Historicizing a Dream of Complete Science.

This paper attempts an historical analysis of a dream of the physicist George Gamow recorded shortly before his death in 1968. The dream is contextualized through Gamow's extended scientific work and popular scientific efforts, and in light of enduring preoccupations with the notion of a complete science. The analysis extends to an examination of the relationship of the dream to dreaming practices and deliberations apart from Gamow's, as evident in the relationship and collaboration between the physicist Wolfgang Pauli and C. G. Jung.

Catch bond kinetics are instrumental to cohesion of fire ant rafts under load.

Dynamic networks composed of constituents that break and reform bonds reversibly are ubiquitous in nature owing to their modular architectures that enable functions like energy dissipation, self-healing, and even activity. While bond breaking depends only on the current configuration of attachment in these networks, reattachment depends also on the proximity of constituents. Therefore, dynamic networks composed of macroscale constituents (not benefited by the secondary interactions cohering analogous networks composed of molecular-scale constituents) must rely on primary bonds for cohesion and self-repair. Toward understanding how such macroscale networks might adaptively achieve this, we explore the uniaxial tensile response of 2D rafts composed of interlinked fire ants (S. invicta). Through experiments and discrete numerical modeling, we find that ant rafts adaptively stabilize their bonded ant-to-ant interactions in response to tensile strains, indicating catch bond dynamics. Consequently, low-strain rates that should theoretically induce creep mechanics of these rafts instead induce elastic-like response. Our results suggest that this force-stabilization delays dissolution of the rafts and improves toughness. Nevertheless, above 35[Formula: see text] strain low cohesion and stress localization cause nucleation and growth of voids whose coalescence patterns result from force-stabilization. These voids mitigate structural repair until initial raft densities are restored and ants can reconnect across defects. However mechanical recovery of ant rafts during cyclic loading suggests that-even upon reinstatement of initial densities-ants exhibit slower repair kinetics if they were recently loaded at faster strain rates. These results exemplify fire ants' status as active agents capable of memory-driven, stimuli-response for potential inspiration of adaptive structural materials.

Construction of a direct Z-scheme Cs3Bi2Cl9/g-C3N4 heterojunction composite for efficient photocatalytic degradation of various pollutants in water: Performance, kinetics and degradation mechanism.

The use of emerging composite materials has been booming to remove environmental pollutants. The aim of this research is to develop a new composite based on Cs3Bi2Cl9 perovskite and graphitic carbon nitride (g-C3N4) to investigate the photocatalytic performance under visible light irradiation. To achieve this, we produce the Cs3Bi2Cl9/g-C3N4 heterojunctions through a simple self-assembly synthesis. The as-synthesized composites are characterized using XRD, FTIR, FESEM, TEM, BET and EDX techniques. The photocatalytic performance of Cs3Bi2Cl9/g-C3N4 is examined in the degradation of various water contaminants, including 4-nitrophenol (4-NP), tetracycline antibiotic (TC), methylene blue (MB) and methyl orange (MO). The experimental results indicate the superior photocatalytic performance of the composites in the degradation of pollutants compared to pure Cs3Bi2Cl9 and g-C3N4. The 10% Cs3Bi2Cl9/g-C3N4 composite achieves the optimal degradation efficiency of 100, 92, 98.7, and 85.1% of 4-NP, TC, MB, and MO, respectively. This superior photocatalytic activity attributes to improved optical and electrochemical properties, including enhanced absorption ability, narrowing band gap, promoted separation efficiency of photogenerated carriers, and a high redox potential, which is confirmed by UV-vis DRS, PL, EIS, and CV analyses. The 10% Cs3Bi2Cl9/g-C3N4 composite also demonstrates high photocatalytic stability after four consecutive cycles. Radical trapping tests show that superoxide radicals (•O2-), holes (h+), and hydroxyl radicals (•OH) contribute to the photocatalytic process. Based on the obtained data, a direct Z-scheme heterojunction mechanism is proposed. Overall, this research offers a new stable photocatalyst with excellent prospect for photocatalytic applications.

BioID Analysis of Actin-Binding Proteins.

Proteins often exist and function as part of higher-order complexes or networks. A challenge is to identify the universe of proximal and interacting partners for a given protein. We describe how the high-activity promiscuous biotin ligase called TurboID is fused to the actin-binding peptide LifeAct to label by biotinylation proteins that bind, or are in close proximity, to actin. The rapid enzyme kinetics of TurboID allows the profiles of actin-binding proteins to be compared under different conditions, such as acute disruption of filamentous actin structures with cytochalasin D.

A cutting tool architecture designed to address the parasitic mechanisms consuming excess power during machining and manufacturing operations-A review-based study towards sustainable manufacturing.

Metal cutting has been extensively studied over the years for improving its efficacy, yet, parasitic mechanisms like chatter and tool wear continue to generate higher forces and energy consumption with poor surface integrity. To address these parasitic mechanisms, a single-point turning cutter design is proposed based on the physics-of-machining including chatter theory to achieve reduced power consumption during the cutting of various metallic alloys like Al-6061, Ti-6Al-4V and others used by critical sectors such as aerospace and automotive. The current work focuses on aspects of machining that effectively reduce parasitic forces feeding into cutting power. The proposed cutter amalgamates features such as optimum side and end cutting edge angles, smaller nose radius and textured rake face into the cutter-body. Such a design is further proposed for use with a mechanochemical effect on a recently discovered plastic flow mode called sinuous flow, which has been reported to bring down cutting forces significantly. Experimental and analytical tests on the cutter design features validate reduction of cutting forces and through that alleviate the tendency to chatter as well as bring about energy savings for cutting of Al 6061. The potential for reduced real-time power consumption makes this design-framework significant for multipoint milling cutters too. It will greatly facilitate frugal manufacturing to account for sustainability in manufacturing operations.

Physics-informed neural wavefields with Gabor basis functions.

Recently, Physics-Informed Neural Networks (PINNs) have gained significant attention for their versatile interpolation capabilities in solving partial differential equations (PDEs). Despite their potential, the training can be computationally demanding, especially for intricate functions like wavefields. This is primarily due to the neural-based (learned) basis functions, biased toward low frequencies, as they are dominated by polynomial calculations, which are not inherently wavefield-friendly. In response, we propose an approach to enhance the efficiency and accuracy of neural network wavefield solutions by modeling them as linear combinations of Gabor basis functions that satisfy the wave equation. Specifically, for the Helmholtz equation, we augment the fully connected neural network model with an adaptable Gabor layer constituting the final hidden layer, employing a weighted summation of these Gabor neurons to compute the predictions (output). These weights/coefficients of the Gabor functions are learned from the previous hidden layers that include nonlinear activation functions. To ensure the Gabor layer's utilization across the model space, we incorporate a smaller auxiliary network to forecast the center of each Gabor function based on input coordinates. Realistic assessments showcase the efficacy of this novel implementation compared to the vanilla PINN, particularly in scenarios involving high-frequencies and realistic models that are often challenging for PINNs.

AI-Aristotle: A physics-informed framework for systems biology gray-box identification.

Discovering mathematical equations that govern physical and biological systems from observed data is a fundamental challenge in scientific research. We present a new physics-informed framework for parameter estimation and missing physics identification (gray-box) in the field of Systems Biology. The proposed framework-named AI-Aristotle-combines the eXtreme Theory of Functional Connections (X-TFC) domain-decomposition and Physics-Informed Neural Networks (PINNs) with symbolic regression (SR) techniques for parameter discovery and gray-box identification. We test the accuracy, speed, flexibility, and robustness of AI-Aristotle based on two benchmark problems in Systems Biology: a pharmacokinetics drug absorption model and an ultradian endocrine model for glucose-insulin interactions. We compare the two machine learning methods (X-TFC and PINNs), and moreover, we employ two different symbolic regression techniques to cross-verify our results. To test the performance of AI-Aristotle, we use sparse synthetic data perturbed by uniformly distributed noise. More broadly, our work provides insights into the accuracy, cost, scalability, and robustness of integrating neural networks with symbolic regressors, offering a comprehensive guide for researchers tackling gray-box identification challenges in complex dynamical systems in biomedicine and beyond.

Physics-assisted machine learning for THz time-domain spectroscopy: sensing leaf wetness.

Signal processing techniques are of vital importance to bring THz spectroscopy to a maturity level to reach practical applications. In this work, we illustrate the use of machine learning techniques for THz time-domain spectroscopy assisted by domain knowledge based on light-matter interactions. We aim at the potential agriculture application to determine the amount of free water on plant leaves, so-called leaf wetness. This quantity is important for understanding and predicting plant diseases that need leaf wetness for disease development. The overall transmission of 12,000 distinct water droplet patterns on a plastized leaf was experimentally acquired using THz time-domain spectroscopy. We report on key insights of applying decision trees and convolutional neural networks to the data using physics-motivated choices. Eventually, we discuss the generalizability of these models to determine leaf wetness after testing them on cases with increasing deviations from the training set.

Physics-Informed Deep Learning Approach for Reintroducing Atomic Detail in Coarse-Grained Configurations of Multiple Poly(lactic acid) Stereoisomers.

Multiscale modeling of complex molecular systems, such as macromolecules, encompasses methods that combine information from fine and coarse representations of molecules to capture material properties over a wide range of spatiotemporal scales. Being able to exchange information between different levels of resolution is essential for the effective transfer of this information. The inverse problem of reintroducing atomistic degrees of freedom in coarse-grained (CG) molecular configurations is particularly challenging as, from a mathematical point of view, it is an ill-posed problem; the forward mapping from the atomistic to the CG description is typically defined via a deterministic operator ("one-to-one" problem), whereas the reversed mapping from the CG to the atomistic model refers to creating one representative configuration out of many possible ones ("one-to-many" problem). Most of the backmapping methods proposed so far balance accuracy, efficiency, and general applicability. This is particularly important for macromolecular systems with different types of isomers, i.e., molecules that have the same molecular formula and sequence of bonded atoms (constitution) but differ in the three-dimensional configurations of their atoms in space. Here, we introduce a versatile deep learning approach for backmapping multicomponent CG macromolecules with chiral centers, trained to learn structural correlations between polymer configurations at the atomistic level and their corresponding CG descriptions. This method is intended to be simple and flexible while presenting a generic solution for resolution transformation. In addition, the method is aimed to respect the structural features of the molecule, such as local packing, capturing therefore the physical properties of the material. As an illustrative example, we apply the model on linear poly(lactic acid) (PLA) in melt, which is one of the most popular biodegradable polymers. The framework is tested on a number of model systems starting from homopolymer stereoisomers of PLA to copolymers with randomly placed chiral centers. The results demonstrate the efficiency and efficacy of the new approach.

Essay: Exploring the Physics of Basic Medical Research.

The 20th century witnessed the emergence of many paradigm-shifting technologies from the physics community, which have revolutionized medical diagnostics and patient care. However, fundamental medical research has been mostly guided by methods from areas such as cell biology, biochemistry, and genetics, with fairly small contributions from physicists. In this Essay, I outline some key phenomena in the human body that are based on physical principles and yet govern our health over a vast range of length and time scales. I advocate that research in life sciences can greatly benefit from the methodology, know-how, and mindset of the physics community and that the pursuit of basic research in medicine is compatible with the mission of physics. Part of a series of Essays that concisely present author visions for the future of their field.

Performance of Fourier-based activation function in physics-informed neural networks for patient-specific cardiovascular flows.

BACKGROUND AND OBJECTIVES: Physics-informed neural networks (PINNs) can be used to inversely model complex physical systems by encoding the governing partial differential equations and training data into the neural network. However, neural networks are known to be biased towards learning less complex functions, called spectral bias. This has important implications in modeling cardiovascular flows, where spatial frequencies can vary substantially across anatomies and pathologies (e.g., aneurysms or stenoses). Recent evidence suggests that Fourier-based activation functions have desirable properties, and can potentially reduce spectral bias; however, the performance and adequacy of such Fourier activation functions have not yet been evaluated in patient-specific cardiovascular flow applications. METHODS: The performance of sine activation function was evaluated against tanh and swish activation functions in a 1D advection-diffusion problem, an eccentric 2D stenosis model (Re=5000), and a patient-specific 3D aortic model (Re=823) under pulsatile flow conditions. CFD simulations were performed at high spatio-temporal resolution and data points were extracted for training the neural network. The number of training data points were normalized by L/D. The performance of the PINNs framework was evaluated with increasing number of training data points and across all three activation functions. RESULTS: Our results demonstrate that sine activation function presents desirable characteristics, such as monotonic reduction in errors, relatively faster convergence, and accurate eigen spectra at higher modes, compared to tanh and swish activation functions. Interestingly, for all activation functions, the domain-averaged errors tended to asymptote at ≈15-20% despite substantial increase in training point density. For 2D eccentric stenosis, errors asymptoted at a sensor point density of 40L/D. For 3D patient-specific aorta, this asymptote was achieved at 180L/D for all three activation functions with an error of ≈15% although sine activation function demonstrated relatively faster convergence. CONCLUSIONS: We have demonstrated that Fourier-based activation functions have higher performance in terms of accuracy and convergence properties for cardiovascular flow applications; however, inherent challenges of neural networks (e.g., spectral bias) can limit the accuracy to ≈15% under physiological, 3D patient-specific blood flow conditions.

Unsupervised Denoising and Super-Resolution of Vascular Flow Data by Physics-Informed Machine Learning.

We present an unsupervised deep learning method to perform flow denoising and super-resolution without high-resolution labels. We demonstrate the ability of a single model to reconstruct three-dimensional stenosis and aneurysm flows, with varying geometries, orientations, and boundary conditions. Ground truth data was generated using computational fluid dynamics, and then corrupted with multiplicative Gaussian noise. Auto-encoders were used to compress the representations of the flow domain geometry and the (possibly noisy and low-resolution) flow field. These representations were used to condition a physics-informed neural network. A physics-based loss was implemented to train the model to recover lost information from the noisy input by transforming the flow to a solution of the Navier-Stokes equations. Our experiments achieved mean squared errors in the true flow reconstruction of O(1.0 × 10-4), and root mean squared residuals of O(1.0 × 10-2) for the momentum and continuity equations. Our method yielded correlation coefficients of 0.971 for the hidden pressure field and 0.82 for the derived wall shear stress field. By performing point-wise predictions of the flow, the model was able to robustly denoise and super-resolve the field to 20× the input resolution.

Physics and the quest for transcendence: A Durkheimian approach.

This essay aims to shed some light on the still common sense of a vocation among scientists. Taking its cue from Paul Forman's analysis of twentieth-century disciplinary science and Emile Durkheim's social view of religions, it suggests that modern scientific communities resemble religious communities in their penchant for transcendence. The essay aims to illustrate this perspective by looking at some developments within the physics discipline since its emergence in the late nineteenth century. One indication for this penchant is the tendency to distance oneself from the material conditions which allowed the discipline to flourish. These utilitarian conditions, industrial as well as material, were seen to pose a threat to the disinterested pursuit of truth. Another is the persistent tendency among theoretical physicists to search for otherworldly, immaterial and unifying foundations.

A three-dimensional network structure of metal-based nanozymes for the construction of colorimetric sensors for the detection of antioxidants.

Although many excellent nanozymes have been developed, designing and synthesizing highly active nanozymes is still challenging. Here, we developed a metal-based nanozyme (metal = Co, Fe, Cu, Zn) with a three-dimensional network structure. It possesses excellent peroxidase activity and catalyzes the reaction between H2O2 and TMB to produce blue oxTMB, while antioxidants have different reducing power on the oxidation product of TMB (oxTMB), which leads to different absorbance and color changes. Using these color reactions, different nanozymes were used to form a colorimetric sensor array with seven antioxidants, and seven antioxidants were sensitively identified. And the differences between the three nanozymes were compared by density functional theory calculations and enzyme kinetic curve results. In conclusion, the colorimetric sensor array based on metal-based nanozymes provides a good strategy for the identification and detection of antioxidants, which has a broad application prospect.

Michaelis-Menten kinetics during dry etching processes.

The chemical etching of germanium in Br2 environment at elevated temperatures is described by the Michaelis-Menten equation. The validity limit of Michaelis-Menten kinetics is subjected to the detailed analysis. The steady-state etching rate requires synergy of two different process parameters. High purity gas should be directed to the substrate on which intermediate reaction product does not accumulate. Theoretical calculations indicate that maximum etching rate is maintained when 99.89% of the germanium surface is covered by the reaction product, and 99.9999967% of the incident Br2 molecules are reflected from the substrate surface. Under these conditions, single GeBr2 molecule is formed after 30 million collisions of Br2 molecules with the germanium surface.

A possible thermodynamic definition and equation of state for a model of political election cycles.

This work demonstrates how a simulation of political discourse can be formulated using variables of the agents' behaviors in a simulation, as thermodynamic variables. With these relations the methodology provides an approach to create a correspondence between the variables of an agent based social system and those of a thermodynamic system. Extended from this observation, diagrams akin to a P-V diagram for gases can be created for this social system. The basic thermodynamic variables of temperature, pressure and volume are defined from a system of agents with political and non-political actions engaged in simulated political discourse. An equation of state is defined for the simulated political phenomenon. Through this equation of state the full thermodynamic map of the system is presented under a P-V diagram with isothermal and isentropic lines, which is able to represent the political situation of the system at each point of time. The classic election cycle that takes place can be represented on this thermodynamic map (corresponding to an Otto cycle). This provides a possibility for researching macroscopic social cycles as a thermodynamic/informational cycle as the traces on the thermodynamic map show similarities to an Otto cycle. Such a formulation reinforces the endeavours of social physics to view social phenomena with physical principles.

Mitochondria at the Nanoscale: Physics Meets Biology-What Does It Mean for Medicine?

Mitochondria are commonly perceived as "cellular power plants". Intriguingly, power conversion is not their only function. In the first part of this paper, we review the role of mitochondria in the evolution of eukaryotic organisms and in the regulation of the human body, specifically focusing on cancer and autism in relation to mitochondrial dysfunction. In the second part, we overview our previous works, revealing the physical principles of operation for proton-pumping complexes in the inner mitochondrial membrane. Our proposed simple models reveal the physical mechanisms of energy exchange. They can be further expanded to answer open questions about mitochondrial functions and the medical treatment of diseases associated with mitochondrial disorders.

The epoch-making importance of Ervin Bauer's theoretical biology.

Ervin Bauer was the only biologist who recognized that the best way to develop theoretical biology on an equal footing with theoretical physics was to follow the method that has ensured the great successes of modern theoretical physics: the general method of science. Following this method, he succeeded to find the universal principle of biology. From this principle he managed to derive all the basic equations of biology, that of metabolism, reproduction, growth, responsiveness and successfully explained all the fundamental phenomena of life. In this paper, I introduce Bauer's theoretical biology and discuss whether he understood it within the framework of the modern physical worldview, or in a broader framework. I point out that the theoretical biology of Ervin Bauer is the first to go beyond the physical worldview, to establish a deeper, biological worldview, and thus to represent a major advance in our understanding of the nature of life, with a significance even greater than that of the Copernican turn. Clarifying the difference between the living and the non-living, it is important to consider the difference between machines and living organisms. It is well known that machines are the manifestations of a dual control; globally, their behavior is controlled by their given structure, while locally, their behavior is governed by the physical laws. Based on Bauer's theoretical biology, it is pointed out that living organisms manifest a three-level causality; the 'additional', biological level corresponds to the autonomous, time-dependent control of their structures.