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John Nash (1928−2015)

John Nash was born on June 13, 1928, in Bluefield, West Virginia, a former coal town nestled deep in the Appalachian Mountains. As a young boy, Nash was solitary, bookish, and introverted. His father, John Sr., was a quiet engineer with an incisive mind. His mother, Virginia, also intelligent, was a former teacher who had large dreams for her son, pushing him to read at four, learn Latin, and skip a grade at school.

The first hint of John Nash’s math talent came in fourth grade, when a teacher told Virginia that the boy couldn’t do the math. Virginia laughed, well aware that her son was going down his own path to solve the simple problems. In high school, John solved his teachers’ clunky proofs in just a few elegant steps. He was one of ten nationally awarded winners of the George Westinghose Award, which provided him with a full scholarship to the Carnegie Institute of Technology. He hopped from engineering to chemistry before discovering his passion: mathematics.

He was accepted into Princeton University, which at the time was to mathematicians what Detroit was, and still is, to cars. Nash first wowed his peers with an elegantly playable board game, which his peers dubbed “Nash,” but later reached the market as Hex. He then absorbed himself in one of the sexiest math fields of the day, game theory, which described strategies in competition, whether in card games or business. His deceptively simple doctoral thesis would later re-orient the field of economics, although no one, not even Nash, predicted its potential.

Axial encoding unlocks up to eightfold faster 3D microscopy with less light

A research team from HKU Engineering has pioneered a fundamentally new imaging strategy known as AIMED (Arbitrary illumination microscopy with encoded depth), which utilizes a sub-sampling approach. By integrating innovations in axial optical encoding with advanced computational image reconstruction, the AIMED technology enables a substantial increase in 3D imaging speed while enhancing photon safety, all with minimal additional system complexity. This breakthrough demonstrates significant advantages across efficiency, image quality, and system compatibility.

This work was conducted by the OMEGA laboratory under the leadership of Professor Kenneth K. Y. Wong of the Department of Electrical and Computer Engineering at the University of Hong Kong (HKU). The study is published in the journal Advanced Photonics.

Toward cheaper, cleaner hydrogen production

Sobek was born and raised in Argentina, but he also grew up at MIT over the course of three degrees and more than a decade. He first studied aeronautics and astronautics at MIT, then jumped to mechanical engineering as a graduate student, then moved to the Department of Electrical Engineering and Computer Science, where he worked under PhD advisors and MIT professors Martha Gray and Stephen Senturia. His thesis focused on a technique for quickly measuring optical properties of large numbers of biological cells.

“A lot of my learnings around microfabrication and materials chemistry ended up being really relevant for 1s1,” Sobek says. “A class that was very important to me was taught by Professor Amar Bose. I was a teaching assistant for him for a couple of semesters, and that had an incredible influence on my thinking.”

Following graduation, Sobek worked in microelectronics and microfluidics before founding his own company, Zymera, in 2004. The company developed deep-tissue imaging technology for detecting cancer and other serious diseases.

Nanofiber implant delivers three drugs, doubles survival in glioblastoma mice

Researchers with the University of Cincinnati and Johns Hopkins Medicine developed a potential treatment for brain cancer that uses nanofibers embedded with a combination of drugs that work in concert to target tumors. The drugs proved more effective in combination than when administered alone and can provide both immediate and long-lasting doses to kill cancer cells.

“In our study, a three-drug combination showed strong synergistic effects across multiple glioblastoma models and significantly improved survival in animal studies,” said Daewoo Han, an assistant professor in UC’s College of Engineering and Applied Science and lead author of the paper published in ACS Biomaterials Science & Engineering.

Han and Distinguished Research Professor Andrew Steckl incorporated the drugs into electrospun fiber membranes, creating a nanofiber drug delivery system. Steckl’s NanoLab at the University of Cincinnati is a leading developer of this technology that uses an electric field to create a multilayered fiber mesh for drug delivery, among other uses. “This combination is pretty powerful,” Steckl said.

A Symbolic Analysis of Relay and Switching Circuits

In 1937, a young graduate student named Claude Shannon submitted a master’s thesis with an unassuming title: “A Symbolic Analysis of Relay and Switching Circuits.”

A Symbolic Analysis of Relay and Switching Circuits is the title of a master’s thesis written by computer science pioneer Claude E. Shannon while attending the Massachusetts Institute of Technology (MIT) in 1937, [ 1 ] [ 2 ] and then published in 1938. In his thesis, Shannon, a dual degree graduate of the University of Michigan, proved that Boolean algebra [ 3 ] could be used to simplify the arrangement of the relays that were the building blocks of the electromechanical automatic telephone exchanges of the day. He went on to prove that it should also be possible to use arrangements of relays to solve Boolean algebra problems. His thesis laid the foundations for all digital computing and digital circuits. [ 4 ] [ 5 ]

The utilization of the binary properties of electrical switches to perform logic functions is the basic concept that underlies all electronic digital computer designs. Shannon’s thesis became the foundation of practical digital circuit design when it became widely known among the electrical engineering community during and after World War II. At the time, the methods employed to design logic circuits (for example, contemporary Konrad Zuse’s Z1) were ad hoc in nature and lacked the theoretical discipline that Shannon’s paper supplied to later projects.

Shannon’s work also differed significantly in its approach and theoretical framework compared to the work of Akira Nakashima. Whereas Shannon’s approach and framework was abstract and based on mathematics, Nakashima tried to extend the existent circuit theory of the time to deal with relay circuits, and was reluctant to accept the mathematical and abstract model, favoring a grounded approach. [ 6 ] Shannon’s ideas broke new ground, with his abstract and modern approach dominating modern-day electrical engineering. [ 6 ].

Paul Dirac

From that insight, Dirac built an entirely new formulation of the theory using what he called “q-numbers” (quantum numbers)—abstract quantities that don’t commute. He independently rediscovered aspects of Hilbert’s operator theory, though he preferred his own algebraic route because he found mathematicians’ obsession with convergence and existence theorems unappealing.

Paul Adrien Maurice Dirac (, dih-RAK ; [ 3 ] 8 August 1902 – 20 October 1984) was a British theoretical physicist who is considered to be one of the founders of quantum mechanics. [ 4 ] [ 5 ] Dirac laid the foundations for both quantum electrodynamics and quantum field theory, coining the former term. [ 6 ] [ 7 ] [ 8 ] [ 9 ] He was Lucasian Professor of Mathematics at the University of Cambridge from 1932 to 1969, and a professor of physics at Florida State University from 1970 to 1984. Dirac shared the 1933 Nobel Prize in Physics with Erwin Schrödinger “for the discovery of new productive forms of atomic theory.” [ 10 ]

Dirac graduated from the University of Bristol with a Bachelor of Science in Electrical Engineering in 1921, and a Bachelor of Arts in Mathematics in 1923. [ 11 ] Dirac then graduated from St John’s College, Cambridge, with a Doctor of Philosophy in Physics in 1926, writing the first ever thesis on quantum mechanics. [ 12 ]

He formulated the Dirac equation, one of the most important results in physics, in 1928. [ 7 ] It connected special relativity and quantum mechanics and predicted the existence of antimatter. [ 13 ] He wrote a famous paper in 1931, [ 14 ] which further predicted the existence of antimatter. [ 15 ] [ 16 ] [ 13 ] Dirac also contributed greatly to the reconciliation of general relativity with quantum mechanics. He contributed to Fermi–Dirac statistics, which describes the behaviour of fermions, particles with half-integer spin. His 1930 monograph, The Principles of Quantum Mechanics, is one of the most influential texts on the subject. [ 17 ] He and Schrödinger tied for eighth in a Physics World poll of the greatest physicists of all time. [ 18 ] .

Evidence of scaling advantage for the quantum approximate optimization algorithm on a classically intractable problem

We study the scaling of QAOA TTS with the problem size on the low autocorrelation binary sequences (LABS) problem (15, 16), also known as the Bernasconi model in statistical physics (17, 18). The LABS problem has applications in communications engineering, where the low autocorrelation sequences are used for designing radar pulses (15, 19). To solve LABS, one has to produce a sequence of N bits that minimizes a specific quartic objective.

We choose LABS to study the scaling of QAOA TTS for the following three reasons. First, the complexity of LABS grows rapidly, with optimal solutions known only for N ≤ 66 and the best heuristics producing approximate solutions of quality decaying with N for N 200 (20, 21). This makes it a promising candidate problem, since only a few hundred qubits are required to tackle classically intractable instances. Second, the performance of classical solvers for LABS has been benchmarked (20, 21) in terms of the scaling of their TTS with problem size. Since optimal solutions are only known for N ≤ 66, the scaling of TTS for all classical solvers is obtained by fitting results for N ≤ 66. We reproduce these results and observe that that the scaling of classical solvers at N ≤ 40 matches the behavior for N up to 66 reported in the literature. This provides evidence that the scaling we observe for QAOA at N ≤ 40 will similarly extrapolate to larger N. Third, LABS has only one instance per problem size N. Combined with the hardness of LABS, this makes it possible to reliably study the scaling of QAOA at large problem sizes, where simulating tens or hundreds of random instances would be computationally infeasible.

We obtain the scaling by performing noiseless exact simulation of QAOA with fixed schedules. Our results are enabled by a custom algorithm-specific graphics processing unit (GPU) simulator (22), which we execute using up to 1,024 GPUs per simulation on the Polaris supercomputer accessed through the Argonne Leadership Computing Facility. We find that the TTS of QAOA with number of layers p = 12 grows as 1.46N, which is improved to 1.21N if combined with quantum minimum finding. This scaling is better than that of the best classical heuristic, which has a TTS that grows as 1.34N. We note that we do not propose any new quantum algorithms in this work. Instead, we study a general quantum optimization heuristic with broad applicability (namely, QAOA) and make no specific modifications to adapt it to the LABS problem.

Leaving gravity behind: Experiment from ISS reveals how particles alter turbulent flow behavior

After traveling hundreds of miles above Earth and spending months aboard the International Space Station, a University of Delaware experiment has returned to campus, bringing new data on how turbulence behaves in microgravity.

The project, led by assistant professor of mechanical engineering Tyler Van Buren, is designed to study how particles influence turbulent flows. From dust in the air to sand in coastal zones and bubbles at the sea surface, particles can change how flows behave.

Van Buren compares it to an energetic crowd moving around while carrying objects.

Optical device uses humidity to unlock hidden information and offers new option for data storage

Engineers at the University of California San Diego have developed an optical device that reveals hidden images and changes colors in response to different levels of humidity. The technology, published in Light: Science & Applications, could lead to the development of new anti-counterfeiting labels, secure data storage, interactive displays, and environmental sensors.

The device works by displaying different images depending on moisture levels in the air. Under normal conditions or low humidity levels, one image (UC San Diego Triton logo) is visible. When humidity increases, a second image (UC San Diego library logo) emerges and conceals the first. This transition can be triggered even when a person breathes on the device. It happens in a fraction of a second and can be repeated many times.

“You can imagine using this as a built-in security feature with the environment acting like a key that unlocks different pieces of information,” said study first author Asad Nauman, an electrical and computer engineering postdoctoral researcher at UC San Diego. “One example would be something like a credit card security tag, where you can blow on it and reveal a hidden code. Another application would be an environmental sensor that changes color as the humidity changes.”

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