Fundamental Limits Research Articles

Weather-dependency of the thermographic flow visualization of the laminar-turbulent transition on wind turbines

Abstract Analyzing the airflow around wind turbines during operation requires an in-process-capable measurement approach that functions without modification of the rotor blade. Infrared–thermographic flow visualization is such a measurement approach. However, its measurement capabilities on wind turbines in operation are highly weather-dependent. Therefore, to understand the expected flow visualization quality in non-laboratory conditions, the dependence of the achievable contrast and contrast-to-noise ratio (CNR) of the laminar-turbulent transition on solar radiation and air temperature is studied, respectively. A linear dependence of the contrast on the absorbed solar radiation is derived as a first estimation from a theoretical study of the heat balance. While the air temperature variations are shown to have no effect under certain conditions. The slope of the linear dependence of about 0.025 m2K W−1 was validated by experiments. To further study the fundamental measurability limit, only the camera noise with constant variance is here applied to determine the achievable CNR, which is thus directly proportional to the contrast. As a result, the achievable contrast and CNR for visualizing the laminar-turbulent flow transition over the year, over the day, and for different yaw angles of the wind turbine are determined. For this investigation, a wind turbine location near in northern Germany, is assumed as an example, and a maximal achievable contrast and CNR of 4.2 K and 122, respectively, are estimated, which agree with previous measurements. The presented method applies to any other wind turbine location and thus enables planning thermographic flow measurements on any wind turbine in the world.

Class-E, active electrically small antenna with enhanced bandwidth-efficiency product and high radiated power at the high-frequency (HF) band

Antennas operating at the high-frequency (HF) band (3–30 MHz) are frequently electrically small due to the large wavelength of electromagnetic waves (10–100 m). However, the bandwidth-efficiency products of passively matched electrically small antennas (ESAs) are fundamentally limited. Wideband HF waveforms using bandwidths of 24 kHz or more have recently received significant attention in military communications applications. Efficiently radiating such signals from conventional passive ESAs is very challenging due to fundamental physical limits on bandwidth-efficiency products of ESAs. However, active antennas are not subject to the same constraints. In this work, we present the design and experimental characterization of a high-power, class-E active ESA with enhanced bandwidth-efficiency product compared to that of passively matched ESAs. Specifically, the proposed class-E active ESA can radiate wideband HF signals with bandwidths of 24 kHz or more at 3 MHz (fractional bandwidths of ≈1%–4%), with total efficiencies ranging from 40% up to 80% depending on the modulation type and radiated power levels approaching 100 W. Our approach uses a highly efficient, integrated class-E switching circuit specifically designed to drive an electrically small, high-Q HF antenna over a bandwidth exceeding 24 kHz at 3 MHz. Using a high-Q RLC antenna model, we have successfully demonstrated wideband binary ASK, PSK, and FSK modulations with the proposed class-E active ESA. Experimental results indicate that the bandwidth-efficiency product of this class-E active ESA is 5.4–9.8 dB higher than that of an equivalent passive design with the same data rate and bit-error-rate.

Future of plasma etching for microelectronics: Challenges and opportunities

Plasma etching is an essential semiconductor manufacturing technology required to enable the current microelectronics industry. Along with lithographic patterning, thin-film formation methods, and others, plasma etching has dynamically evolved to meet the exponentially growing demands of the microelectronics industry that enables modern society. At this time, plasma etching faces a period of unprecedented changes owing to numerous factors, including aggressive transition to three-dimensional (3D) device architectures, process precision approaching atomic-scale critical dimensions, introduction of new materials, fundamental silicon device limits, and parallel evolution of post-CMOS approaches. The vast growth of the microelectronics industry has emphasized its role in addressing major societal challenges, including questions on the sustainability of the associated energy use, semiconductor manufacturing related emissions of greenhouse gases, and others. The goal of this article is to help both define the challenges for plasma etching and point out effective plasma etching technology options that may play essential roles in defining microelectronics manufacturing in the future. The challenges are accompanied by significant new opportunities, including integrating experiments with various computational approaches such as machine learning/artificial intelligence and progress in computational approaches, including the realization of digital twins of physical etch chambers through hybrid/coupled models. These prospects can enable innovative solutions to problems that were not available during the past 50 years of plasma etch development in the microelectronics industry. To elaborate on these perspectives, the present article brings together the views of various experts on the different topics that will shape plasma etching for microelectronics manufacturing of the future.

Curved crease origami and topological singularities enable hyperextensibility of L. olor.

Fundamental limits of cellular deformations, such as hyperextension of a living cell, remain poorly understood. Here, we describe how the single-celled protist Lacrymaria olor, a 40-micrometer cell, is capable of reversibly and repeatably extending its necklike protrusion up to 1200 micrometers in 30 seconds. We discovered a layered cortical cytoskeleton and membrane architecture that enables hyperextensions through the folding and unfolding of cellular-scale origami. Physical models of this curved crease origami display topological singularities, including traveling developable cones and cytoskeletal twisted domain walls, which provide geometric control of hyperextension. Our work unravels how cell geometry encodes behavior in single cells and provides inspiration for geometric control in microrobotics and deployable architectures.

Relay Adsorption in Metal-Organic Frameworks for One-Step Helium Purification at Ambient Temperature.

One-step He purification from natural gas represents a crucial solution for addressing the global He shortages. The prevailing method to produce high-grade He involves cryogenic distillation and ultralow temperature adsorption processes, which is highly cost- and energy-intensive. Separating and purifying He at ambient temperature is a great challenge because the fundamental limitation lies in the boiling point, polarizability, and kinetic diameters of CH4/N2/He gases. In this study, we seek to implement a relay adsorption strategy using Ni(ina)2 and MIL-100(Cr) metal-organic frameworks (MOFs) to produce high-purity He from ternary mixtures (CH4, N2, and He) at ambient temperature. The CH4/He selectivity in Ni(ina)2 and N2/He selectivity in MIL-100(Cr) both reach record 15.39 and 128.49, respectively, making the relay adsorption for helium purification highly efficient. The breakthrough experiments show that the two MOFs can sequentially adsorb CH4 and N2 in ternary mixtures, producing He with a purity of up to 99.99% in one step. The remarkable separation performance and stability of these MOFs underscore the industrial potential in purifying He at ambient temperature.

Electromechanically Reconfigurable Terahertz Stereo Metasurfaces.

Dynamic terahertz devices are vital for the next generation of wireless communication, sensing, and non-destructive imaging technologies. Metasurfaces have emerged as a paradigm-shifting platform, offering varied functionalities, miniaturization, and simplified fabrication compared to their 3D counterparts. However, the presence of in-plane mirror symmetry and reduced degree of freedom impose fundamental limitations on achieving advanced chiral response, beamforming, and reconfiguration capabilities. In this work, a platform composed of electrically actuated resonators that can be colossally reconfigured between planar and 3D geometries is demonstrated. To illustrate the platform, metadevices with 3D Split Ring Resonators are fabricated, wherein two counteracting driving forces are combined: i) folding induced by stress mismatch, which enables non-volatile state design and ii) unfolding triggered by the strain associated with insulator-to-metal transition in VO2, which facilitates volatile structural reconfiguration. This large structural reconfiguration space allows for resonance mode switching, widely tunable magnetic and electric polarizabilities, and increased frequency agility. Moreover, the unique properties of VO2, such as the hysteretic nature of its phase transition is harnessed to demonstrate a multi-state memory. Therefore, these VO2 integrated metadevices are highly attractive for the realization of 6G communication devices such as reconfigurable intelligent surfaces, holographic beam formers, and spatial light modulators.

Two-dimensional electro-optical multiphoton microscopy.

The development of genetically encoded fluorescent indicators of neural activity with millisecond dynamics has generated demand for ever faster two-photon (2P) imaging systems, but acoustic and mechanical beam scanning technologies are approaching fundamental limits. We demonstrate that potassium tantalate niobate (KTN) electro-optical deflectors (EODs), which are not subject to the same fundamental limits, are capable of ultrafast two-dimensional (2D) 2P imaging in vivo. To determine if KTN-EODs are suitable for 2P imaging, compatible with 2D scanning, and capable of ultrafast in vivo imaging of genetically encoded indicators with millisecond dynamics. The performance of a commercially available KTN-EOD was characterized across a range of drive frequencies and laser parameters relevant to in vivo 2P microscopy. A second KTN-EOD was incorporated into a dual-axis scan module, and the system was validated by imaging signals in vivo from ASAP3, a genetically encoded voltage indicator. Optimal KTN-EOD deflection of laser light with a central wavelength of 960nm was obtained up to the highest average powers and pulse intensities tested (power: 350mW; pulse duration: 118fs). Up to 32 resolvable spots per line at a 560kHz line scan rate could be obtained with single-axis deflection. The complete dual-axis EO 2P microscope was capable of imaging a by field-of-view at over 10kHz frame rate with lateral resolution. We demonstrate in vivo imaging of neurons expressing ASAP3 with high temporal resolution. We demonstrate the suitability of KTN-EODs for ultrafast 2P cellular imaging in vivo, providing a foundation for future high-performance microscopes to incorporate emerging advances in KTN-based scanning technology.

Multi-Modality Deep Infarct: Non-invasive identification of infarcted myocardium using composite in-silico-human data learning.

Myocardial infarction (MI) continues to be a leading cause of death worldwide. The precise quantification of infarcted tissue is crucial to diagnosis, therapeutic management, and post-MI care. Late gadolinium enhancement-cardiac magnetic resonance (LGE-CMR) is regarded as the gold standard for precise infarct tissue localization in MI patients. A fundamental limitation of LGE-CMR is the invasive intravenous introduction of gadolinium-based contrast agents that present potential high-risk toxicity, particularly for individuals with underlying chronic kidney diseases. Herein, we develop a completely non-invasive methodology that identifies the location and extent of an infarct region in the left ventricle via a machine learning (ML) model using only cardiac strains as inputs. In this transformative approach, we demonstrate the remarkable performance of a multi-fidelity ML model that combines rodent-based in-silico-generated training data (low-fidelity) with very limited patient-specific human data (high-fidelity) in predicting LGE ground truth. Our results offer a new paradigm for developing feasible prognostic tools by augmenting synthetic simulation-based data with very small amounts of in-vivo human data. More broadly, the proposed approach can significantly assist with addressing biomedical challenges in healthcare where human data are limited.

Multi-Modality Deep Infarct: Non-invasive identification of infarcted myocardium using composite in-silico-human data learning.

Myocardial infarction (MI) continues to be a leading cause of death worldwide. The precise quantification of infarcted tissue is crucial to diagnosis, therapeutic management, and post-MI care. Late gadolinium enhancement-cardiac magnetic resonance (LGE-CMR) is regarded as the gold standard for precise infarct tissue localization in MI patients. A fundamental limitation of LGE-CMR is the invasive intravenous introduction of gadolinium-based contrast agents that present potential high-risk toxicity, particularly for individuals with underlying chronic kidney diseases. Herein, we develop a completely non-invasive methodology that identifies the location and extent of an infarct region in the left ventricle via a machine learning (ML) model using only cardiac strains as inputs. In this transformative approach, we demonstrate the remarkable performance of a multi-fidelity ML model that combines rodent-based in-silico-generated training data (low-fidelity) with very limited patient-specific human data (high-fidelity) in predicting LGE ground truth. Our results offer a new paradigm for developing feasible prognostic tools by augmenting synthetic simulation-based data with very small amounts of in-vivo human data. More broadly, the proposed approach can significantly assist with addressing biomedical challenges in healthcare where human data are limited.

On quantum computing for artificial superintelligence

Artificial intelligence algorithms, fueled by continuous technological development and increased computing power, have proven effective across a variety of tasks. Concurrently, quantum computers have shown promise in solving problems beyond the reach of classical computers. These advancements have contributed to a misconception that quantum computers enable hypercomputation, sparking speculation about quantum supremacy leading to an intelligence explosion and the creation of superintelligent agents. We challenge this notion, arguing that current evidence does not support the idea that quantum technologies enable hypercomputation. Fundamental limitations on information storage within finite spaces and the accessibility of information from quantum states constrain quantum computers from surpassing the Turing computing barrier. While quantum technologies may offer exponential speed-ups in specific computing cases, there is insufficient evidence to suggest that focusing solely on quantum-related problems will lead to technological singularity and the emergence of superintelligence. Subsequently, there is no premise suggesting that general intelligence depends on quantum effects or that accelerating existing algorithms through quantum means will replicate true intelligence. We propose that if superintelligence is to be achieved, it will not be solely through quantum technologies. Instead, the attainment of superintelligence remains a conceptual challenge that humanity has yet to overcome, with quantum technologies showing no clear path toward its resolution.

Maximisation of synchronisability under low injection power

Synchronisability of limit cycle oscillators has been measured by the width of the synchronous frequency band, known as the Arnold tongue, concerning external forcing. We clarify a fundamental limit on maximizing this synchronisability within a specified extra low power budget, which underlies an important and ubiquitous problem in nonlinear science related to an efficient synchronisation of weakly forced nonlinear oscillators. In this letter, injection-locked Class-E oscillators are considered as a practical case study, and we systematically analyse their power consumption; our observations demonstrate the independence of power consumption in the oscillator from power consumption in the injection circuit and verify the dependency of power consumption in the oscillator solely on its oscillation frequency. These systematic observations, followed by the mathematical optimisation establish the existence of a fundamental limit on synchronisability, validated through systematic circuit simulations. The results offer insights into the energetics of synchronisation for a specific class of injection-locked oscillators.

Graph‐theoretical chirality measure and chirality–property relations for chemical structures with multiscale mirror asymmetries

AbstractChirality is an essential geometric property unifying small molecules, biological macromolecules, inorganic nanomaterials, biological microparticles, and many other chemical structures. Numerous chirality measures have attempted to quantify this geometric property of mirror asymmetry and to correlate these measures with physical and chemical properties. However, their utility has been widely limited because these correlations have been largely notional. Furthermore, chirality measures also require prohibitively demanding computations, especially for chiral structures comprised of thousands of atoms. Acknowledging the fundamental problems with quantification of mirror asymmetry, including the ambiguity of sign‐variable pseudoscalar chirality measures, we revisit this subject because of the significance of quantifying chirality for quantitative biomimetics and describing the chirality of nanoscale materials that display chirality continuum and scale‐dependent mirror asymmetry. We apply the concept of torsion within the framework of differential geometry to the graph theoretical representation of chiral molecules and nanostructures to address some of the fundamental problems and practical limitations of other chirality measures. Chiral gold clusters and other chiral structures are used as models to elaborate a graph‐theoretical chirality (GTC) measure, demonstrating its applicability to chiral materials with different degrees of chirality at different scales. For specific cases, we show that GTC provides an adequate description of both the sign and magnitude of mirror asymmetry. The direct correlations with macroscopic properties, such as chiroptical spectra, are enhanced by using the hybrid chirality measures combining parameters from discrete mathematics and physics. Taking molecular helices as an example, we established a direct relation between GTC and optical activity, indicating that this chirality measure can be applied to chiral metamaterials and complex chiral constructs.

Exploring the Fundamental Spatial Limits of Magnetic All-Optical Switching.

All-optical switching (AOS) results in ultrafast and deterministic magnetization reversal upon single laser pulse excitation, potentially supporting faster and more energy-efficient data storage. To explore the fundamental limits of achievable bit densities in AOS, we have used soft X-ray transient grating spectroscopy to study the ultrafast magnetic response of a GdFe alloy after a spatially structured excitation with a periodicity of 17 nm. The ultrafast spatial evolution of the magnetization in combination with atomistic spin dynamics and microscopic temperature model calculations allows us to derive a detailed phase diagram of AOS as a function of both the absorbed energy density and the nanoscale excitation period. Our results suggest that the minimum size for AOS in GdFe alloys, induced by a nanoscale periodic excitation, is around 25 nm and that this limit is governed by ultrafast lateral electron diffusion and by the threshold for optical damage.

Analyzing the Problems of a District-Based Administration Using Monte Carlo Simulation: The Case of Sex Offender Notifications in Korea

The problems of administrations based simply on administrative units that do not consider the operational purposes of the system have been consistently discussed. For example, in the Republic of Korea, sex offenders’ information is distributed via physical mail only in a few regions, a practice that is too rigidly based on the boundaries of the administrative ‘Dong’ of the offender’s residence. This implies that citizens in an adjacent building will not be notified if their Dong is different. Therefore, this study analyzed the problems of an administrative system that does not consider its realistic scope by using the case study of sex offender notifications. By expanding the distance from children and youth grids, we ascertained the extent of the problems with sex offender notifications. Additionally, to determine whether these problems have occurred by chance at a specific point in time or if there has been a fundamental limitation in the system, the Monte Carlo simulation was applied to compare the actual and random data of residences.

A vision chip with complementary pathways for open-world sensing.

Image sensors face substantial challenges when dealing with dynamic, diverse and unpredictable scenes in open-world applications. However, the development of image sensors towards high speed, high resolution, large dynamic range and high precision is limited by power and bandwidth. Here we present a complementary sensing paradigm inspired by the human visual system that involves parsing visual information into primitive-based representations and assembling these primitives to form two complementary vision pathways: a cognition-oriented pathway for accurate cognition and an action-oriented pathway for rapid response. To realize this paradigm, a vision chip called Tianmouc is developed, incorporating a hybrid pixel array and a parallel-and-heterogeneous readout architecture. Leveraging the characteristics of the complementary vision pathway, Tianmouc achieves high-speed sensing of up to 10,000 fps, a dynamic range of 130 dB and an advanced figure of merit in terms of spatial resolution, speed and dynamic range. Furthermore, it adaptively reduces bandwidth by 90%. We demonstrate the integration of a Tianmouc chip into an autonomous driving system, showcasing its abilities to enable accurate, fast and robust perception, even in challenging corner cases on open roads. The primitive-based complementary sensing paradigm helps in overcoming fundamental limitations in developing vision systems for diverse open-world applications.

A unifying quantum speed limit for time-independent Hamiltonian evolution

Quantum speed limit (QSL) is the study of fundamental limits on the evolution time of quantum systems. For instance, under the action of a time-independent Hamiltonian, the evolution time between an initial and a final quantum state obeys various mutually complementary lower bounds. They include the Mandelstam–Tamm (MT) bound, the Margolus–Levitin (ML) bound, the Luo–Zhang bound, the Lee–Chau (LC) bound together with the dual ML bound introduced by Ness and coworkers. Here we show that the MT bound can be obtained by optimizing the LC bound over a certain parameter. More importantly, we report a QSL that includes all the above bounds as special cases before optimizing over the physically meaningless reference energy level of a quantum system. This unifying bound depends on a certain parameter p. For any fixed p, we find all pairs of time-independent Hamiltonian and initial pure quantum state that saturate this unifying bound. More importantly, these pairs allow us to compute this bound accurately and efficiently using an oracle that returns certain pth moments related to the absolute value of energy of the quantum state. Moreover, this oracle can be simulated by a computationally efficient and accurate algorithm for finite-dimensional quantum systems as well as for certain infinite-dimensional quantum states with bounded and continuous energy spectra. This makes our computational method feasible in a lot of practical situations. We further compare the performance of this bound for the case of a fixed p as well as the case of optimizing over p with existing QSLs. We find that if the dimension of the underlying Hilbert space is ≲2000 , our unifying bound optimized over p can be computed accurately in a few minutes using Mathematica code with just-in-time compilation in a typical desktop. Besides, this optimized unifying QSL is at least as good as all the existing ones combined and can occasionally be a few percent to a few times better.

On the optimality of the radical-pair quantum compass

Quantum sensing enables the ultimate precision attainable in parameter estimation. Circumstantial evidence suggests that certain organisms, most notably migratory songbirds, also harness quantum-enhanced magnetic field sensing via a radical-pair-based chemical compass for the precise detection of the weak geomagnetic field. However, what underpins the acuity of such a compass operating in a noisy biological setting, at physiological temperatures, remains an open question. Here, we address the fundamental limits of inferring geomagnetic field directions from radical-pair spin dynamics. Specifically, we compare the compass precision, as derived from the directional dependence of the radical-pair recombination yield, to the ultimate precision potentially realisable by a quantum measurement on the spin system under steady-state conditions. To this end, we probe the quantum Fisher information and associated Cramér–Rao bound in spin models of realistic complexity, accounting for complex inter-radical interactions, a multitude of hyperfine couplings, and asymmetric recombination kinetics, as characteristic for the magnetosensory protein cryptochrome. We compare several models implicated in cryptochrome magnetoreception and unveil their optimality through the precision of measurements ostensibly accessible to nature. Overall, the comparison provides insight into processes honed by nature to realise optimality whilst constrained to operating with mere reaction yields. Generally, the inference of compass orientation from recombination yields approaches optimality in the limits of complexity, yet levels off short of the theoretical optimal precision bounds by up to one or two orders of magnitude, thus underscoring the potential for improving on design principles inherent to natural systems.

The Hill function is the universal Hopfield barrier for sharpness of input–output responses

The Hill functions, [Formula: see text], have been widely used in biology for over a century but, with the exception of [Formula: see text], they have had no justification other than as a convenient fit to empirical data. Here, we show that they are the universal limit for the sharpness of any input-output response arising from a Markov process model at thermodynamic equilibrium. Models may represent arbitrary molecular complexity, with multiple ligands, internal states, conformations, coregulators, etc, under core assumptions that are detailed in the paper. The model output may be any linear combination of steady-state probabilities, with components other than the chosen input ligand held constant. This formulation generalizes most of the responses in the literature. We use a coarse-graining method in the graph-theoretic linear framework to show that two sharpness measures for input-output responses fall within an effectively bounded region of the positive quadrant, [Formula: see text], for any equilibrium model with [Formula: see text] input binding sites. [Formula: see text] exhibits a cusp which approaches, but never exceeds, the sharpness of [Formula: see text], but the region and the cusp can be exceeded when models are taken away from thermodynamic equilibrium. Such fundamental thermodynamic limits are called Hopfield barriers, and our results provide a biophysical justification for the Hill functions as the universal Hopfield barriers for sharpness. Our results also introduce an object, [Formula: see text], whose structure may be of mathematical interest, and suggest the importance of characterizing Hopfield barriers for other forms of cellular information processing.

How Does the Neighborhood Unit Inform Community Revitalization?

Community revitalization is a complex, multifaceted process, studied conceptually and empirically in the vast multidisciplinary literature. Among the cited elements of community revitalization are housing; school, civic, and retail spaces; street networks; parks; and green spaces. However, the elements are commonly studied in isolation, not considering their interrelated qualities as all-of-a-piece of the community revitalization process. In this paper, we draw on the concept of the neighborhood unit that facilities a holistic approach to community revitalization. We show how the neighborhood unit is metamorphosed and thereby endured from the classic to the contemporary. We argue that the neighborhood unit informs, as well as being challenged by, community revitalization. Furthermore, inadequate attention is given to how urban revitalization challenges the efficacy of the neighborhood unit itself. The inner-city blight provides an impetus to look beyond the neighborhood to the metropolitan region as a whole. The neighborhood unit’s fundamental limitation is posed by its cellular autonomy, in favor of alternatives that connect the neighborhood to the metropolitan region’s jobs–housing–services–mobility opportunity holistically. Our literature review of the impactful elements of community revitalization is aided by AI (ChatGPT) as an expeditious search engine. It is found that the AI-aided search of the universal poses anew the significance of the particular—the site- and context-specific. We conclude with) universal “performance dimensions” of Good City Form that are calibrated locally, reflecting the goodness of the city form, of which the neighborhood is a building block.

Compatibility Review for Object Detection Enhancement through Super-Resolution.

With the introduction of deep learning, a significant amount of research has been conducted in the field of computer vision in the past decade. In particular, research on object detection (OD) continues to progress rapidly. However, despite these advances, some limitations need to be overcome to enable real-world applications of deep learning-based OD models. One such limitation is inaccurate OD when image quality is poor or a target object is small. The performance degradation phenomenon for small objects is similar to the fundamental limitations of an OD model, such as the constraint of the receptive field, which is a difficult problem to solve using only an OD model. Therefore, OD performance can be hindered by low image quality or small target objects. To address this issue, this study investigates the compatibility of super-resolution (SR) and OD techniques to improve detection, particularly for small objects. We analyze the combination of SR and OD models, classifying them based on architectural characteristics. The experimental results show a substantial improvement when integrating OD detectors with SR models. Overall, it was demonstrated that, when the evaluation metrics (PSNR, SSIM) of the SR models are high, the performance in OD is correspondingly high as well. Especially, evaluations on the MS COCO dataset reveal that the enhancement rate for small objects is 9.4% higher compared to all objects. This work provides an analysis of SR and OD model compatibility, demonstrating the potential benefits of their synergistic combination. The experimental code can be found on our GitHub repository.