Search

In this paper, we will address a recurring problem in the field of the general philosophy of science but one that takes on particular relevance in the context of chemistry: the problem surrounding scientific laws. The main challenge is that the laws of chemistry are not universal; moreover, in practice, they are stated alongside numerous exceptions. Given that there are exceptions, we could argue that what the laws assert is neither universally true nor necessary. But if that's the case, are they genuine scientific laws? And if so, what is the reason for such lawfulness? [ABSTRACT FROM AUTHOR]

This tutorial covers physical reservoir computing from a computer science perspective. It first defines what it means for a physical system to compute, rather than merely evolve under the laws of physics. It describes the underlying computational model, the Echo State Network (ESN), and also some variants designed to make physical implementation easier. It explains why the ESN model is particularly suitable for direct physical implementation. It then discusses the issues around choosing a suitable material substrate, and interfacing the inputs and outputs. It describes how to characterise a physical reservoir in terms of benchmark tasks, and task-independent measures. It covers optimising configuration parameters, exploring the space of potential configurations, and simulating the physical reservoir. It ends with a look at the future of physical reservoir computing as devices get more powerful, and are integrated into larger systems. [ABSTRACT FROM AUTHOR]

While IMU based motion capture offers a cost‐effective alternative to premium camera‐based systems, it often falls short in matching the latter's realism. Common distortions, such as self‐penetrating body parts, foot skating, and floating, limit the usability of these systems, particularly for high‐end users. To address this, we employed reinforcement learning to train an AI agent that mimics erroneous sample motion. Since our agent operates within a simulated environment, it inherently avoids generating these distortions since it must adhere to the laws of physics. Impressively, the agent manages to mimic the sample motions while preserving their distinctive characteristics. We assessed our method's efficacy across various types of input data, showcasing an ideal blend of artefact‐laden IMU‐based data with high‐grade optical motion capture data. Furthermore, we compared the configuration of observation and action spaces with other implementations, pinpointing the most suitable configuration for our purposes. All our models underwent rigorous evaluation using a spectrum of quantitative metrics complemented by a qualitative review. These evaluations were performed using a benchmark dataset of IMU‐based motion data from actors not included in the training data. [ABSTRACT FROM AUTHOR]

In this study, the influence of slit pipe wall thickness on shaped charge blasting efficiency was investigated. The influence of different slit pipe wall thicknesses on the dynamic behavior of slotted charge blasting was analyzed by a combination of experimental observations and numerical simulations. Polymethyl methacrylate (PMMA) was used as a rock simulation material to prepare slit pipes with different slit pipe wall thicknesses (0.5, 1.0, and 1.5 mm). A dynamic caustics system is used to capture the crack propagation process. ANSYS/LS‐DYNA was used for the numerical simulation analysis. The experimental results show that there is a positive correlation between the slit pipe wall thickness and the crack propagation length, velocity, and dynamic stress intensity factor. With increasing slit pipe wall thickness, the directional propagation and energy concentration ability of cracks are significantly improved, but an increase in the slit pipe wall thickness will also promote the propagation of secondary cracks. In addition, the numerical simulation results support the experimental observations and reveal the existence of an optimal slit pipe wall thickness that can maximize the blasting efficiency while maintaining structural integrity. This study not only deepens the understanding of the physical laws of the blasting process but also provides a practical reference for the optimal design of slotted charges in engineering applications. Highlights: Sample PMMA material as explosive mediumDynamic caustic test was used to visualize the crack growth process.The results of numerical simulation and model experiment are similar but different.The thickness of slit pipe has great influence on crack propagation. [ABSTRACT FROM AUTHOR]

Motivated by string-theoretic swampland conjectures, the existence of a dark fifth dimension, whose size is roughly 1–10 microns, has been proposed. A great deal of supporting evidence has been presented, and definitive experimental tests are likely to be carried out. The basic idea is that the four-dimensional space–time that we observe is confined to a brane that is localized in the dark dimension. This short note points out that there are two distinct ways to realize such a scenario in string theory/M-theory. In the one considered previously, the dark dimension is topologically a circle and our observable 4d space–time is confined to a brane that is localized in a GUT-scale region of the circle. An alternative possibility is that the dark dimension is a line interval with “end-of-the-world-branes” at each end. The latter option would imply the existence of a parallel 4d space–time, with the same gravitational forces as ours, but otherwise its own physical laws, microns away from us! [ABSTRACT FROM AUTHOR]

The mechanisms of oxygen diffusion in brain capillaries have not been fully clarified to date. According to the laws of physics, the well-documented phenomenon of hyperoxemia-induced excessive increases in brain tissue oxygen pressure (PbtO2) contradicts traditional models of cerebral capillary oxygen diffusion. Circulating models predict a significant drop in oxygen pressure (PO2), and some of them foresee the presence of hypoxic or anoxic corners near the capillary end, regardless of high PbtO2 levels. We propose that the cerebral intracapillary transformation of hemoglobin from the relaxed (R) to the tense (T) quaternary conformational state, driven by deoxygenation and an overload of negative allosteric effectors, and characterized by a lower, more hyperbolic dissociation curve, mitigates the oxygen pressure difference across cerebral capillaries, ensuring a homogeneous pericapillary distribution of oxygen. The hemoglobin R to T state transition is responsible for the high PbtO2 levels observed in viable cerebral tissue during hyperoxemia. [ABSTRACT FROM AUTHOR]

This study investigates the optical information for visual event perception. Events are objects in motion, with properties like shape, weight and surface material influencing the dynamics that shape movements and optics. The progressive transformation of visible textures, known as visual kinaesthetic information, specifies movements and objects. Four experiments tested whether events could be perceived using only visual kinaesthetic information. Participants identified their own walking from point‐light displays (Experiment 1), from simulated environmental texture transformations as a result of their walking (Experiment 2), and from videos shot by a head‐mounted camera during outdoor walking (Experiment 3); and distinguishing strangers from footages captured by their head‐mounted cameras (Experiment 4). In Experiments 2–4, the displays did not resemble the outline of a person or look like walking but revealed the physical relations between the walker and the environment as a result of their movement. Regardless, participants were able to recognize themselves and distinguish strangers. Thus, observers are able to perceive events using visual kinaesthetic information that stems from dynamics. The one‐to‐one correspondences between object property, dynamics, kinematics and optical information are governed by the laws of physics, and unaffected by the event's appearance or viewing perspectives. [ABSTRACT FROM AUTHOR]

Combining ferromagnetic–antiferromagnetic materials in nanoalloys (i.e., nanoparticles, NPs, containing more than one element) can create a diverse landscape of potential electronic structures. As a result, a number of their magnetic properties can be manipulated, such as the exchange bias between NP core and shell, the Curie temperature of nanoparticulated samples, or their magnetocaloric effect. In this work, such a family of materials (namely M–Cr NPs where M is Fe, Co, Ni, or some combination of them) is reviewed with respect to the tunability of their magnetic properties via optimized doping with Cr up to its solubility limit. To this end, gas‐phase synthesis has proven a most effective method, allowing excellent control over the physical structure, composition, and chemical ordering of fabricated NPs by appropriately selecting various deposition parameters. Recent advances in this field (both experimental and computational) are distilled to provide a better understanding of the underlying physical laws and point toward new directions for cutting‐edge technological applications. For each property, a relevant potential application is associated, such as memory cells and recording heads, induced hyperthermia treatment, and magnetic cooling, respectively, aspiring to help connect the output of fundamental and applied research with current real‐world challenges. [ABSTRACT FROM AUTHOR]

The document "Specialty Grand Challenge for Heat Transfer and Thermal Power" published in the journal "Frontiers in Thermal Engineering" discusses the importance of studying radiative and combined heat transfer in various applications, particularly in thermal engineering. The text highlights the significance of thermal radiation in different media, such as scattering media, and provides insights into the theoretical foundations and practical applications of radiative heat transfer. It also delves into specific research topics, including thermal radiation in combustion systems, radiative heat transfer in advanced thermal insulations, and near-field radiative heat transfer, among others. The document emphasizes the diverse nature of research problems in heat transfer, spanning across thermal engineering, geophysics, and biomedicine, and underscores the potential for advancements in these areas through universal physical models and mathematical methods. [Extracted from the article]

Interoception — the internal sensing of bodily states and their communication to the brain — has recently become a 'buzzword'. However, the scientific study of how the brain and the body communicate has a much longer and well-established history. In the 16th century, the French philosopher and scientist René Descartes (1596–1650) proposed that the mind and body are two fundamentally distinct entities, yet they interact with each other to regulate animal and human behaviors. Descartes argued that the body, which operates like a machine and follows the laws of physics, is controlled by the mind (or the brain), the source of our cognitive functions. Today, it is widely recognized that the interactions between the body and brain are critical for our survival and overall well-being (Figure 1). At the same time, we are just beginning to learn about the mechanistic bases of these interactions. In this primer, we will discuss the origins of interoception research and how body–brain interactions govern some of our most critical homeostatic functions, such as energy balance. Given their key roles in health and disease, we will focus on the neural and hormonal pathways involved in gut–brain communication and their impact on feeding behavior and metabolism. The brain senses changes to the internal state of the body, eliciting responses that maintain homeostasis. Alhadeff and Yapici provide an overview of this process, termed interoception, with a particular focus on the gut–brain axis. [ABSTRACT FROM AUTHOR]

In this research, we introduce a novel method leveraging the Transformer architecture to generate high-fidelity precipitation model outputs. This technique emulates the statistical characteristics of high-resolution datasets while substantially lowering computational expenses. The core concept involves utilizing a blend of coarse and fine-grained simulated precipitation data, encompassing diverse spatial resolutions and geospatial distributions, to instruct Transformer in the transformation process. We have crafted an innovative ST-Transformer encoder component that dynamically concentrates on various regions, allocating heightened focus to critical spatial zones or sectors. The module is capable of studying dependencies between different locations in the input sequence and modeling at different scales, which allows it to fully capture spatiotemporal correlations in meteorological element data, which is also not available in other downscaling methods. This tailored module is instrumental in enhancing the model's ability to generate outcomes that are not only more realistic but also more consistent with physical laws. It adeptly mirrors the temporal and spatial distribution in precipitation data and adeptly represents extreme weather events, such as heavy and enduring storms. The efficacy and superiority of our proposed approach are substantiated through a comparative analysis with several cutting-edge forecasting techniques. This evaluation is conducted on two distinct datasets, each derived from simulations run by regional climate models over a period of 4 months. The datasets vary in their spatial resolutions, with one featuring a 50 km resolution and the other a 12 km resolution, both sourced from the Weather Research and Forecasting (WRF) Model. [ABSTRACT FROM AUTHOR]

Since the emergent advent of miniaturized technologies in the last century, the past ninety years have witnessed the extensive use of micro- and nano-manipulation methods, which were promoted by the tremendous advances in fundamental physics as well as in techniques for sensing and actuating and in award-winning precision instrumentation. Micro- and nano-manipulation techniques imply the application of distinct physical laws at distinct spatial scales, and in turn enabled unprecedented progress in a significant number of scientific disciplines, particularly in the life sciences and its biomedical applications. Today, scientists have to cope with a series of issues that are inherently related to theory implementations at micro- and nanoscales, in the attempt to reveal the underlying principles that can enhance the capability of manipulating micrometer and nanometer-sized objects. Our report has the aim of giving an extensive review of the major results achieved in the study of micro- and nano-manipulation, by focusing on how fundamental physics (from robotic manipulation to magnetic, acoustic and optical fields, to electric and fluidic methods) is elaborated and leveraged to gather control over small objects, and how novel techniques are conceptually designed and practically implemented for important tool sets. We also summarize the representative applications in many disciplines over the past decades and discuss potential trends and open problems for future studies. [ABSTRACT FROM AUTHOR]

A mathematical discussion is introduced to describe the gradient coils characterizing the NMRI system. A comparison between the physical laws characterizing the magnetic field of winding coils and a magnetohydrodynamic analysis is built up. The requirements for gradient coil performance are described and analyzed. Design methods are characterized by mathematical manipulation to reduce the nonlinearities that make the magnetic field not suitable for image extraction. A mathematical study has been shown to represent the Golay's pairs as generators of linear magnetic fields. The entire study shows how the choice of the magnetic field pattern inside the NMRI can occur by choosing the proper geometrical parameters that are a function of the nonlinearities involved in the gradient coils design. [ABSTRACT FROM AUTHOR]

Longitudinal vehicle trajectories suffer from errors and noise because of detection and extraction techniques, challenging their applications. Existing smoothing methods either lack physical meaning or cannot ensure solution existence and uniqueness. To address this, we propose a two-step quadratic programming method that aligns smoothed speeds and higher-order derivatives with physical laws, drivers' behaviors, and vehicle characteristics. Unlike the well-known smoothing splines method, which minimizes a weighted sum of discrepancy and roughness in a single quadratic programming problem, our method incorporates prior knowledge of position errors into two sequential quadratic programming problems. Step 1 solves half-smoothed positions by minimizing the discrepancy between them and raw positions, subject to physically meaningful bounds on speeds and higher-order derivatives of half-smoothed positions. Step 2 solves smoothed positions by minimizing the roughness while maintaining physically meaningful bounds and allowing the deviations from raw data of smoothed positions by at most those of the half-smoothed positions and prior position errors. The second step's coefficient matrix is not positive definite, necessitating the matching of the first few smoothed positions with corresponding half-smoothed ones, with equality constraints equaling the highest order of derivatives. We establish the solution existence and uniqueness for both problems, ensuring their well-defined nature. Numerical experiments using Next Generation Simulation (NGSIM) data demonstrate that a third-order derivative constraint yields an efficient method and produces smoothed trajectories comparable with manually re-extracted ones, consistent with the minimum jerk principle for human movements. Comparisons with an existing approach and application to the Highway Drone data set further validate our method's efficacy. Notably, our method is a postprocessing smoothing technique based on trajectory data and is not intended for systematic errors. Future work will extend this method to lateral vehicle trajectories and trajectory prediction and planning for both human-driven and automated vehicles. This approach also holds potential for broader smoothing problems with known average error in raw data. History: This paper has been accepted for the Transportation Science Special Issue on ISTTT25 Conference. Funding: The authors extend their gratitude to the Pacific Southwest Region University Transportation Center and the University of California Institute of Transportation Studies (UC ITS) Statewide Transportation Research Program (STRP) for their valuable financial support. [ABSTRACT FROM AUTHOR]

Porphyrazine and tetrakis porphyrazine are organic compounds with intricate ring structures. They are made up of four indole rings that fuse together to form a larger cyclic structure. These structures have been extensively studied in physics and chemistry. They exhibit unique electronic and optical properties due to their highly conjugated systems. In several scientific domains, they are used as the building blocks for functional materials, molecular electronics, sensors, and catalysis. In order to derive a topological index from data, molecules are represented as graphs, with atoms acting as nodes and bonds acting as edges. There are many different techniques and algorithms that can be used to determine the topological index. We have used the reverse degree-based methodology for the mentioned structures. Reverse degree-based topological indices evaluate the structural complexity and stability of chemical compounds by adding the reversed atom degree sequence to their molecular networks. In this study, we calculated the reverse degree-based topological indices like the reverse redefined Zagreb indices, the reverse forgotten index, the reverse hyper-Zagreb index, the reverse Balaban index, the reverse atom bond connectivity index, the reverse geometric arithmetic index, and the reverse general Randić index for porphyrazine ( P z) and tetrakis porphyrazine (T P z) networks. This research advances our knowledge of the fundamental laws of physics and chemistry by illuminating the complex interrelationships between molecular structure, biological interactions, and chemical reactivity. [ABSTRACT FROM AUTHOR]

In the domain of maritime surveillance, the continuous tracking and monitoring of vessels are imperative for the early detection of potential threats. The Automatic Identification System (AIS) database, which collects vessel movement data over time, including timestamps and other motion details, plays a crucial role in real-time maritime monitoring. However, it frequently exhibits irregular intervals of data collection and intricate, intersecting trajectories, underscoring the importance of analyzing long-term temporal patterns for effective vessel tracking. While Kalman Filters and other physics-based models have been employed to tackle these issues, their effectiveness is limited by their inability to capture long-term dependence and non-linearity in the historical data. This paper introduces a novel approach that leverages Long Short-Term Memory (LSTM), a type of recurrent neural network, renowned for its proficiency in recognizing patterns over extended periods. Recognizing the strengths and limitations of the LSTM model, we propose a hybrid machine-learning algorithm that integrates LSTM with a physics-based model. This combination harnesses the physical laws governing vessel movements alongside data driven pattern mining, thereby enhancing the predictive accuracy of vessel locations. To assess the performance of standalone and hybrid models, various scenarios with different levels of complexity are generated. Furthermore, to simulate real-world data loss conditions often encountered in maritime tracking, temporal data gaps are randomly introduced into the scenarios. The competing approaches are then evaluated using both with time gap and without time gap conditions. Our results show that, although the LSTM model performs better than the physics-based model, the hybrid model consistently outperforms both standalone models across all scenarios. Furthermore, while data gaps negatively impact the accuracy of all models, the performance reduction is minimal for the physics-infused model. In summary, this study not only demonstrates the potential of combining data-driven and physics-based approaches but also sets a new benchmark for maritime vessel tracking. [ABSTRACT FROM AUTHOR]

X-band marine radar captures the signal reflected from the sea surface. Theoretical studies indicate that the initial unfiltered signal contains meaningful information about wind wave parameters. Traditional methods of significant wave height (SWH) estimation rely on physical laws describing signal reflection from rough surfaces. However, recent studies suggest the feasibility of employing artificial neural networks (ANNs) for SWH approximation. Both classical and ANN based approaches necessitate costly in situ data. In this study, as a viable alternative, we propose generating synthetic radar images with specified wave parameters using Fourier-based approach and Pierson- Moskowitz wave spectrum. We generate synthetic images and use them for unsupervised learning approach to train a convolutional component of the reconstruction ANN. After that, we train the regression ANN based on the previous convolutional part to obtain SWH back from the synthetic images. Then, we apply preliminary trained weights for the regression model to train SWH approximation on the dataset of real sea clutter images. In this study, we demonstrate the increase in SWH estimation accuracy from radar images with preliminary training on synthetic data. [ABSTRACT FROM AUTHOR]

The sun floods our environment with a sea of electromagnetic radiation. The largest proportion of this spectrum that reaches the ground falls within the visible spectrum, which is termed 'light'. The light that reaches the earth is structured by the physical laws that determine how the visible spectrum interacts with surfaces, materials, and objects. In order to understand the different forms of optical structure generated in natural environments, we need to understand the physics that give rise to that structure. In this Primer, Bart Anderson discusses the different physical processes that generate optical structure, the variety of evolutionary solutions for sampling it, and different theoretical approaches for understanding how human visual system derives the environmental causes of optical structure from images. [ABSTRACT FROM AUTHOR]

Physics describes reality through the understanding and elucidation of physical laws. From these laws, the universe is predictable. It has indeed been suggested that explanation of observable phenomena comes from our ability to predict these events, through our understanding of physical laws and causal processes. Biological entities operate, of course, through physical and chemical laws. But there exist biological processes that have emergent properties, notably the evolution of adaptation through natural selection, which add an extra dimension to the concept of the understanding of biological phenotypes. The population genetic theory of evolution is mathematically formulated, and contains elements of determinism, typified by the action of natural selection, and elements of randomness, typified by the action of genetic drift and by the randomness of mutation. But is the goal-directed property of natural selection sufficient to conclude that the deterministic elements of the evolutionary process make evolutionary change predictable? And to what extent can we expect 'adaptation' to function as an explanation of biological phenotypes? In this Essay, John Brookfield considers parallels between the operation of physical laws and the processes of population genetic change and suggests that the operation of selection will be too complex and unpredictable for the process to have a predictability that matches those seen in the physical sciences. [ABSTRACT FROM AUTHOR]

Sound occurs when vibrations travel through a medium. These vibrations, their propagation, and their interaction with the environment are dictated by laws of physics, and indirectly reflect underlying physical properties of things in the world. The sense of audition exists to measure sound and infer its causes in the world, so as to help organisms interact with the world around them. Audition is thus indirectly but intimately shaped by the physics of sound. In this primer, Josh McDermott, Vinayak Agarwal, and James Traer provide an overview of how physical principles shape sound in the world, and how implicit knowledge of these principles guides our sense of hearing. [ABSTRACT FROM AUTHOR]

The results of investigation of DNA-protein complexes by two independent structural methods, namely, molecular dynamics (MD) and small-angle X-ray scattering (SAXS), were compared. Molecular dynamics is a computational method enabling the visualization of the behavior of macromolecules in real media, which is based on the laws of physics, but it is limited by numerous simplifications. Small-angle X-ray scattering is an X-ray method, which allows the reconstruction of the three-dimensional structure of a system in solution from a one-dimensional small-angle scattering profile, but it faces the problem of ambiguity in solving inverse problems. The use of structural characteristics of the complexes determined by SAXS for the validation of 3D structural models generated by MD simulations made it possible to significantly reduce the ambivalence of theoretical predictions and demonstrated the efficiency of a combination of MD simulations and SAXS for solving problems of structural biology. [ABSTRACT FROM AUTHOR]

For understanding many real-world problems involving rarefied hypersonic, micro-, and nanoscale gas flows, the primary method may be the direct simulation Monte Carlo (DSMC). However, its computational cost is prohibitive in comparison with the Navier–Stokes–Fourier (NSF) solvers, eclipsing the advantages it provides, especially for situations where flow is in the near continuum regime or three-dimensional applications. This study presents an alternate computational method that bypasses this issue by taking advantage of data-driven modeling and nonlinear coupled constitutive relations. Instead of using numerical solutions of higher-order constitutive relations in conventional partial differential equation-based methods, we build compact constitutive relations in advance by applying deep neural network algorithms to available DSMC solution data and later combine them with the conventional finite volume method for the physical laws of conservation. The computational accuracy and cost of the methodology thus developed were tested on the shock wave inner structure problem, where high thermal non-equilibrium occurs due to rapid compression, for a range of Mach numbers from 2 to 10. The simulation results obtained with the computing time comparable to that of the NSF solver showed almost perfect agreement between the neural network-based combined finite volume method and DSMC and original DSMC solutions. We also present a topology of DSMC constitutive relations that allows us to study how the DSMC topology deviates from the NSF topology. Finally, several challenging issues that must be overcome to become a robust method for solving practical problems were discussed. [ABSTRACT FROM AUTHOR]

Physics-informed neural networks (PINNs) represent an emerging computational paradigm that incorporates observed data patterns and the fundamental physical laws of a given problem domain. This approach provides significant advantages in addressing diverse difficulties in the field of complex fluid dynamics. We thoroughly investigated the design of the model architecture, the optimization of the convergence rate, and the development of computational modules for PINNs. However, efficiently and accurately utilizing PINNs to resolve complex fluid dynamics problems remain an enormous barrier. For instance, rapidly deriving surrogate models for turbulence from known data and accurately characterizing flow details in multiphase flow fields present substantial difficulties. Additionally, the prediction of parameters in multi-physics coupled models, achieving balance across all scales in multiscale modeling, and developing standardized test sets encompassing complex fluid dynamic problems are urgent technical breakthroughs needed. This paper discusses the latest advancements in PINNs and their potential applications in complex fluid dynamics, including turbulence, multiphase flows, multi-field coupled flows, and multiscale flows. Furthermore, we analyze the challenges that PINNs face in addressing these fluid dynamics problems and outline future trends in their growth. Our objective is to enhance the integration of deep learning and complex fluid dynamics, facilitating the resolution of more realistic and complex flow problems. [ABSTRACT FROM AUTHOR]

Background/Objectives: Numerical simulation plays an important role in pharmaceutical preparation recently. Mechanistic models, as a type of numerical model, are widely used in the study of pharmaceutical preparations. Mechanistic models are based on a priori knowledge, i.e., laws of physics, chemistry, and biology. However, due to interdisciplinary reasons, pharmacy researchers have greater difficulties in using computer models. Methods: In this paper, we highlight the application scenarios and examples of mechanistic modelling in pharmacy research and provide a reference for drug researchers to get started. Results: By establishing a suitable model and inputting preparation parameters, researchers can analyze the drug preparation process. Therefore, mechanistic models are effective tools to optimize the preparation parameters and predict potential quality problems of the product. With product quality parameters as the ultimate goal, the experiment design is optimized by mechanistic models. This process emphasizes the concept of quality by design. Conclusions: The use of numerical simulation saves experimental cost and time, and speeds up the experimental process. In pharmacy experiments, part of the physical information and the change processes are difficult to obtain, such as the mechanical phenomena during tablet compression and the airflow details in the nasal cavity. Therefore, it is necessary to predict the information and guide the formulation with the help of mechanistic models. [ABSTRACT FROM AUTHOR]

This study presents the development of a so‐called Turbulent Kinetic Energy (TKE)‐l, or TKE‐l, parameterization of the diffusion coefficients for the representation of turbulent diffusion in neutral and stable conditions in large‐scale atmospheric models. The parameterization has been carefully designed to be completely tunable in the sense that all adjustable parameters have been clearly identified and the number of parameters has been minimized as much as possible to help the calibration and to thoroughly assess the parametric sensitivity. We choose a mixing length formulation that depends on both static stability and wind shear to cover the different regimes of stable boundary layers. We follow a heuristic approach for expressing the stability functions and turbulent Prandlt number in order to guarantee the versatility of the scheme and its applicability for planetary atmospheres composed of an ideal and perfect gas such as that of Earth and Mars. Particular attention has been paid to the numerical stability and convergence of the TKE equation at large time steps, an essential prerequisite for capturing stable boundary layers in General Circulation Models (GCMs). Tests, parametric sensitivity assessments and preliminary tuning are performed on single‐column idealized simulations of the weakly stable boundary layer. The robustness and versatility of the scheme are assessed through its implementation in the Laboratoire de Météorologie Dynamique Zoom GCM and the Mars Planetary Climate Model and by running simulations of the Antarctic and Martian nocturnal boundary layers. Plain Language Summary: In planetary atmospheres, turbulent motions actively contribute to the mixing of quantities such as heat, momentum, and chemical species. Such motions are not resolved in coarse‐grid atmospheric models and have to be parameterized. The parameterization of turbulent mixing should ideally be based on physical laws and sufficiently sophisticated to realistically represent the full spectrum of motions over the full range of stability encountered in the atmospheres. However, it also necessarily contains a number of closure parameters not always well identified and whose values are determined empirically—thereby questioning the universality of the parameterization and its potential application over the full globe or even to other planets—or adjusted to guarantee the numerical stability of the model. This study presents the design of a turbulent mixing parameterization that can be fully calibrated and applied in planetary atmospheres such as that of Mars. We then calibrate the parameterization on an idealized simulation set‐up and test its robustness and performance by running simulations of the Antarctic and Martian atmospheres. Key Points: A simple TKE‐l turbulent diffusion scheme is developed in a semi‐heuristic way for applications in models of the Earth and Mars atmospheresAll adjustable parameters are clearly identified and the number of parameters is minimized to thoroughly assess the parametric sensitivityOnce tuned on GEWEX Atmospheric Boundary Layer Study 1 1D simulations, the scheme is able to capture the Antarctic and Martian stable boundary layers in 3D simulations [ABSTRACT FROM AUTHOR]

Moral realists often disagree about the nature of moral properties. These properties can be natural (as per naturalistic moral realism) or non-natural. But it is unclear how we should understand the notion of naturalness employed in these discussions. In this paper I propose a novel account of moral naturalness. I suggest that a property F is natural iff F falls within the scope of a natural law. In turn, a law is natural when it figures in a nomic nexus involving the laws of physics. [ABSTRACT FROM AUTHOR]

The Regular Phantom Black Hole (RPBH)s are of theoretical and observational importance, some of their properties have been studied. In this work, some of thermodynamical properties such as entropy, temperature, etc., in three background cases, that is, flat, de–Sitter (dS) and Anti–de Sitter (AdS) are studied. Many of the RPBH properties, including horizon radius, are (directly or indirectly) dependent on a scale parameter b$b$. Due to the slightly different structure from Schwarzschild—like metrics, the method to express relations between thermodynamical variables requires a new function of the scale parameter. The local and global stability through the Heat Capacity (HC) and Gibbs free Energy (GE), respectively are also treated. In the AdS case, the regularized metric allows a Hawking‐Page like phase transition of first order. The calculations and graphs show the results in the flat background, are very similar to Schwarzschild black hole and the asymptotically AdS RPBH is more compatible with physical laws than the dS and flat backgrounds. [ABSTRACT FROM AUTHOR]

With the advancement of AI‐generated human motion, it is of increasing importance to think about how we distinguish real human motion from machine‐generated movements. In this study, we recruited professional performers to use the whole body to make a short movement to inform potential observers that they are real humans (instead of machines). Their movements were captured with a motion capture system (Vicon) and later reduced to dynamic point‐like displays (biological motion). They were interviewed after the recording to provide their acting strategies. Naive observers who did not participate in the motion data collection were recruited to watch these videos and judge whether the biological motions looked human‐like or not (YES/NO), as well as to report their judging criteria. The major factors extracted from these reports include kinematics, context, body mechanics, and principles of physical laws. We discuss the impact of these criteria and how they may possibly help improve the future generation of human‐like motions. [ABSTRACT FROM AUTHOR]

Evolution requires selection. Molecular/chemical/preDarwinian evolution is no exception. One molecule must be selected over another for molecular evolution to occur and advance. Evolution, however, has no goal. The laws of physics have no utilitarian desire, intent or proficiency. Laws and constraints are blind to "usefulness." How then were potential multi-step processes anticipated, valued and pursued by inanimate nature? Can orchestration of formal systems be physico-chemically spontaneous? The purely physico-dynamic self-ordering of Chaos Theory and irreversible non-equilibrium thermodynamic "engines of disequilibria conversion" achieve neither orchestration nor formal organization. Natural selection is a passive and after-the-fact-of-life selection. Darwinian selection reduces to the differential survival and reproduction of the fittest already-living organisms. In the case of abiogenesis, selection had to be 1) Active, 2) Pre-Function, and 3) Efficacious. Selection had to take place at the molecular level prior to the existence of non-trivial functional processes. It could not have been passive or secondary. What naturalistic mechanisms might have been at play? [ABSTRACT FROM AUTHOR]

With the global expansion of geothermal energy, accurate prediction of pressure and temperature during drilling has become essential for ensuring the safety and efficiency of geothermal wells. Traditional numerical methods, however, often struggle to handle complex wellbore environments due to their high data demands and limited computational accuracy. To address these challenges, this paper introduces an innovative predictive model based on Physics-Informed Neural Networks (PINNs). By integrating physical laws with deep learning, the model theoretically surpasses the limitations of conventional methods. Trained on pressure and temperature data from a geothermal well in the Xiong'an area, the model demonstrates exceptional accuracy and robustness. Additionally, the model was rigorously tested under extreme wellbore conditions, showcasing its strong generalization capabilities. The findings suggest that PINNs offer a highly advantageous solution for geothermal drilling, with significant potential for practical engineering applications. [ABSTRACT FROM AUTHOR]

Existing traditional ocean vertical-mixing schemes are empirically developed without a thorough understanding of the physical processes involved, resulting in a discrepancy between the parameterization and forecast results. The uncertainty in ocean-mixing parameterization is primarily responsible for the bias in ocean models. Benefiting from deep-learning technology, we design the Adaptive Fully Connected Module with an Inception module as the baseline to minimize bias. It adaptively extracts the best features through fully connected layers with different widths, and better learns the nonlinear relationship between input variables and parameterization fields. Moreover, to obtain more accurate results, we impose KPP (K-Profile Parameterization) and PP (Pacanowski–Philander) schemes as physical constraints to make the network parameterization process follow the basic physical laws more closely. Since model data are calculated with human experience, lacking some unknown physical processes, which may differ from the actual data, we use a decade-long time record of hydrological and turbulence observations in the tropical Pacific Ocean as training data. Combining physical constraints and a nonlinear activation function, our method catches its nonlinear change and better adapts to the ocean-mixing parameterization process. The use of physical constraints can improve the final results. [ABSTRACT FROM AUTHOR]

In recent years, many researchers have used machine learning approaches to bridge the relationship between big data and physics in the practical engineering field. However, the widely used machine learning models are highly dependent on the quality and quantity of data. These long-term monitoring data usually are expensive to obtain in water system. This paper presents a novel neural network structure, the physics-informed neural network (PINN), which can implement the shallow-water equations (SWEs) directly so that the training stage is based fully on physical laws. Similar to numerical models, our PINN model requires the same data as the numerical method, e.g., boundary conditions, the digital elevation of the terrain, and so forth. Because the SWEs are solved directly in our framework, this framework can be understood as a data-free method. The PINN was tested using two case studies: a flow spike in a hypothetical trapezoidal channel, and a historical scenario of downstream Cypress Creek, Houston. The results indicated great agreement with the widely used numerical solver, HEC-RAS. [ABSTRACT FROM AUTHOR]

Life on Earth uses DNA as the central template for self‐replication, genetic encoding, and information transfer. However, there are no physical laws precluding life's existence elsewhere in space, and alternative life forms may not need DNA. In the search for exobiology, knowing what to look for as a biosignature remains a challenge – especially if it is not from the obvious list of biologic building blocks. Clues from chemicals recently discovered on Mars and in the Taurus Molecular Cloud 1 (TMC‐1), show that intriguing organic compounds exist beyond Earth, which could provide a starting point for unconventional exobiotic designs. Here we present a new potential self‐replicating system with structural similarities to recently discovered compounds on Mars and TMC‐1. Rather than using DNA's hydrogen‐bonding motif for reliable base‐paring, our design employs sulfur‐nitrogen interactions to selectively template unique benzothiadiazole units in sequence. We synthesized and studied two versions of this system, one reversible and the other irreversible, and found experimental evidence of self‐replication in d‐chloroform solvent. These results are part of a larger pursuit in our lab for developing a basis for a potential exobiological system using starting blocks closely related to these cosmic compounds. [ABSTRACT FROM AUTHOR]