Aurelia Butler

In recent years, pneumatic artificial muscles (PAMs) have emerged as promising actuators for simulating human-like movements, with prominent applications in various industries including robotics, rehabilitation, and prosthetics. PAMs are usually composed of rubber and covered with braided yarn and can mimic the mechanics of human muscles. They can stiffen and contract on being supplied with pressurized air and soften and lengthen upon releasing the air. However, PAM is a nonlinear system and experiences huge latency, making it important to have control systems that can regulate their performance.
While determining a nonlinear mathematical model for PAM is challenging, researchers in the past have proposed many control methods to solve the problems associated with PAM. However, while these traditional control methods exhibit decent performance, they are not able to deal with PAM’s nonlinearity and hysteresis. Moreover, while learning control algorithms have been theoretically effective in improving PAM-based system’s performance, their implementation is practice is quite difficult.
To overcome these limitations and address this open problem, a group of researchers led by Associate Professor Ngoc-Tam BUI of the Innovative Global Program, College of Engineering, Shibaura Institute of Technology in Japan, along with Dr. Quy-Thinh Dao of Hanoi University of Science and Technology, has proposed a novel solution. In their study published in the journal Scientific Reports on 22 May 2023, they propose a control approach called “adaptive fuzzy sliding mode controller (or AFSMC)” that uses fuzzy logic (a type of computational thinking) for estimating control parameters of PAM-based systems.
“The proposed innovative control strategy leverages the Takagi-Sugeno fuzzy algorithm to estimate the disturbance component and automatically update the output variable values, demonstrating enhanced tracking accuracy and adaptability compared to traditional sliding mode control methods,” explains Associate Professor BUI.
The researchers first developed a sliding mode controller with a control signal that incorporates a special variable to estimate the disturbances and improve the control performance. Next, they designed an adaptive fuzzy algorithm, wherein parameter vectors of the component rules are automatically updated by an adaptive law, to compute the disturbance variable. The stability of the developed ASFMC algorithm was then analyzed using the Lyapunov stability condition (used to study the stability of a nonlinear system). Furthermore, the researchers conducted a series of experiments to assess the performance of their controller by comparing it with traditional sliding mode control methods.
Remarkably, the AFSMC approach exhibited improved tracking accuracy, with a root mean square error value of 2.68° at a frequency of 0.5 Hz under load, while the sliding mode controller approach displayed a higher value of 4.21°. Moreover, it showed exceptional adaptability to abrupt external disturbances. Explaining these results further, Associate Professor BUI says, “In a comparative evaluation against the well-known commercial rehabilitation system, LOKOMAT, the AFSMC controller delivered similar performance. It also exhibited superior adaptability to sudden load changes, swiftly returning to the desired trajectory by manipulating its control output.”
These findings thus point to the potential of the novel AFSMC approach for integration into robotic rehabilitation devices, assistive devices, and physical therapy equipment for precise and personalized therapy. Moreover, this approach can aid in the design and development of advanced prosthetic limbs for enhanced functionality and rehabilitation outcomes.
Talking about the long-term implications of this study, Associate Professor BUI says: “With the outcomes of this research, the emergence of a commercial rehabilitation system actuated by PAM can be anticipated within the next 5 to 10 years. This innovative system will provide significant benefits to patients, including those with spinal cord injuries and stroke and others requiring rehabilitation.”
While this research has laid the groundwork for advancing trajectory tracking control in PAM systems, we hope that it ignites further exploration and development in the field of rehabilitation technology. More

In many cases, cells are very active in their movement and serve as power generators. The ability of cells to produce physical forces is one of the basic functions of the body. When running, for example, the forces generated in the cells cause the muscles to contract and the breath to work. It has been possible to measure even the forces experienced by individual proteins by force sensors developed in the past, but previously intracellular forces and mechanical strains could not have been measured.
Together with the scientists from The Ohio State University OSU, cell biology researchers at Tampere University have developed a force sensor that can be attached to the side of a mechanically responding protein, allowing it to sense forces and strain on the protein within the cell.
The development of the micro-sized sensor began on a conference travel in December 2019.
“The power-sensing part is like a rubber band that changes colour when stretched. This part is attached to the antibodies at both ends of the rubber band, which bind to the cellular target protein under study. The force or elongation of the studied protein can then be detected under a microscope by following the elongation of the rubber band, i.e. the colour it produces,” says Teemu Ihalainen,a Senior Research Fellow from BioMediTech at Tampere University.
According to Ihalainen, the force sensor, which is only about twenty nanometres in size, can be easily generalised to a wide range of cell biology research and various target proteins. With the help of the protein biosensor, forces can be measured, for example, in the nuclear membrane, between different proteins, or generally in the cytoskeleton of the cell. It allows the mechanics of the cell to be transformed into visible form for the first time. There has already been great interest in this technology in various laboratories in Japan, India, Norway and the United States.
Internal forces of the cell provide information on the mechanics of cancer
Cells are subject to forces all the time, both in normal bodily functions and diseases. More

Researchers at the National University of Singapore (NUS) have developed a novel aero-elastic pressure sensor, called ‘eAir’. This technology can be applied to minimally-invasive surgeries and implantable sensors by directly addressing the challenges associated with existing pressure sensors.
The eAir sensor promises increased precision and reliability across medical applications. It can potentially transform laparoscopic surgeries by enabling tactile feedback for surgeons, allowing more precise manipulation of patient tissues. In addition, the sensor can improve patient experiences by offering a less invasive means of monitoring intracranial pressure (ICP), a key health metric for individuals with neurological conditions.
Led by Associate Professor Benjamin TEE from the NUS College of Design and Engineering and NUS Institute for Health Innovation & Technology, the research team’s findings were recently published in scientific journal Nature Materials on 17 August 2023.
From lotus leaf to laboratory: Harnessing nature’s design
Conventional pressure sensors frequently struggle with accuracy. They have trouble delivering consistent readings, often returning varying results when the same pressure is applied repeatedly and can overlook subtle changes in pressure — all of which can lead to significant errors. They are also typically made from stiff and mechanically inflexible materials.
To address these challenges in pressure sensing, the NUS team drew inspiration from a phenomenon known as the ‘lotus leaf effect’ — a unique natural phenomenon where water droplets effortlessly roll off the leaf’s surface, made possible by its minuscule, water-repelling structures. Mimicking this effect, the team has engineered a pressure sensor designed to significantly improve the sensing performance.
“The sensor, akin to a miniature ‘capacity meter’, can detect minute pressure changes — mirroring the sensitivity of a lotus leaf to the extremely light touch of a water droplet,” explained Assoc Prof Tee. More

Environmental regulators and other organizations should do more scientific experimentation to inform natural resource policy, according to an international group of economists that includes University of Wyoming researchers.
In a new paper in the  journal Science, the economists say more frequent use of up-front experiments would result in more effective environmental policymaking in areas ranging from pollution control to timber harvesting across the world.
“Although formal experimentation is a cornerstone of science and is increasingly embedded in nonenvironmental social programs, it is virtually absent in environmental programs,” the researchers wrote. “Strengthening the culture of experimentation in the environmental community will require changes in norms and incentives.”
The paper acknowledges that scientists and practitioners can legitimately argue about how much time and effort should be given to experiments in environmental policy, but it contends that the current allocation of roughly zero percent is suboptimal.
The paper was produced by The Teton Group, an initiative led by Professor Todd Cherry, the John S. Bugas Chair in UW’s Department of Economics. The prominent group of economists meets every fall in Wyoming to discuss critical ideas that impact environmental policy and economic development. Members include colleagues from UW and scholars in behavioral environmental policy from Carnegie Mellon University, Johns Hopkins University, Purdue University, the University of Texas-Austin, the University of Wisconsin-Madison and several key European universities. The group of UW economists include Todd Cherry, Jacob Hochard, Stephen Newbold, Jason Shogren, Linda Thunström and Klaas van ‘t veld.
“Guesswork is expensive, so we need to apply tools that reduce uncertainty about what works and what doesn’t,” Cherry says. “Lessons learned can improve current and future policy.”
According to the new paper, environmental scientists and practitioners typically rely on field experience, case studies and retrospective evaluations of programs that were not designed to generate evidence about cause and effect. The result can be ineffective or even counterproductive programs. More

Osaka Metropolitan University scientists have developed an AI model that accurately estimates a patient’s age, using chest radiographs of healthy individuals collected from multiple facilities. Furthermore, they found a positive relationship between differences in the AI-estimated and chronological ages and a variety of chronic diseases, such as hypertension, hyperuricemia, and chronic obstructive pulmonary disease. In the future, it is expected that AI biomarkers will be developed to predict life expectancy, estimate the severity of chronic diseases, and forecast surgery-related risks.
What if “looking your age” refers not to your face, but to your chest? Osaka Metropolitan University scientists have developed an advanced artificial intelligence (AI) model that utilizes chest radiographs to accurately estimate a patient’s chronological age. More importantly, when there is a disparity, it can signal a correlation with chronic disease. These findings mark a leap in medical imaging, paving the way for improved early disease detection and intervention. The results are set to be published in The Lancet Healthy Longevity.
The research team, led by graduate student Yasuhito Mitsuyama and Dr. Daiju Ueda from the Department of Diagnostic and Interventional Radiology at the Graduate School of Medicine, Osaka Metropolitan University, first constructed a deep learning-based AI model to estimate age from chest radiographs of healthy individuals. They then applied the model to radiographs of patients with known diseases to analyze the relationship between AI-estimated age and each disease. Given that AI trained on a single dataset is prone to overfitting, the researchers collected data from multiple institutions.
For the development, training, internal and external testing of the AI model for age estimation, a total of 67,099 chest radiographs were obtained between 2008 and 2021 from 36,051 healthy individuals who underwent health check-ups at three facilities. The developed model showed a correlation coefficient of 0.95 between the AI-estimated age and chronological age. Generally, a correlation coefficient of 0.9 or higher is considered to be very strong.
To validate the usefulness of AI-estimated age using chest radiographs as a biomarker, an additional 34,197 chest radiographs were compiled from 34,197 patients with known diseases from two other institutions. The results revealed that the difference between AI-estimated age and the patient’s chronological age was positively correlated with a variety of chronic diseases, such as hypertension, hyperuricemia, and chronic obstructive pulmonary disease. In other words, the higher the AI-estimated age compared to the chronological age, the more likely individuals were to have these diseases.
“Chronological age is one of the most critical factors in medicine,” stated Mr. Mitsuyama. “Our results suggest that chest radiography-based apparent age may accurately reflect health conditions beyond chronological age. We aim to further develop this research and apply it to estimate the severity of chronic diseases, to predict life expectancy, and to forecast possible surgical complications.” More

Trinity’s quantum physicists in collaboration with IBM Dublin have successfully simulated super diffusion in a system of interacting quantum particles on a quantum computer.
This is the first step in doing highly challenging quantum transport calculations on quantum hardware and, as the hardware improves over time, such work promises to shed new light in condensed matter physics and materials science.
The work is one of the first outputs of the TCD-IBM predoctoral scholarship programme which was recently established where IBM hires PhD students as employees while being co-supervised at Trinity. The paper was published recently in leading Nature journal NPJ Quantum Information.
IBM is a global leader in the exciting field of quantum computation. The early stage quantum computer used in this study consists of 27 superconducting qubits (qubits are the building blocks of quantum logic) and is physically located in IBMs lab in Yorktown Heights in New York and programmed remotely from Dublin.
Quantum computing is currently one of the most exciting technologies and is expected to be edging closer towards commercial applications in the next decade. Commercial applications aside there are fascinating fundamental questions which quantum computers can help with. The team at Trinity and IBM Dublin tackled one such question concerning quantum simulation.
Explaining the significance of the work and the idea of quantum simulation in general, Trinity’s Professor John Goold, Director of the newly established Trinity Quantum Alliance, who led the research, explains:
“Generally speaking the problem of simulating the dynamics of a complex quantum system with many interacting constituents is a formidable challenge for conventional computers. Consider the 27 qubits on this particular device. In quantum mechanics the state of such a system is described mathematically by an object called a wave function. In order to use a standard computer to describe this object you require a huge number of coefficients to be stored in memory and the demands scale exponentially with the number of qubits; roughly 134 million coefficients, in the case of this simulation. More

A new study led by Vinod M. Menon and his group at the City College of New York shows that trapping light inside magnetic materials may dramatically enhance their intrinsic properties. Strong optical responses of magnets are important for the development of magnetic lasers and magneto-optical memory devices, as well as for emerging quantum transduction applications.
In their new article in Nature, Menon and his team report the properties of a layered magnet that hosts strongly bound excitons — quasiparticles with particularly strong optical interactions. Because of that, the material is capable of trapping light — all by itself. As their experiments show, the optical responses of this material to magnetic phenomena are orders of magnitude stronger than those in typical magnets. “Since the light bounces back and forth inside the magnet, interactions are genuinely enhanced,” said Dr. Florian Dirnberger, the lead-author of the study. “To give an example, when we apply an external magnetic field the near-infrared reflection of light is altered so much, the material basically changes its color. That’s a pretty strong magneto-optic response.”
“Ordinarily, light does not respond so strongly to magnetism,” said Menon. “This is why technological applications based on magneto-optic effects often require the implementation of sensitive optical detection schemes.”
On how the advances can benefit ordinary people, study co-author Jiamin Quan said: “Technological applications of magnetic materials today are mostly related to magneto-electric phenomena. Given such strong interactions between magnetism and light, we can now hope to one day create magnetic lasers and may reconsider old concepts of optically controlled magnetic memory.” Rezlind Bushati, a graduate student in the Menon group, also contributed to the experimental work.
The study conducted in close collaboration with Andrea Alù and his group at CUNY Advanced Science Research Center is the result of a major international collaboration. Experiments conducted at CCNY and ASRC were complemented by measurements taken at the University of Washington in the group of Prof. Xiaodong Xu by Dr. Geoffrey Diederich. Theoretical support was provided by Dr. Akashdeep Kamra and Prof. Francisco J. Garcia-Vidal from the Universidad Autónoma de Madrid and Dr. Matthias Florian from the University of Michigan. The materials were grown by Prof. Zdenek Sofer and Kseniia Mosina at the UCT Prague and the project was further supported by Dr. Julian Klein at MIT. The work at CCNY was supported through the US Air Force Office of Scientific Research, the National Science Foundation (NSF) — Division of Materials Research, the NSF CREST IDEALS center, DARPA and the German Research Foundation. More