Reconstructing transient pressures in pipe networks from local observations by using physics-informed neural networks.

Reconstructing transient states presents significant challenges, particularly within complex pipe networks. These challenges arise due to nonlinear behaviours, inherent uncertainties in the system, and limitations in data availability. This work proposed a novel approach employing Physics-Informed Neural Networks (PINN) to reconstruct transient states in pipe networks, even with limited sensor data. To integrate the complex topology of pipe network systems into neural networks, the method integrates the PINN framework with an efficient elastic water column (EWC) model which can be simply formulated across diverse pipe network configurations. The results showed the proposed PINN method can accurately reconstruct the pressure and flow variation at unmonitored locations, even provided with noisy data at a limited number of locations. One of its advantages lies in its ability to effectively capture extreme values that hold potential significance for pipe infrastructure, providing a promising avenue for pipe failure analysis and enhanced safety management. Laboratory experiments have also been conducted to validate the efficacy and reliability of this method, thus further underlining its potential for real-world applications.

-Bottlebrush Networks: A Primer for Advanced Architectures.

Bottlebrush networks (BBNs) are an exciting new class of materials with interesting physical properties derived from their unique architecture. While great strides have been made in our fundamental understanding of bottlebrush polymers and networks, an interdisciplinary approach is necessary for the field to accelerate advancements. This perspective aims to act as a primer to BBN chemistry and physics for both new and current members of the community. In addition to providing an overview of contemporary BBN synthetic methods, we developed a workflow and desktop application (LengthScale), enabling bottlebrush physics to be more approachable. We conclude by addressing several topical issues and asking a series of pointed questions to stimulate conversation within the community.

Harmonic resonance and entrainment of propagating chemical waves by external mechanical stimulation in BZ self-oscillating hydrogels.

Smart polymer materials that are nonliving yet exhibit complex "life-like" or biomimetic behaviors have been the focus of intensive research over the past decades, in the quest to broaden our understanding of how living systems function under nonequilibrium conditions. Identification of how chemical and mechanical coupling can generate resonance and entrainment with other cells or external environment is an important research question. We prepared Belousov-Zhabotinsky (BZ) self-oscillating hydrogels which convert chemical energy to mechanical oscillation. By cyclically applying external mechanical stimulation to the BZ hydrogels, we found that when the oscillation of a gel sample entered into harmonic resonance with the applied oscillation during stimulation, the system kept a "memory" of the resonant oscillation period and maintained it post stimulation, demonstrating an entrainment effect. More surprisingly, by systematically varying the cycle length of the external stimulation, we revealed the discrete nature of the stimulation-induced resonance and entrainment behaviors in chemical oscillations of BZ hydrogels, i.e., the hydrogels slow down their oscillation periods to the harmonics of the cycle length of the external mechanical stimulation. Our theoretical model calculations suggest the important roles of the delayed mechanical response caused by reactant diffusion and solvent migration in affecting the chemomechanical coupling in active hydrogels and consequently synchronizing their chemical oscillations with external mechanical oscillations.

Mediation of collisionless turbulent dissipation through cyclotron resonance.

The dissipation of turbulence in astrophysical systems is fundamental to energy transfer and heating in environments ranging from the solar wind and corona to accretion disks and the intracluster medium. Although turbulent dissipation is relatively well understood in fluid dynamics, astrophysical plasmas often exhibit exotic behaviour, arising from the lack of interparticle collisions, which complicates turbulent dissipation and heating in these systems. Recent observations by NASA's Parker Solar Probe mission in the inner heliosphere have shed new light on the role of ion cyclotron resonance as a potential candidate for turbulent dissipation and plasma heating. Here, using in situ observations of turbulence and wave populations, we show that ion cyclotron waves provide a major pathway for dissipation and plasma heating in the solar wind. Our results support recent theoretical predictions of turbulence in the inner heliosphere, known as the helicity barrier, that suggest a role of cyclotron resonance in ion-scale dissipation. Taken together, these results provide important constraints for turbulent dissipation and acceleration efficiency in astrophysical plasmas.

Self-injection-locked thin-film regenerative laser amplifier.

Organic lasers based on distributed feedback (DFB) microcavities have been extensively investigated. However, the application of these lasers is limited by their low output power and large beam divergence. Therefore, laser amplifiers are needed to achieve practically applicable laser intensity and controllable lasing modes for far-field applications. In this work, we report self-injection-locked laser amplifiers using the combination of a DFB microcavity and a Bragg reflector, where a high-reflection mirror acts as the Bragg reflector and its feedback supplies the external-cavity injection. The coherent coupling between the DFB microcavity and the Bragg amplifier is crucial for achieving high conversion efficiency and high-contrast transverse modes. An amplification factor larger than 20 and a single output laser spot with high contrast that has been achieved. Such an integration design of the self-injected DFB microcavity amplifier can be directly utilized in the realization of high-performance thin-film laser sources for practical applications.

Lidar as a potential tool for monitoring migratory insects.

The seasonal migrations of insects involve a substantial displacement of biomass with significant ecological and economic consequences for regions of departure and arrival. Remote sensors have played a pivotal role in revealing the magnitude and general direction of bioflows above 150 m. Nevertheless, the takeoff and descent activity of insects below this height is poorly understood. Our lidar observations elucidate the low-height dusk movements and detailed information of insects in southern Sweden from May to July, during the yearly northward migration period. Importantly, by filtering out moths from other insects based on optical information and wingbeat frequency, we have introduced a promising new method to monitor the flight activities of nocturnal moths near the ground, many of which participate in migration through the area. Lidar thus holds the potential to enhance the scientific understanding of insect migratory behavior and improve pest control strategies.

Machine learning interatomic potential: Bridge the gap between small-scale models and realistic device-scale simulations.

Machine learning interatomic potential (MLIP) overcomes the challenges of high computational costs in density-functional theory and the relatively low accuracy in classical large-scale molecular dynamics, facilitating more efficient and precise simulations in materials research and design. In this review, the current state of the four essential stages of MLIP is discussed, including data generation methods, material structure descriptors, six unique machine learning algorithms, and available software. Furthermore, the applications of MLIP in various fields are investigated, notably in phase-change memory materials, structure searching, material properties predicting, and the pre-trained universal models. Eventually, the future perspectives, consisting of standard datasets, transferability, generalization, and trade-off between accuracy and complexity in MLIPs, are reported.

Resonance tuning of rhythmic movements is disrupted at short time scales: A centrifuge study.

The human body exploits its neural mechanisms to optimize actions. Rhythmic movements are optimal when their frequency is close to the natural frequency of the system. In a pendulum, gravity modulates this spontaneous frequency. Participants unconsciously adjust their natural pace when cyclically moving the arm in altered gravity. However, the timescale of this adaptation is unexplored. Participants performed cyclic movements before, during, and after fast transitions between hypergravity levels (1g-3g and 3g-1g) induced by a human centrifuge. Movement periods were modulated with the average value of gravity during transitions. However, while participants increased movement pace on a cycle basis when gravity increased (1g-3g), they did not decrease pace when gravity decreased (3g-1g). We highlight asymmetric effects in the spontaneous adjustment of movement dynamics on short timescales, suggesting the involvement of cognitive factors, beyond standard dynamical models.

Dwell Times, Wavepacket Dynamics, and Quantum Trajectories for Particles with Spin 1/2.

The theoretical connections between quantum trajectories and quantum dwell times, previously explored in the context of 1D time-independent stationary scattering applications, are here generalized for multidimensional time-dependent wavepacket applications for particles with spin 1/2. In addition to dwell times, trajectory-based dwell time distributions are also developed, and compared with previous distributions based on the dwell time operator and the flux-flux correlation function. Dwell time distributions are of interest, in part because they may be of experimental relevance. In addition to standard unipolar quantum trajectories, bipolar quantum trajectories are also considered, and found to relate more directly to the dwell time (and other quantum time) quantities of greatest relevance for scattering applications. Detailed calculations are performed for a benchmark 3D spin-1/2 particle application, considered previously in the context of computing quantum arrival times.

Physical Mechanism of Nanocrystalline Composite Deformation Responsible for Fracture Plastic Nature at Cryogenic Temperatures.

In this work, we consider the physical basis of deformation and fracture in layered composite nanocrystalline/amorphous material-low-melting crystalline alloy in a wide temperature range. Deformation and fracture at the crack tip on the boundary of such materials as nanocrystalline alloy of the trademark 5BDSR, amorphous alloy of the trademark 82K3XSR and low-melting crystalline alloy were experimentally investigated. The crack was initiated by uniaxial stretching in a temperature range of 77-293 K. A theoretical description of the processes of deformation and fracture at the crack tip is proposed, with the assumption that these processes lead to local heating and ensure the plastic character of crack growth at liquid nitrogen temperatures. The obtained results improve the theoretical understanding of the physics of fracture at the boundary of nanocrystalline and crystalline alloys in a wide temperature range. The possibility of preserving the plastic nature of fracture in a thin boundary layer of crystalline-nanocrystalline material at cryogenic temperatures has been experimentally shown.

Machine learning and statistical physics modeling of tetracycline adsorption using activated carbon derived from Cynometra ramiflora fruit biomass.

The current investigation reports the usage of adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN), the two recognized machine learning techniques in modelling tetracycline (TC) adsorption onto Cynometra ramiflora fruit biomass derived activated carbon (AC). Many characterization methods utilized, confirmed the porous structure of synthesized AC. ANN and ANFIS models utilized pH, dose, initial TC concentration, mixing speed, time duration, and temperature as input parameters, whereas TC removal percentage was designated as the output parameter. The optimized configuration for the ANN model was determined as 6-8-1, while the ANFIS model employed trimf input and linear output membership functions. The obtained results showed a strong correlation, indicated by high R2 values (ANNR2: 0.9939 & ANFISR2: 0.9906) and low RMSE values (ANNRMSE: 0.0393 & ANFISRMSE: 0.0503). Apart from traditional isotherms, the dataset was fitted to statistical physics models wherein, the double-layer with a single energy satisfactorily explained the physisorption mechanism of TC adsorption. The sorption energy was 21.06 kJ/mol, and the number of TC moieties bound per site (n) was found to be 0.42, conclusive of parallel binding of TC molecules to the adsorbent surface. The adsorption capacity at saturation (Qsat) was estimated to be 466.86 mg/g - appreciably more than previously reported values. These findings collectively demonstrate that the AC derived from C. ramiflora fruit holds great potential for efficient removal of TC from a given system, and machine learning approaches can effectively model the adsorption processes.

Time-modulated near-field radiative heat transfer.

Near-field radiative heat transfer has recently attracted increasing interests for its applications in energy technologies, such as thermophotovoltaics. Existing works, however, are restricted to time-independent systems. Here, we explore near-field radiative heat transfer between two bodies under time modulation by developing a rigorous fluctuational electrodynamics formalism. We demonstrate that time modulation can result in the enhancement, suppression, elimination, or reversal of radiative heat flow between the two bodies, and can be used to create a radiative thermal diode with an infinite contrast ratio, as well as a near-field radiative heat engine that pumps heat from the cold to the hot bodies. The formalism reveals a fundamental symmetry relation in the radiative heat transfer coefficients that underlies these effects. Our results indicate the significant capabilities of time modulation for managing nanoscale radiative heat flow.

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.

AAPM WGVRTO report 390: A survey of veterinary radiation oncology equipment and infrastructure in 2022.

Since 2010, there has been little published data on the state of equipment and infrastructure in veterinary radiation oncology clinical practice. These data are important not only to identify the status and use of technology within the veterinary radiation oncology community but also to help identify the extent of medical physics support. The purpose of our study is to report findings from a survey of veterinary radiation oncologists in the USA, Canada, and select centers outside of North America in 2022. A 40-question survey covering topics such as type of radiotherapy equipment, techniques offered, treatment planning systems and dose calculation algorithms, special techniques, board-certified radiation oncologists and residents, and extent of medical physics support was distributed through an online survey tool. Results from 40 veterinary radiation oncology institutions, with equipment explicitly used for veterinary care, suggest that the current state of practice is not dissimilar to what currently exists in human radiation oncology facilities; techniques and technologies commonly employed include flattening filter-free mode megavoltage beams, volumetric arc therapy, daily cone-beam computed tomography, image-guided radiation therapy, and sophisticated dose calculation algorithms. These findings suggest the need for modern radiation oncology acceptance testing, commissioning, and quality assurance programs within the veterinary community. The increase in veterinary radiation oncology residency positions and increasing sophistication of equipment suggests that increased levels of standardized medical physics support would benefit the veterinary radiation oncology community.