Six-dimensional force/torque sensor for aerodynamic characteristic study of high-speed train with different wind angles under stationary tornado.

Researchers conducted an investigation by tornado simulator to study the impact of wind angle on the aerodynamic characteristics of a reduced (1:150) high-speed train model using six-dimensional force/torque sensor. The reduced scale model size can match the relative size relationship between high-speed train and tornado vortex core in real condition. Results show that the wind angle affects the mean value and standard deviation of the force and moment coefficient of the high-speed train at the same radial position. The variations of the mean value and standard deviation of the pitching moment coefficient of the high-speed train carriage model at 60°and 90°are different from that at other wind angles. The variations of the mean value of the pitching moment coefficient of the high-speed train head model at 0°, 15°and 30°are different from that at other wind angles. The variations of the standard deviation of the pitching moment coefficient of the high-speed train head model at 60°,75°and 90°are different from that at other wind angles. This research will help the further study of the operation safety of high-speed train in the event of a tornado impacting a high-speed train network.

Efficient removal of phosphorus and nitrogen from aquatic environment using sepiolite-MgO nanocomposites: preparation, characterization, removal performance, and mechanism.

Excessive phosphorus will lead to eutrophication in aquatic environment; the efficient removal of phosphorus is crucial for wastewater engineering and surface water management. This study aimed to fabricate a nanorod-like sepiolite-supported MgO (S-MgO) nanocomposite with high specific surface area for efficient phosphate removal using a facile microwave-assisted method and calcining processes. The impact of solution pH, adsorbent dosage, contact time, initial phosphate concentrations, Ca2+ addition, and N/P ratio on the phosphate removal was extensively examined by the batch experiments. The findings demonstrated that the S-MgO nanocomposite exhibited effective removal performance for low-level phosphate (0 ~ 2.0 mM) within the pH range of 3.0 ~ 10.0. Additionally, the nanocomposite can synchronously remove phosphate and ammonium in high-level nutrient conditions (> 2.0 mM), with the maximum removal capacities of 188.49 mg P/g and 89.78 mg N/g. Quantitative and qualitative analyses confirmed the successful harvesting of struvite in effluent with high-phosphate concentrations, with the mechanisms involved attributed to a synergistic combination of sorption and struvite crystallization. Due to its proficient phosphate removal efficiency, cost-effectiveness, and substantial removal capacity, the developed S-MgO nanocomposite exhibits promising potential for application in phosphorus removal from aquatic environments.

A minimal model of peripheral clocks reveals differential circadian re-entrainment in aging.

The mammalian circadian system comprises a network of endogenous oscillators, spanning from the central clock in the brain to peripheral clocks in other organs. These clocks are tightly coordinated to orchestrate rhythmic physiological and behavioral functions. Dysregulation of these rhythms is a hallmark of aging, yet it remains unclear how age-related changes lead to more easily disrupted circadian rhythms. Using a two-population model of coupled oscillators that integrates the central clock and the peripheral clocks, we derive simple mean-field equations that can capture many aspects of the rich behavior found in the mammalian circadian system. We focus on three age-associated effects that have been posited to contribute to circadian misalignment: attenuated input from the sympathetic pathway, reduced responsiveness to light, and a decline in the expression of neurotransmitters. We find that the first two factors can significantly impede re-entrainment of the clocks following perturbation, while a weaker coupling within the central clock does not affect the recovery rate. Moreover, using our minimal model, we demonstrate the potential of using the feed-fast cycle as an effective intervention to accelerate circadian re-entrainment. These results highlight the importance of peripheral clocks in regulating the circadian rhythm and provide fresh insights into the complex interplay between aging and the resilience of the circadian system.

Optimal synchronization in pulse-coupled oscillator networks using reinforcement learning.

Spontaneous synchronization is ubiquitous in natural and man-made systems. It underlies emergent behaviors such as neuronal response modulation and is fundamental to the coordination of robot swarms and autonomous vehicle fleets. Due to its simplicity and physical interpretability, pulse-coupled oscillators has emerged as one of the standard models for synchronization. However, existing analytical results for this model assume ideal conditions, including homogeneous oscillator frequencies and negligible coupling delays, as well as strict requirements on the initial phase distribution and the network topology. Using reinforcement learning, we obtain an optimal pulse-interaction mechanism (encoded in phase response function) that optimizes the probability of synchronization even in the presence of nonideal conditions. For small oscillator heterogeneities and propagation delays, we propose a heuristic formula for highly effective phase response functions that can be applied to general networks and unrestricted initial phase distributions. This allows us to bypass the need to relearn the phase response function for every new network.

Effect of ambient wind on the performance of alpine downhill skier.

Alpine skiing, especially alpine downhill, is one of the most extreme winter sports in terms of high-speed and narrow winning margin, and its tracks are always located in mountainous areas with high altitudes and complex ambient wind fields, resulting in a significant impact of ambient wind on the performance and the final ranking of alpine downhill skiers. In the present study, a method based upon the combination of field measurements, wind tunnel tests and kinematic simulations was used to evaluate the effect of ambient wind on the performance of an alpine downhill skier. Considering the effect of ambient wind, a kinematic model of the alpine skier-ski system was established, and the equations of motion for straight gliding and turning were deduced. Then, the Chinese National Alpine Ski Center (CNASC) downhill track was taken as a case study to investigate the effect of ambient wind on the gliding time using the proposed combined evaluation method. Field measurements and wind tunnel tests were performed to identify five critical ambient wind directions of 270°, 292.5°, 315°, 337.5° and 360°. Moreover, the wind speeds and the wind directions for 16 different measurement points of the downhill track were also obtained. The results of the modelling analysis showed that the finish time increased by 19.75% for the ambient wind direction of 270°, whereas the finish time decreased by 1.29% for the ambient wind direction of 360°.

Higher-order interactions shape collective dynamics differently in hypergraphs and simplicial complexes.

Higher-order networks have emerged as a powerful framework to model complex systems and their collective behavior. Going beyond pairwise interactions, they encode structured relations among arbitrary numbers of units through representations such as simplicial complexes and hypergraphs. So far, the choice between simplicial complexes and hypergraphs has often been motivated by technical convenience. Here, using synchronization as an example, we demonstrate that the effects of higher-order interactions are highly representation-dependent. In particular, higher-order interactions typically enhance synchronization in hypergraphs but have the opposite effect in simplicial complexes. We provide theoretical insight by linking the synchronizability of different hypergraph structures to (generalized) degree heterogeneity and cross-order degree correlation, which in turn influence a wide range of dynamical processes from contagion to diffusion. Our findings reveal the hidden impact of higher-order representations on collective dynamics, highlighting the importance of choosing appropriate representations when studying systems with nonpairwise interactions.

Basins with Tentacles.

To explore basin geometry in high-dimensional dynamical systems, we consider a ring of identical Kuramoto oscillators. Many attractors coexist in this system; each is a twisted periodic orbit characterized by a winding number q, with basin size proportional to e^{-kq^{2}}. We uncover the geometry behind this size distribution and find the basins are octopuslike, with nearly all their volume in the tentacles, not the head of the octopus (the ball-like region close to the attractor). We present a simple geometrical reason why basins with tentacles should be common in high-dimensional systems.

Designing temporal networks that synchronize under resource constraints.

Being fundamentally a non-equilibrium process, synchronization comes with unavoidable energy costs and has to be maintained under the constraint of limited resources. Such resource constraints are often reflected as a finite coupling budget available in a network to facilitate interaction and communication. Here, we show that introducing temporal variation in the network structure can lead to efficient synchronization even when stable synchrony is impossible in any static network under the given budget, thereby demonstrating a fundamental advantage of temporal networks. The temporal networks generated by our open-loop design are versatile in the sense of promoting synchronization for systems with vastly different dynamics, including periodic and chaotic dynamics in both discrete-time and continuous-time models. Furthermore, we link the dynamic stabilization effect of the changing topology to the curvature of the master stability function, which provides analytical insights into synchronization on temporal networks in general. In particular, our results shed light on the effect of network switching rate and explain why certain temporal networks synchronize only for intermediate switching rate.

Random heterogeneity outperforms design in network synchronization.

A widely held assumption on network dynamics is that similar components are more likely to exhibit similar behavior than dissimilar ones and that generic differences among them are necessarily detrimental to synchronization. Here, we show that this assumption does not generally hold in oscillator networks when communication delays are present. We demonstrate, in particular, that random parameter heterogeneity among oscillators can consistently rescue the system from losing synchrony. This finding is supported by electrochemical-oscillator experiments performed on a multielectrode array network. Remarkably, at intermediate levels of heterogeneity, random mismatches are more effective in promoting synchronization than parameter assignments specifically designed to facilitate identical synchronization. Our results suggest that, rather than being eliminated or ignored, intrinsic disorder in technological and biological systems can be harnessed to help maintain coherence required for function.

Synchronizing Chaos with Imperfections.

Previous research on nonlinear oscillator networks has shown that chaos synchronization is attainable for identical oscillators but deteriorates in the presence of parameter mismatches. Here, we identify regimes for which the opposite occurs and show that oscillator heterogeneity can synchronize chaos for conditions under which identical oscillators cannot. This effect is not limited to small mismatches and is observed for random oscillator heterogeneity on both homogeneous and heterogeneous network structures. The results are demonstrated experimentally using networks of Chua's oscillators and are further supported by numerical simulations and theoretical analysis. In particular, we propose a general mechanism based on heterogeneity-induced mode mixing that provides insights into the observed phenomenon. Since individual differences are ubiquitous and often unavoidable in real systems, it follows that such imperfections can be an unexpected source of synchronization stability.

Molecular mechanism of secreted amyloid-ß precursor protein in binding and modulating GABABR1a.

A recent phenomenal study discovered that the extension domain of secreted amyloid-ß precursor protein (sAPP) can bind to the intrinsically disordered sushi 1 domain of the γ-aminobutyric acid type B receptor subunit 1a (GABABR1a) and modulate its synaptic transmission. The work provided an important structural foundation for the modulation of GABABR1a; however, the detailed molecular interaction mechanism, crucial for future drug design, remains elusive. Here, we further investigated the dynamical interactions between sAPP peptides and the natively unstructured sushi 1 domain using all-atom molecular dynamics simulations, for both the 17-residue sAPP peptide (APP 17-mer) and its minimally active 9 residue segment (APP 9-mer). We then explored mutations of the APP 9-mer with rigorous free energy perturbation (FEP) calculations. Our in silico mutagenesis studies revealed key residues (D4, W6, and W7) responsible for the binding with the sushi 1 domain. More importantly, one double mutation based on different vertebrate APP sequences from evolution exhibited a stronger binding (ΔΔG = -1.91 ± 0.66 kcal mol-1), indicating a potentially enhanced GABABR1a modulator. These large-scale simulations may provide new insights into the binding mechanism between sAPP and the sushi 1 domain, which could open new avenues in the development of future GABABR1a-specific therapeutics.

Mechanism for Strong Chimeras.

Chimera states have attracted significant attention as symmetry-broken states exhibiting the unexpected coexistence of coherence and incoherence. Despite the valuable insights gained from analyzing specific systems, an understanding of the general physical mechanism underlying the emergence of chimeras is still lacking. Here, we show that many stable chimeras arise because coherence in part of the system is sustained by incoherence in the rest of the system. This mechanism may be regarded as a deterministic analog of noise-induced synchronization and is shown to underlie the emergence of strong chimeras. These are chimera states whose coherent domain is formed by identically synchronized oscillators. Recognizing this mechanism offers a new meaning to the interpretation that chimeras are a natural link between coherence and incoherence.

Applying Artificial Intelligence Methods for the Estimation of Disease Incidence: The Utility of Language Models.

Background: AI-driven digital health tools often rely on estimates of disease incidence or prevalence, but obtaining these estimates is costly and time-consuming. We explored the use of machine learning models that leverage contextual information about diseases from unstructured text, to estimate disease incidence. Methods: We used a class of machine learning models, called language models, to extract contextual information relating to disease incidence. We evaluated three different language models: BioBERT, Global Vectors for Word Representation (GloVe), and the Universal Sentence Encoder (USE), as well as an approach which uses all jointly. The output of these models is a mathematical representation of the underlying data, known as "embeddings." We used these to train neural network models to predict disease incidence. The neural networks were trained and validated using data from the Global Burden of Disease study, and tested using independent data sourced from the epidemiological literature. Findings: A variety of language models can be used to encode contextual information of diseases. We found that, on average, BioBERT embeddings were the best for disease names across multiple tasks. In particular, BioBERT was the best performing model when predicting specific disease-country pairs, whilst a fusion model combining BioBERT, GloVe, and USE performed best on average when predicting disease incidence in unseen countries. We also found that GloVe embeddings performed better than BioBERT embeddings when applied to country names. However, we also noticed that the models were limited in view of predicting previously unseen diseases. Further limitations were also observed with substantial variations across age groups and notably lower performance for diseases that are highly dependent on location and climate. Interpretation: We demonstrate that context-aware machine learning models can be used for estimating disease incidence. This method is quicker to implement than traditional epidemiological approaches. We therefore suggest it complements existing modeling efforts, where data is required more rapidly or at larger scale. This may particularly benefit AI-driven digital health products where the data will undergo further processing and a validated approximation of the disease incidence is adequate.

Topological Control of Synchronization Patterns: Trading Symmetry for Stability.

Symmetries are ubiquitous in network systems and have profound impacts on the observable dynamics. At the most fundamental level, many synchronization patterns are induced by underlying network symmetry, and a high degree of symmetry is believed to enhance the stability of identical synchronization. Yet, here we show that the synchronizability of almost any symmetry cluster in a network of identical nodes can be enhanced precisely by breaking its structural symmetry. This counterintuitive effect holds for generic node dynamics and arbitrary network structure and is, moreover, robust against noise and imperfections typical of real systems, which we demonstrate by implementing a state-of-the-art optoelectronic experiment. These results lead to new possibilities for the topological control of synchronization patterns, which we substantiate by presenting an algorithm that optimizes the structure of individual clusters under various constraints.

Asymmetry-induced synchronization in oscillator networks.

A scenario has recently been reported in which in order to stabilize complete synchronization of an oscillator network-a symmetric state-the symmetry of the system itself has to be broken by making the oscillators nonidentical. But how often does such behavior-which we term asymmetry-induced synchronization (AISync)-occur in oscillator networks? Here we present the first general scheme for constructing AISync systems and demonstrate that this behavior is the norm rather than the exception in a wide class of physical systems that can be seen as multilayer networks. Since a symmetric network in complete synchrony is the basic building block of cluster synchronization in more general networks, AISync should be common also in facilitating cluster synchronization by breaking the symmetry of the cluster subnetworks.

Graphene-Induced Pore Formation on Cell Membranes.

Examining interactions between nanomaterials and cell membranes can expose underlying mechanisms of nanomaterial cytotoxicity and guide the design of safer nanomedical technologies. Recently, graphene has been shown to exhibit potential toxicity to cells; however, the molecular processes driving its lethal properties have yet to be fully characterized. We here demonstrate that graphene nanosheets (both pristine and oxidized) can produce holes (pores) in the membranes of A549 and Raw264.7 cells, substantially reducing cell viability. Electron micrographs offer clear evidence of pores created on cell membranes. Our molecular dynamics simulations reveal that multiple graphene nanosheets can cooperate to extract large numbers of phospholipids from the membrane bilayer. Strong dispersion interactions between graphene and lipid-tail carbons result in greatly depleted lipid density within confined regions of the membrane, ultimately leading to the formation of water-permeable pores. This cooperative lipid extraction mechanism for membrane perforation represents another distinct process that contributes to the molecular basis of graphene cytotoxicity.

Folding and Stabilization of Native-Sequence-Reversed Proteins.

Though the problem of sequence-reversed protein folding is largely unexplored, one might speculate that reversed native protein sequences should be significantly more foldable than purely random heteropolymer sequences. In this article, we investigate how the reverse-sequences of native proteins might fold by examining a series of small proteins of increasing structural complexity (α-helix, ß-hairpin, α-helix bundle, and α/ß-protein). Employing a tandem protein structure prediction algorithmic and molecular dynamics simulation approach, we find that the ability of reverse sequences to adopt native-like folds is strongly influenced by protein size and the flexibility of the native hydrophobic core. For ß-hairpins with reverse-sequences that fail to fold, we employ a simple mutational strategy for guiding stable hairpin formation that involves the insertion of amino acids into the ß-turn region. This systematic look at reverse sequence duality sheds new light on the problem of protein sequence-structure mapping and may serve to inspire new protein design and protein structure prediction protocols.

DNA translocation through single-layer boron nitride nanopores.

Ultra-thin nanopores have become promising biological sensors because of their outstanding signal-to-noise ratio and spatial resolution. Here, we show that boron nitride (BN), which is a new two-dimensional (2D) material similar to graphene, could be utilized for making a nanopore with an atomic thickness. Using an all-atom molecular dynamics simulation, we investigated the dynamics of DNA translocation through the BN nanopore. The results of our simulations demonstrated that it is possible to detect different double-stranded DNA (dsDNA) sequences from the recording of ionic currents through the pore during the DNA translocation. Surprisingly, opposite to results for a graphene nanopore, we found the calculated blockage current for poly(A-T)40 in a BN nanopore to be less than that for poly(G-C)40. Also in contrast with the case of graphene nanopores, dsDNA models moved smoothly and in an unimpeded manner through the BN nanopores in the simulations, suggesting a potential advantage for using BN nanopores to design stall-free sequencing devices. BN nanopores, which display several properties (such as being hydrophilic and non-metallic) that are superior to those of graphene, are thus expected to find applications in the next generation of high-speed and low-cost biological sensors.

Bio-mimicking of proline-rich motif applied to carbon nanotube reveals unexpected subtleties underlying nanoparticle functionalization.

Here, we report computational studies of the SH3 protein domain interacting with various single-walled carbon nanotubes (SWCNT) either bare or functionalized by mimicking the proline-rich motif (PRM) ligand (PPPVPPRR) and compare it to the SH3-PRM complex binding. With prolines or a single arginine attached, the SWCNT gained slightly on specificity when compared with the bare control, whereas with multi-arginine systems the specificity dropped dramatically to our surprise. Although the electrostatic interaction provided by arginines is crucial in the recognition between PRM and SH3 domain, our results suggest that attaching multiple arginines to the SWCNT has a detrimental effect on the binding affinity. Detailed analysis of the MD trajectories found two main factors that modulate the specificity of the binding: the existence of competing acidic patches at the surface of SH3 that leads to "trapping and clamping" by the arginines, and the rigidity of the SWCNT introducing entropic penalties in the proper binding. Further investigation revealed that the same "clamping" phenomenon exits in the PRM-SH3 system, which has not been reported in previous literature. The competing effects between nanoparticle and its functionalization components revealed by our model system should be of value to current and future nanomedicine designs.

Capability of charge signal conversion and transmission by water chains confined inside Y-shaped carbon nanotubes.

The molecular scale signal conversion, transmission, and amplification by a single external charge through a water-mediated Y-shaped nanotube have been studied using molecular dynamics simulations. Our results show that the signal converting capability is highly sensitive to the magnitude of the charge, while the signal transmitting capability is independent of the charge signal. There is a sharp two-state-like transition in the signal converting capacity for both positive and negative charges. When the charge magnitude is above a threshold (|q| ≥ ~0.7 e), the water dipole orientations in the main tube can be effectively controlled by the signaling charge (i.e., signal conversion), and then be transmitted and amplified through the Y-junction, despite the thermal noises and interferences between branch signals. On the other hand, the signal transmitting capability, characterized by the correlation between the two water dipole orientations in the two branches, is found to be always larger than 0.6, independent of charge signals, indicating that the water-mediated Y-tube is an excellent signal transmitter. These findings may provide useful insights for the future design of molecular scale signal processing devices based on Y-shaped nanotubes.