“Gels are made by using bonds to link polymer molecules together,” explains Li. “When the bonds are connected, the material is more solid, and when they break in response to stress, the material turns to liquid.”
Owing to their high biocompatibility, water solubility and temperature sensitivity, DNA strands would be highly suitable for linking polymer molecules by taking advantage of their ability to form complementary bonds. However, scientists have so far found it difficult to use DNA links to develop homogeneous gels with on-demand elastic properties.
Looking to solve this problem, Li and his colleagues used software programs to simulate the formation of different DNA sequences and their complementary strands, and to determine how these double strands respond to changes in temperature. Their aim was to identify complementary DNA sequences that would only disconnect above 63°C in order to ensure a potential gel’s stability in the human body.
Based on the software simulations, they chose a pair of complementary DNA sequences to link four-armed molecules of polyethylene glycol (PEG). They prepared the gel by dissolving DNA strands and PEG separately in buffer solutions before mixing them in a test tube immersed in a hot water bath that was then cooled to ambient temperature. Finally, they conducted a series of experiments and analyses to evaluate the resulting gel’s properties.
The gel performed as predicted by the simulations, remaining elastic, self-repairing and solid until its melting temperature of 63°C over multiple testing cycles. The experiments also showed that the PEG molecules were homogeneously linked together by the DNA double strands and that liquid formation happened when the strands separated.
“Our findings suggest that we will be able to fabricate DNA gels with on-demand viscoelastic properties by making use of already available data on DNA thermodynamics and kinetics,” says Li. “The aim will be to improve the understanding and applications of this class of gel.”
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Scientists have developed mathematical models that act as patient ‘surrogates’ for evaluating potential prostate cancer treatments.
The research, published today in eLife, could ultimately help clinicians choose the most effective drug combination before they start to treat a patient, potentially improving their response and avoiding drug resistance.
Researchers used an approach called Boolean modelling, which is already used to describe dynamics of complex cell signalling processes. But existing models have been generic and have not accounted for the differences between individual patients’ diseases or how they respond to treatment.
“The dream has always been to use more and more complex models and data until we can have digital twins, or virtual humans or surrogates — a simulation that helps select the proper clinical treatment for a given patient with high degrees of specificity or sensitivity,” explains Arnau Montagud, who was a researcher at Institut Curie, Paris, France, at the time the study was carried out, and is now at the Barcelona Supercomputing Center (BSC), Spain. “We wanted to know if our method of tailoring Boolean models of cell signalling was accurate enough to discriminate between different patients, and whether the models could be used as testbeds to rank personalised drug treatments.”
To begin, the team used data from The Cancer Genome Atlas (TCGA) and other databases to create a network of all relevant pathways involved in prostate cell signalling. Then they converted this into a generic Boolean model where all the nodes in the network can be assigned one of two values — 0 (inactivated or absent) or 1 (activated or present). Data from 488 prostate cancer patients from TCGA were used to create 488 patient-specific Boolean models. For example, where a patient’s tumour had a mutation in a specific gene, this meant the node in the network was inactivated, and assigned a value of 0.
Having built these models, the team looked in each patient model for genes that, when inhibited, would block growth or encourage death of cancer cells. They narrowed these genes down to a list of targets of existing drugs, and ran simulations to predict what would happen if the drugs were combined. This allowed them to compare the effects of individual drugs on each patient, and to propose certain drugs that would work for specific patients or for groups of patients. Inactivation of some of the genes had a greater effect in some patients compared with others, highlighting opportunities for personalised drug treatments. The simulations also spotted patterns linked to the grade of patients’ tumours as measured by the Gleason score, suggesting it might be possible to tailor drug treatments to prostate cancer patients according to their score in the future.
Testing whether these treatment predictions hold true in patients would require a clinical trial, so the team instead built eight different personalised prostate cancer cell line models from publicly available data. As with the patient models, they looked for commonly occurring mutations in the cell lines that influenced cancer cell growth or death. This resulted in the identification of 17 proteins that could be targeted with drugs.
Next, to investigate if drugs targeting these proteins would have the anticipated effects, they mimicked the effect of different drug dosages in the model by switching off each node from 100% active to 0% active and looking at the effects on growth, death and spread of the cancer cells. When they carried out the same experiment in real cell lines, it confirmed that blocking the identified nodes in the model had differential effects on cell growth and survival. Moreover, the model could predict synergistic effects of treatments that work against different nodes in the network, which could help to identify promising drug combinations for future investigation.
“Our personalised models suggest single and combined drug treatments for individual prostate cancer patients,” concludes Laurence Calzone, a researcher at Institut Curie, and a co-senior author of the study alongside Julio Saez-Rodriguez from Heidelberg University, Germany. “These advances are incremental steps towards having digital twins that will help clinicians before they go to the patient’s bedside, allowing them to capture patient individualities and test and rank different drug treatments.”
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A machine-learning algorithm developed at Sandia National Laboratories could provide auto manufacturing, aerospace and other industries a faster and more cost-efficient way to test bulk materials.
The technique was published recently in the scientific journal Materials Science and Engineering: A.
Production stoppages are costly. So, manufacturers screen materials like sheet metal for formability before using them to make sure the material will not crack when it is stamped, stretched and strained as it’s formed into different parts. Companies often use commercial simulation software calibrated to the results of various mechanical tests, said Sandia scientist David Montes de Oca Zapiain, the lead author on the paper. However, these tests can take months to complete.
And while certain high-fidelity computer simulations can assess formability in only a few weeks, companies need access to a supercomputer and specialized expertise to run them, Montes de Oca Zapiain said.
Sandia has shown machine learning can dramatically cut time and resources to calibrate commercial software because the algorithm does not need information from mechanical tests, said Montes de Oca Zapiain. Nor does the method need a supercomputer. Additionally, it opens a new path to perform faster research and development.
“You could efficiently use this algorithm to potentially find lighter materials with minimal resources without sacrificing safety or accuracy,” Montes de Oca Zapiain said. More

New research from physicists at the University of Sussex will ‘significantly advance’ the new technology area of liquid electronics, enhancing the functionality and sustainability of potential applications in printed electronics, wearable health monitors and even batteries.
In their research paper published in ACS Nano, the Sussex scientists have built on their previous work to wrap emulsion droplets with graphene and other 2D materials by reducing the coatings down to atomically-thin nanosheet layers. In doing so they were able to create electrically-conducting liquid emulsions that are the lowest-loading graphene networks ever reported — just 0.001 vol%.
This means that the subsequent liquid electronic technology — whether that might be strain sensors to monitor physical performance and health, electronic devices printed from emulsion droplets, and even potentially more efficient and longer-lasting electric vehicle batteries, will be both cheaper and more sustainable because they will require less graphene or other 2D nanosheets coating the droplets.
Another significant development was that the scientists can now make these electronic droplet networks using any liquids — whereas previous research focused on conventional oils and water — because they have discovered how to control which liquid droplets are wrapped in graphene, meaning that they can design the emulsions specifically to the desired application.
Research Fellow in Material Physics in the University of Sussex School of Mathematical and Physical Science and lead author of the paper, Dr Sean Ogilvie explains the science behind the development: “The potential of 2D materials, such as graphene, is in their electronic properties and their processability; we developed a process to harness the surface area of our nanosheet dispersions to stabilise emulsion droplets with ultra-thin coatings.
“The tuneability of these emulsions allows us to wrap 2D materials around any liquid droplets to exploit their electronic properties. This includes emulsion inks, in which, we’ve discovered that droplets can be deposited without the coffee ring effect which hinders printing of conventional functional inks, potentially enabling single-droplet films for printed transistors and other electronic devices.
“Another exciting development for our research group is that we can now also design and control our emulsions towards specific applications such as wrapping soft polymers such as silicone for wearable strain sensors that exhibit increased sensitivity at low graphene loading, and we are also investigating emulsion assembly of battery electrode materials to enhance the robustness of these energy storage devices.”
Professor of Experimental Physics at the University of Sussex, Alan Dalton, who was first inspired by the making of a salad dressing to explore the potential of adding graphene to liquid emulsions, explains why this development is exciting: “In bringing the graphene coatings of the liquid droplets down to atomically-thin layers and in opening wide the potential for real-world applications by being able to do so with any liquid material, this research development will significantly advance the emerging and scientifically exciting field of liquid electronics.”
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Materials provided by University of Sussex. Original written by Alice Ingall. Note: Content may be edited for style and length. More