Aurelia Butler

Walking can boost not only your own energy but also, potentially, the energy of your wearable electronic devices. Osaka Metropolitan University scientists made a significant advance toward self-charging wearable devices with their invention of a dynamic magnifier-enhanced piezoelectric vibration energy harvester that can amplify power generated from impulsive vibrations, such as from human walking, by about 90 times, while remaining as small as currently developed energy harvesters. The results were published in Applied Physics Letters.
These days, people carry multiple electronic devices such as smartphones, and wearable devices are expected to become increasingly widespread in the near future. The resulting demand for more efficient recharging of these devices has increased the attention paid to energy harvesting, a technology that converts energy such as heat and light into electricity that can power small devices. One form of energy harvesting called vibration energy harvesting is deemed highly practical given that it can transform the kinetic energy from vibration into electricity and is not affected by weather or climate.
A research team led by Associate Professor Takeshi Yoshimura from the Graduate School of Engineering at Osaka Metropolitan University has developed a microelectromechanical system (MEMS) piezoelectric vibration energy harvester that is only approximately 2 cm in diameter with a U-shaped metal component called a dynamic magnifier. Compared with conventional harvesters, the new harvester allows for an increase of about 90 times in the power converted from impulsive vibrations, which can be generated by human walking motion.
The team has been working on developing vibration energy harvesters that utilize the piezoelectric effect, a phenomenon in which specific types of materials produce an electric charge or voltage in response to applied pressure. So far, they have succeeded in generating microwatt-level electricity from mechanical vibrations with a constant frequency, such as those generated by motors and washing machines. However, the power generation of these harvesters drops drastically when the applied vibrations are nonstationary and impulsive, such as those generated by human walking.
Responding to this challenge, the team developed and incorporated the U-shaped vibration amplification component under the harvester. The component allowed for improvement in power generation without increasing the device size. The technology is expected to generate electric power from non-steady vibrations, including walking motion, in order to power small wearable devices such as smartphones and wireless earphones.
Professor Yoshimura concluded, “Since electronic devices are expected to become more energy-efficient, we hope that this invention will contribute to the realization of self-charging wearable devices.”
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Researchers at LMU and Charité hospital in Berlin have developed a method for classifying difficult-to-diagnose nasal cavity tumors.
Although tumors in the nasal cavity and the paranasal sinus are confined to a small space, they encompass a very broad spectrum with many tumor types. As they often do not exhibit any specific pattern or appearance, they are difficult to diagnose. This applies especially to so-called sinonasal undifferentiated carcinomas (SNUCs).
Now a team led by Dr. Philipp Jurmeister and Prof. Frederick Klauschen from the Institute of Pathology at LMU and Prof. David Capper from Charité University Hospital as well as the German Cancer Consortium (DKTK) ), partner sites Munich and Berlin, has achieved a decisive improvement in diagnostics. The team developed an AI tool that reliably distinguishes tumors on the basis of chemical DNA modifications and assigns the SNUCs, which the methods available before now have been unable to distinguish, to four clearly distinct groups. This breakthrough could open up new opportunities for targeted therapies.
Tumor-specific DNA modifications
Chemical modifications in DNA play a vital role in the regulation of gene activity. This includes DNA methylation, whereby an extra methyl group is added to the DNA building blocks. In earlier studies, the scientists had already demonstrated that the methylation pattern of the genome is specific for different tumor types, because it can be traced back to the tumor’s cell of origin.
“On this basis, we’ve now recorded the DNA methylation patterns of almost 400 tumors in the nasal cavity and paranasal sinus,” says Capper. Thanks to an extensive international collaboration, the researchers managed to compile such a large number of samples even though these tumors are rare and comprise only about four percent of all malignant tumors in the nose and throat area.
Four tumor groups with different prognoses
For the analysis of the DNA methylation data, the researchers have developed an AI model that assigns the tumors to different classes. “Due to the large volumes of data involved, machine learning methods are indispensable,” says Jurmeister. “To actually recognize patterns, we had to evaluate several thousand methylation positions in our study.” This revealed that SNUCs can be classified into four groups, which also differ in terms of further molecular characteristics.
Furthermore, these results are clinically relevant, as the various groups have different prognoses. “One group takes a surprisingly good course, for example, even though the tumors look very aggressive under the microscope,” says Klauschen. “Whereas another group has a poor prognosis.” On the basis of the molecular characteristics of the groups, researchers may also be able to develop targeted new therapy approaches in the future.
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As machine-learning models become larger and more complex, they require faster and more energy-efficient hardware to perform computations. Conventional digital computers are struggling to keep up.
An analog optical neural network could perform the same tasks as a digital one, such as image classification or speech recognition, but because computations are performed using light instead of electrical signals, optical neural networks can run many times faster while consuming less energy.
However, these analog devices are prone to hardware errors that can make computations less precise. Microscopic imperfections in hardware components are one cause of these errors. In an optical neural network that has many connected components, errors can quickly accumulate.
Even with error-correction techniques, due to fundamental properties of the devices that make up an optical neural network, some amount of error is unavoidable. A network that is large enough to be implemented in the real world would be far too imprecise to be effective.
MIT researchers have overcome this hurdle and found a way to effectively scale an optical neural network. By adding a tiny hardware component to the optical switches that form the network’s architecture, they can reduce even the uncorrectable errors that would otherwise accumulate in the device.
Their work could enable a super-fast, energy-efficient, analog neural network that can function with the same accuracy as a digital one. With this technique, as an optical circuit becomes larger, the amount of error in its computations actually decreases. More

Previously, very little was known about online gaming behaviour based on the actual games played and how career interests are reflected in what people play. To examine this correlation, in collaboration with Game Academy Ltd, Surrey researchers investigated the gaming behaviour of 16,033 participants to explore how the hobby could support video game players’ future career planning and professional training.
The participants played a different number of games on Steam — a video game digital distribution service and storefront. Researchers studied the 800 most-played games and only included participants for whom they had access to gender and job details.
Researchers discovered that IT professionals and engineers played puzzle-platform games, which possibly enhance their spatial skills. People in managerial roles showed an interest in action roleplay games where organisational and planning skills are involved and engineering professionals were associated with strategy games which often require problem-solving and spatial skills. There were apparent gender differences too — females preferred playing single-player games, whereas males preferred playing shooting games.
Dr Anna-Stiina Wallinheimo, lead author of the study, Cognitive Psychologist, and Postdoctoral Research Fellow at the University of Surrey’s Centre for Translation Studies (CTS) said:
“In recruitment processes, the best candidates may be missed because organisations do not consider the soft skills that have been gained through non-work activities (for example, online gaming). As a result of our research, we believe applicants’ online gaming experiences should be highlighted because these acquired soft skills can really help to develop their all-round strengths for the job at hand.”
Dr Anesa Hosein, co-author of the study and Associate Professor in Higher Education at the University of Surrey said:
“By understanding to what extent career interests are reflected in game playing, we may be able to demonstrate more clearly how these align with career interests and encourage employers to understand the value of the soft skills associated with gaming. Our research could also inspire game developers to work on honing these soft skills more closely in their design. Furthermore, places of learning, such as universities, could allow students to reflect and incorporate gaming as part of their career development and consider how gaming can be included in the curriculum to enhance alignment between students’ learning, career aspirations and extra-curricular gaming interests.”
This research was published in SAGE Journals.
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In recent years, research and development on quantum computers has made considerable progress. Quantum chemical calculations for electronic structures of atoms and molecules are attracting great attention as one of the most promising applications of quantum computers. In order to utilize quantum chemical calculations for chemistry and related fields, it is essential to develop geometry optimization methods for finding the most stable structure of molecules. The geometry optimization requires calculations of energy derivatives with respect to nuclear coordinates of molecules.
The finite difference method is one approach for energy derivative calculations. On a classical computer, calculations based on this method for one-dimensional systems require at least two evaluations of the energy. Previous research has shown that a quantum computer, in contrast, requires only a single query to calculate the energy derivatives based on the finite difference method, regardless of the number of degrees of freedom. However, quantum circuits relevant to quantum algorithms capable of performing energy derivative calculations have not been implemented.
A research group including Dr. Kenji Sugisaki, Professor Kazunobu Sato, and Professor Emeritus Takeji Takui from the Graduate School of Science at Osaka Metropolitan University has successfully extended the quantum phase difference estimation algorithm, a general quantum algorithm for the direct calculations of energy gaps, to enable the direct calculation of energy differences between two different molecular geometries. This allows for the computation, based on the finite difference method, of energy derivatives with respect to nuclear coordinates in a single calculation.
Furthermore, the research group has applied the developed energy derivative calculations to execute geometry optimizations of H2, LiH, BeH2, and N2 molecules without calculating the total energies, demonstrating the usefulness of the developed method. The group also discussed how quantum circuits can be assembled according to different degrees of freedom of the molecules.
This research is the latest in a series of the researchers’ articles on quantum chemical calculations on quantum computers. “Our latest findings bring us one step closer to applying quantum chemical calculations on a quantum computer to real-world problems,” said Dr. Sugisaki. “Since energy derivative calculations are used for not only molecular geometry optimizations but also various calculations for molecular properties, the application of our method is expected to play a very important role in a wide range of related fields, such as in silico drug discovery/design and materials development.”
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Imagine walking into an ice cream shop and scanning your options. A sugar cone with one scoop is $3. A second scoop comes out to $4, but for just 50 cents more, you can get a large waffle cone with three scoops. Some people may not want that much ice cream. But for many, it’s hard to pass up a good deal.
Adding a third option to make something else (usually the higher priced item) more attractive is a common marketing strategy. But since the 1980s, scholars have been debating whether this attraction effect via a “decoy option” actually works in real-life settings.
Findings from a new, in-depth study bolster the argument that decoy options can shift consumer preferences. It’s also the first to test this approach in digital markets where billions of people make choices every day.
The researchers randomly assigned 4,000 participants to eight experiments based on reward-based crowdfunding campaigns, including one on Kickstarter with a real Swiss watchmaking company. Their findings, published in Information Systems Research, show the attraction effect shifted preferences from a low-priced to a high-priced reward by as much as 28%.
“That’s a significant jump that can turn an entrepreneur’s project into a reality,” said Abhay Mishra, associate professor of information systems and business analytics at Iowa State University and co-author of the study. “Our findings can help inform artists, innovators and creatives to make smarter, more effective reward menus to reach their fundraising goals.”
In reward-based crowdfunding markets, entrepreneurs set a fundraising target with a deadline for their project. Backers choose from several reward menu options, pledging a certain amount of money to receive a product or sample of the to-be-funded project. Whether the entrepreneur gets any of the funding and the backers receive their rewards depends on the project successfully reaching the fundraising goal by the set date. More

Microscopic materials analysis is essential to achieve desirable performance in next-generation nanoelectronic devices, such as low power consumption and high speeds. However, the magnetic materials involved in such devices often exhibit incredibly complex interactions between nanostructures and magnetic domains. This, in turn, makes functional design challenging.
Traditionally, researchers have performed a visual analysis of the microscopic image data. However, this often makes the interpretation of such data qualitative and highly subjective. What is lacking is a causal analysis of the mechanisms underlying the complex interactions in nanoscale magnetic materials.
In a recent breakthrough published in Scientific Reports, a team of researchers led by Prof. Masato Kotsugi from Tokyo University of Science, Japan succeeded in automating the interpretation of the microscopic image data. This was achieved using an “extended Landau free energy model” that the team developed using a combination of topology, data science, and free energy. The model could illustrate the physical mechanism as well as the critical location of the magnetic effect, and proposed an optimal structure for a nano device. The model used physics-based features to draw energy landscapes in the information space, which could be applied to understand the complex interactions at the nanoscales in a wide variety of materials.
“Conventional analysis are based on a visual inspection of microscope images, and the relationships with the material function are expressed only qualitatively, which is a major bottleneck for material design. Our extended Landau free energy model enables us to identify the physical origin and location of the complex phenomena within these materials. This approach overcomes the explainability problem faced by deep learning, which, in a way, amounts to reinventing new physical laws,” Prof. Kotsugi explains. This work was supported by KAKENHI, JSPS, and the MEXT-Program for Creation of Innovative Core Technology for Power Electronics Grant.
When designing the model, the team made use of the state-of-art technique in the fields of topology and data science to extend the Landau free energy model. This led to a model that enabled a causal analysis of the magnetization reversal in nanomagnets. The team then carried out an automated identification of the physical origin and visualization of the original magnetic domain images.
Their results indicated that the demagnetization energy near a defect gives rise to a magnetic effect, which is responsible for the “pinning phenomenon.” Further, the team could visualize the spatial concentration of energy barriers, a feat that had not been achieved until now. Finally, the team proposed a topologically inverse design of recording devices and nanostructures with low power consumption.
The model proposed in this study is expected to contribute to a wide range of applications in the development of spintronic devices, quantum information technology, and Web 3.
“Our proposed model opens up new possibilities for optimization of magnetic properties for material engineering. The extended method will finally allow us to clarify ‘why’ and ‘where’ the function of a material is expressed. The analysis of material functions, which used to rely on visual inspection, can now be quantified to make precise functional design possible,” concludes an optimistic Prof. Kotsugi.
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A team of engineers at UC Santa Cruz has developed a new method for remote automation of the growth of cerebral organoids — miniature, three-dimensional models of brain tissue grown from stem cells. Cerebral organoids allow researchers to study and engineer key functions of the human brain with a level of accuracy not possible with other models. This has implications for understanding brain development and the effects of pharmaceutical drugs for treating cancer or other diseases.
In a new study published in the journal Nature Scientific Reports, researchers from the UCSC Braingeneers group detail their automated, internet-connected microfluidics system, called “Autoculture.” The system precisely delivers feeding liquid to individual cerebral organoids in order to optimize their growth without the need for human interference with the tissue culture.
Cerebral organoids require a high level of expertise and consistency to maintain the precise conditions for cell growth over weeks or months. Using an automated system, as demonstrated in this study, can eliminate disturbance to cell culture growth caused by human interference or error, provide more robust results, and allow more scientists access to opportunities to conduct research with human brain models.
Autoculture also addresses variation that arises in organoid growth due to “batch effect” issues, where organoids grown at different times or at different labs under similar conditions may vary just because of the complexity of their growth. Using this uniform, automated system can reduce variation and allow researchers to better compare and validate their results.
“One of the big challenges is that these cultures are not very reproducible, and in part it’s not surprising because these are months-long experiments. You have to change media every couple of days and try to treat these cultures uniformly, which is extremely challenging,” said Sofie Salama, an acting professor of molecular, cellular and developmental biology at UCSC and an author on the study.
Unique design
Autoculture uses a microfluidic chip designed by the researchers, spearheaded by Associate Professor of Electrical and Computer Engineering Mircea Teodorescu and Biomolecular Engineering Ph.D. student Spencer Seiler. Their novel chips, created from a unique bi-layer mold, have tiny wells and channels for delivering minute amounts of liquid to the organoid, which allow the scientists to have a high level of control over nutrient concentrations and byproducts. Overall, the system uses mostly off-the-shelf, low-cost components, making it accessible and modular. More