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Abstract The processes governing multilayer nucleation in the chemical vapour deposition (CVD) of graphene are important for obtaining high-quality monolayer sheets, but remain poorly understood. Here we show that higher-order carbon species in the gas-phase play a major role in multilayer nucleation, through the use of in-situ ultraviolet (UV) absorption spectroscopy. These species are the volatilized products of reactions between hydrogen and carbon contaminants that have backstreamed into the reaction chamber from downstream system components. Consequently, we observe a dramatic suppression of multilayer nucleation when backstreaming is suppressed. These results point to an important and previously undescribed mechanism for multilayer nucleation, wherein higher-order gas-phase carbon species play an integral role. Our work highlights the importance of gas-phase dynamics in understanding the overall mechanism of graphene growth.

Abstract While Boolean logic has been the backbone of digital information processing, there exist classes of computationally hard problems wherein this paradigm is fundamentally inefficient. Vertex coloring of graphs, belonging to the class of combinatorial optimization, represents one such problem. It is well studied for its applications in data sciences, life sciences, social sciences and technology, and hence, motivates alternate, more efficient non-Boolean pathways towards its solution. Here we demonstrate a coupled relaxation oscillator based dynamical system that exploits insulator-metal transition in Vanadium Dioxide (VO2) to efficiently solve vertex coloring of graphs. Pairwise coupled VO2 oscillator circuits have been analyzed before for basic computing operations, but using complex networks of VO2 oscillators, or any other oscillators, for more complex tasks have been challenging in theory as well as in experiments. The proposed VO2 oscillator network harnesses the natural analogue between optimization problems and energy minimization processes in highly parallel, interconnected dynamical systems to approximate optimal coloring of graphs. We further indicate a fundamental connection between spectral properties of linear dynamical systems and spectral algorithms for graph coloring. Our work not only elucidates a physics-based computing approach but also presents tantalizing opportunities for building customized analog co-processors for solving hard problems efficiently.

Characterizing the complicated flow behaviors arising from high water cut and low velocity oil-water flows is an important problem of significant challenge. We design a high-speed cycle motivation conductance sensor and carry out experiments for measuring the local flow information from different oil-in-water flow patterns. We first use multivariate time-frequency analysis to probe the typical features of three flow patterns from the perspective of energy and frequency. Then we infer complex networks from multi-channel measurements in terms of phase lag index, aiming to uncovering the phase dynamics governing the transition and evolution of different oil-in-water flow patterns. In particular, we employ spectral radius and weighted clustering coefficient entropy to characterize the derived unweighted and weighted networks and the results indicate that our approach yields quantitative insights into the phase dynamics underlying the high water cut and low velocity oil-water flows.

Compact laser interferometers with large dynamic range are one of the core emerging tools to improve low frequency performance in gravitational wave detectors by providing local displacement sensing with sub 1 pm Hz - 0.5 precision. Strong sinusoidal frequency modulations are used in such laser interferometers to create heterodyne-like photodetector signals from which the phase and other parameters, such as the absolute distance, can be extracted. The nested sinusoidal function in such signals is a challenge for the real-time parameter estimation in low-noise applications. In this article, we present an algorithm to calculate exact signal parameters in a non-iterative way from such interferometric signals. The algorithm makes use of a recurrence relation between Bessel functions to enable a direct extraction of modulation parameters from the signal. Additionally, the algorithm is capable of dealing with high phase dynamics where the Doppler-shift of the signal becomes relevant and can limit the range and precision of the parameter estimation, if not accounted for. Simulations show that the algorithm is computationally efficient, can be well parallelised and the phase estimation is close to optimal precision given by the Cramer–Rao lower bound of the signal parameters. [ABSTRACT FROM AUTHOR]

Analysis of ex vivo Per2 bioluminescent rhythm previously recorded in the mouse dorsal vagal complex reveals a characteristic phase relationship between three distinct circadian oscillators. These signals represent core clock gene expression in the area postrema (AP), the nucleus of the solitary tract (NTS) and the ependymal cells surrounding the 4th ventricle (4Vep). Initially, the data suggests a consistent phasing in which the AP peaks first, followed shortly by the NTS, with the 4Vep peaking 8–9 h later. Wavelet analysis reveals that this pattern is not consistently maintained throughout a recording, however, the phase dynamics strongly imply that oscillator interactions are present. A simple phase model of the three oscillators is developed and it suggests that realistic phase dynamics occur between three model oscillators with coupling close to a synchronisation transition. The coupling topology suggests that the AP bidirectionally communicates phase information to the NTS and the 4Vep to synchronise the three structures. A comparison of the model with previous experimental manipulations demonstrates its feasibility to explain DVC circadian phasing. Finally, we show that simulating steadily decaying coupling improves the model's ability to capture experimental phase dynamics. [ABSTRACT FROM AUTHOR]

Random lasers (RLs) are remarkable experimental platforms to advance the understanding of complex systems phenomena, such as the replica-symmetry-breaking (RSB) spin glass phase, dynamics modes correlations, and turbulence. Here we study these three phenomena jointly in a Nd:YAG based RL synthesized for the first time using a spray pyrolysis method. We propose a couple of modified Pearson correlation coefficients that are simultaneously sensitive to the emergence and fading out of photonic intermittency turbulent-like effects, dynamics evolution of modes correlations, and onset of RSB behavior. Our results show how intertwined these phenomena are in RLs, and suggest that they might share some common underlying mechanisms, possibly approached in future theoretical models under a unified treatment. [ABSTRACT FROM AUTHOR]

Attention facilitates the gating of information from the sending brain area to the receiving areas, with this being achieved by dynamical changes in effective connectivity, which refers to the directional influences between cortical areas. To probe the effective connectivity and cortical excitability modulated by covertly shifted attention, transcranial magnetic stimulation (TMS) was used to directly perturb the right retinotopic visual cortex with respect to attended and unattended locations, and the impact of this was tracked from the stimulated area to other areas by concurrent use of electroencephalography (EEG). TMS to the contralateral visual hemisphere led to a stronger evoked potential than stimulation to the ipsilateral hemisphere. Moreover, stronger beta- and gamma-band effective connectivities assessed as time-delayed phase synchronizations between stimulated areas and other areas were observed when TMS was delivered to the contralateral hemisphere. These effects were more enhanced when they preceded more prominent alpha lateralization, which is known to be associated with attentional gating. Our results indicate that attention-regulated cortical feedforward effective connectivity can be probed by TMS-EEG with direct cortical stimulation, thereby bypassing thalamic gating. These results suggest that cortical gating of the feedforward input is achieved by regulating the effective connectivity in the phase dynamics between cortical areas. [ABSTRACT FROM AUTHOR]

We studied the properties of low-frequency (LF) heart rate variability (HRV) and photoplethysmographic waveform variability (PPGV) and their interaction under conditions where the hemodynamic connection between them is obviously absent, as well as the LF regulation of PPGV in the absence of heart function. The parameters of HRV and finger PPGV were evaluated in 10 patients during cardiac surgery under cardiopulmonary bypass (on-pump cardiac surgery) with or without cardioplegia. The following spectral indices of PPGV and HRV were ertimated: the total spectral power (TP), the high-frequency (HF) and the LF ranges of TP in percents (HF% and LF%), and the LF/HF ratio. We assessed also the index S of synchronization between the LF oscillations in finger photoplethysmogram (PPG) and heart rate (HR) signals. The analysis of directional couplings was carried out using the methods of phase dynamics modeling. It is shown that the mechanisms leading to the occurrence of oscillations in the LF range of PPGV are independent of the mechanisms causing oscillations in the LF range of HRV. At the same time, the both above-mentioned LF oscillations retain their activity under conditions of artificial blood circulation and cardioplegia (the latter case applies only to LF oscillations in PPG). In artificial blood circulation, there was a coupling from the LF oscillations in PPG to those in HR, whereas the coupling in the opposite direction was absent. The coupling from the LF oscillations in PPG to those in HR has probably a neurogenic nature, whereas the opposite coupling has a hemodynamic nature (due to cardiac output). [ABSTRACT FROM AUTHOR]

Gallium-doped zinc oxide (GZO) has demonstrated significant potential in gas-sensing applications due to its enhanced electrical and chemical properties. This study focuses on the synthesis, characterization, and gas-sensing performance of GZO nanoparticles (NPs), specifically targeting CO₂ detection, which is crucial for environmental monitoring and industrial safety. The GZO samples were synthesized using a sol–gel method, and their crystal structure was determined through X-ray diffraction (XRD), confirming the successful incorporation of gallium into the ZnO lattice. X-ray photoelectron spectroscopy (XPS) was employed to analyze the samples' elemental composition and chemical state, revealing the presence of Ga in the ZnO matrix and providing insights into the doping effects. Transmission electron microscopy (TEM) combined with energy-dispersive X-ray spectroscopy (EDS) was used to confirm the purity and elemental distribution of the synthesized samples, ensuring the homogeneity of the Ga doping. In-situ TEM measurements were also conducted on one of the three samples, with the smallest size. The experiment involved exposing the sample to argon (Ar) as a reference gas and carbon dioxide (CO₂) as the target gas to evaluate the sensor's response under real-time conditions. The in-situ TEM provided nanoscale observation of changes in the crystal structure parameters, particularly the d-spacing, which exhibited significant alterations exceeding 3.2% when exposed to CO₂ and Ar gases. Furthermore, electron paramagnetic resonance (EPR) and optical joint density of states (OJDS) analyses were performed to examine the presence of paramagnetic defects and to comprehensively understand the electronic structure within the GZO sample, respectively. [ABSTRACT FROM AUTHOR]

Neurons convert external stimuli into action potentials, or spikes, and encode the contained information into the biological nervous system. Despite the complexity of neurons and the synaptic interactions in between, rate models are often adapted to describe neural encoding with modest success. However, it is not clear whether the firing rate, the reciprocal of the time interval between spikes, is sufficient to capture the essential features for the neuronal dynamics. Going beyond the usual relaxation dynamics in Ginzburg-Landau theory for statistical systems, we propose that neural activities can be captured by the U(1) dynamics, integrating the action potential and the "phase" of the neuron together. The gain function of the Hodgkin-Huxley neuron and the corresponding dynamical phase transitions can be described within the U(1) neuron framework. In addition, the phase dependence of the synaptic interactions is illustrated and the mapping to the Kinouchi-Copelli neuron is established. It suggests that the U(1) neuron is the minimal model for single-neuron activities and serves as the building block of the neuronal network for information processing. [ABSTRACT FROM AUTHOR]

The brain is a complex non-equilibrium system capable of expressing many different dynamics as well as the transitions between them. We hypothesized that the level of non-equilibrium can serve as a signature of a given brain state, which was quantified using the arrow of time (the level of irreversibility). Using this thermodynamic framework, the irreversibility of emergent cortical activity was quantified from local field potential recordings in male Lister-hooded rats at different anesthesia levels and during the sleep-wake cycle. This measure was carried out on five distinct brain states: slow-wave sleep, awake, deep anesthesia–slow waves, light anesthesia–slow waves, and microarousals. Low levels of irreversibility were associated with synchronous activity found both in deep anesthesia and slow-wave sleep states, suggesting that slow waves were the state closest to the thermodynamic equilibrium (maximum symmetry), thus requiring minimum energy. Higher levels of irreversibility were found when brain dynamics became more asynchronous, for example, in wakefulness. These changes were also reflected in the hierarchy of cortical dynamics across different cortical areas. The neural dynamics associated with different brain states were characterized by different degrees of irreversibility and hierarchy, also acting as markers of brain state transitions. This could open new routes to monitoring, controlling, and even changing brain states in health and disease. [ABSTRACT FROM AUTHOR]

At present, researches on brain fatigue recognition are still in the stage of single task and simple brain region network features, while researches on high-order brain functional network features and brain region state mechanisms during fatigue in multi-task scenarios are still insufficient, making it difficult to meet the needs of fatigue recognition under complex conditions. Therefore, this study utilized functional near-infrared spectroscopy (fNIRS) technology to explore the correlation and differences in the low-order and high-order brain functional network attributes of three task induced mental fatigue, and to explore the brain regions that have a major impact on mental fatigue. Self-training algorithms were used to identify the three levels of brain fatigue. The results showed that during the fatigue development, the overall connection strength of the endothelial cell metabolic activity and neural activity frequency bands of the low-order brain functional network first decreased and then increased, while the myogenic activity and heart rate activity frequency bands showed the opposite pattern. Network topology analysis indicated that from no fatigue to mild fatigue, the clustering coefficient of endothelial cell metabolic activity and myogenic activity frequency bands significantly decreased, while the characteristic path length of myogenic activity significantly increased; when experiencing severe fatigue, the small-world attribute of the neural frequency band significantly weakened. However, each frequency band maintained its small-world attribute, reflecting the self-optimization and adaptability of the network during the fatigue process. During mild fatigue, neuronal activity bands' node degree, cluster coefficient, and efficiency rose in high-order brain networks, while low-order networks showed no significant changes. As fatigue progressed, the myogenic activity bands of high-order network properties dominated, but neural bands gained prominence in mild fatigue, approaching the level of myogenic bands in severe fatigue, indicating that brain fatigue orchestrated a shift from myogenic to neural dominance in frequency bands. In addition, during the process of fatigue, the four network attributes of the high-order network cluster composed of low-order nodes related to the prefrontal cortex region, left anterior motor region, motor assist region, and left frontal lobe eye movement region significantly increased, indicating that these brain regions had a significant impact on brain fatigue status. The accuracy of using both high-order and low-order features to identify fatigue levels reached 88.095%, indicating that the combined network features of both high-order and low-order fNIRS signals could effectively detect multi-level mental fatigue, providing innovative ideas for fatigue warning. [ABSTRACT FROM AUTHOR]

Under the support of deep neural networks (DNN), a multifunctional switchable terahertz metamaterial (THz MMs) device is designed and optimized. This device not only achieves ideal ultra-wideband (UWB) absorption in the THz frequency range but enables dual-functional polarization transformation over UWB. When vanadium dioxide (VO2) is in the metallic state, the device as a UWB absorber with an absorption rate exceeding 90% in the 2.43–10 THz range, with a relative bandwidth (RBW) of 145.2%, and its wideband absorption performance is insensitive to polarization. When VO2 is in the insulating state, the device can switch to a polarization converter, achieving conversions from linear to cross polarization and from linear to circular polarization in the ranges of 4.58–10 THz and 4.16–4.43 THz, respectively. Within the 4.58–10 THz range, the polarization conversion ratio approaches 100% with an RBW of 74.3%, the polarization rotation angle is near 90°. Within the 4.16–4.43 THz range, the RBW is 6.29% and the ellipticity ratio approaches 1, Moreover, the effects of incident angle and polarization angle on the operational characteristics are studied. This THz MMs due to its advantages of wide angle, broad bandwidth, and high efficiency, provides valuable references for the research of new multifunctional THz devices. It has great application potential in short-range wireless THz communication, ultrafast optical switches, high-temperature resistant switches, transient spectroscopy, and optical polarization control devices. [ABSTRACT FROM AUTHOR]

A large-scale biophysical network model for the isolated striatal body is developed to optimise potential intrastriatal deep brain stimulation applied to, e.g. obsessive-compulsive disorder. The model is based on modified Hodgkin–Huxley equations with small-world connectivity, while the spatial information about the positions of the neurons is taken from a detailed human atlas. The model produces neuronal spatiotemporal activity patterns segregating healthy from pathological conditions. Three biomarkers were used for the optimisation of stimulation protocols regarding stimulation frequency, amplitude and localisation: the mean activity of the entire network, the frequency spectrum of the entire network (rhythmicity) and a combination of the above two. By minimising the deviation of the aforementioned biomarkers from the normal state, we compute the optimal deep brain stimulation parameters, regarding position, amplitude and frequency. Our results suggest that in the DBS optimisation process, there is a clear trade-off between frequency synchronisation and overall network activity, which has also been observed during in vivo studies. [ABSTRACT FROM AUTHOR]

We investigated a time-delayed optoelectronic oscillator (OEO) that displays a wide range of complex dynamic behavior under small time delay. The phase-space trajectory distributions in different dynamic regimes were compared which brings a new perspective on the underlying mechanism of the transition process. It was found that bifurcation is always possible no matter how small the time delay is even if the universal adiabatic approximation model is invalid. Hereby we proposed a versatile simple oscillator which has a potential capacity as memory carrier and high-dimensional state spatial mapping ability that brings 1000 times computing-efficiency improvements of reservoir computing over the large time delay one. Furthermore, we demonstrated a new approach for a tunable optoelectronic pulse generator (repetition rate at 0.2 MHz and 0.25 GHz) which depends critically on time-delayed input electrical pulse. The proposed oscillator is also a promising system for the applications of fast chaos-based communication. [ABSTRACT FROM AUTHOR]

Freak or rogue waves are a danger to ships, offshore infrastructure, and other maritime equipment. Reliable rogue wave forecasts could mitigate this risk for operations at sea. While the occurrence of oceanic rogue waves at sea is generally acknowledged, reliable rogue wave forecasts are unavailable. In this paper, the authors seek to overcome this shortcoming by demonstrating how rogue waves can be predicted from field measurements. An extensive buoy data set consisting of billions of waves is utilized to parameterize neural networks. This network is trained to distinguish waves prior to an extreme wave from waves which are not followed by an extreme wave. With this approach, three out of four rogue waves are correctly predicted 1 min ahead of time. When the advance warning time is extended to 5 min, it is found that the ratio of accurate predictions is reduced to seven out of ten rogue waves. Another strength of the trained neural networks is their capabilities to extrapolate. This aspect is verified by obtaining forecasts for a buoy location that is not included in the networks' training set. Furthermore, the performance of the trained neural network carries over to realistic scenarios where rogue waves are extremely rare. [ABSTRACT FROM AUTHOR]

The impact of recombinant protein production (RPP) on host cells and the metabolic burden associated with it undermine the efficiency of the production system. This study utilized proteomics to investigate the dynamics of parent and recombinant cells induced at different time points for RPP. The results revealed significant changes in both transcriptional and translational machinery that may have impacted the metabolic burden, growth rate of the culture and the RPP. The timing of protein synthesis induction also played a critical role in the fate of the recombinant protein within the host cell, affecting protein and product yield. The study identified significant differences in the expression of proteins involved in fatty acid and lipid biosynthesis pathways between two E. coli host strains (M15 and DH5⍺), with the E. coli M15 strain demonstrating superior expression characteristics for the recombinant protein. Overall, these findings contribute to the knowledge base for rational strain engineering for optimized recombinant protein production. [ABSTRACT FROM AUTHOR]

It is hypothesized that disparate brain regions interact via synchronous activity to control behavior. The nature of these interconnected ensembles remains an area of active investigation, and particularly the role of high frequency synchronous activity in simplistic behavior is not well known. Using intracranial electroencephalography, we explored the spectral dynamics and network connectivity of sensorimotor cortical activity during a simple motor task in seven epilepsy patients. Confirming prior work, we see a “spectral tilt” (increased high-frequency (HF, 70–100 Hz) and decreased low-frequency (LF, 3–33 Hz) broadband oscillatory activity) in motor regions during movement compared to rest, as well as an increase in LF synchrony between these regions using time-resolved phase-locking. We then explored this phenomenon in high frequency and found a robust but opposite effect, where time-resolved HF broadband phase-locking significantly decreased during movement. This “connectivity tilt” (increased LF synchrony and decreased HF synchrony) displayed a graded anatomical dependency, with the most robust pattern occurring in primary sensorimotor cortical interactions and less robust pattern occurring in associative cortical interactions. Connectivity in theta (3–7 Hz) and high beta (23–27 Hz) range had the most prominent low frequency contribution during movement, with theta synchrony building gradually while high beta having the most prominent effect immediately following the cue. There was a relatively sharp, opposite transition point in both the spectral and connectivity tilt at approximately 35 Hz. These findings support the hypothesis that task-relevant high-frequency spectral activity is stochastic and that the decrease in high-frequency synchrony may facilitate enhanced low frequency phase coupling and interregional communication. Thus, the “connectivity tilt” may characterize behaviorally meaningful cortical interactions. [ABSTRACT FROM AUTHOR]

Reduction of fossil fuel usage, clean energy supply, and dependability are all major benefits of integrating distributed energy resources (DER) with electrical utility grid (UG). Nevertheless, there are difficulties with this integration, most notably accidental islanding that puts worker and equipment safety at risk. Islanding detection methods (IDMs) play a critical role in resolving this problem. All IDMs are thoroughly evaluated in this work, which divides them into two categories: local approaches that rely on distributed generation (DG) side monitoring and remote approaches that make use of communication infrastructure. The study offers a comparative evaluation to help choose the most efficient and applicable IDM, supporting well-informed decision-making for the safe and dependable operation of distributed energy systems within electrical distribution networks. IDMs are evaluated based on NDZ outcomes, detection duration, power quality impact, multi-DG operation, suitability, X/R ratio reliance, and efficient functioning. [ABSTRACT FROM AUTHOR]

Sensorimotor control of complex, dynamic systems such as humanoids or quadrupedal robots is notoriously difficult. While artificial systems traditionally employ hierarchical optimisation approaches or black-box policies, recent results in systems neuroscience suggest that complex behaviours such as locomotion and reaching are correlated with limit cycles in the primate motor cortex. A recent result suggests that, when applied to a learned latent space, oscillating patterns of activation can be used to control locomotion in a physical robot. While reminiscent of limit cycles observed in primate motor cortex, these dynamics are unsurprising given the cyclic nature of the robot's behaviour (walking). In this preliminary investigation, we consider how a similar approach extends to a less obviously cyclic behaviour (reaching). This has been explored in prior work using computational simulations. But simulations necessarily make simplifying assumptions that do not necessarily correspond to reality, so do not trivially transfer to real robot platforms. Our primary contribution is to demonstrate that we can infer and control real robot states in a learnt representation using oscillatory dynamics during reaching tasks. We further show that the learned latent representation encodes interpretable movements in the robot's workspace. Compared to robot locomotion, the dynamics that we observe for reaching are not fully cyclic, as they do not begin and end at the same position of latent space. However, they do begin to trace out the shape of a cycle, and, by construction, they are driven by the same underlying oscillatory mechanics. [ABSTRACT FROM AUTHOR]

Noise is expected to play an important role in the dynamics of analog systems such as coupled oscillators which have recently been explored as a hardware platform for application in computing. In this work, we experimentally investigate the effect of noise on the synchronization of relaxation oscillators and their computational properties. Specifically, in contrast to its typically expected adverse effect, we first demonstrate that a common white noise input induces frequency locking among uncoupled oscillators. Experiments show that the minimum noise voltage required to induce frequency locking increases linearly with the amplitude of the oscillator output whereas it decreases with increasing number of oscillators. Further, our work reveals that in a coupled system of oscillators—relevant to solving computational problems such as graph coloring, the injection of white noise helps reduce the minimum required capacitive coupling strength. With the injection of noise, the coupled system demonstrates frequency locking along with the desired phase-based computational properties at 5 × lower coupling strength than that required when no external noise is introduced. Consequently, this can reduce the footprint of the coupling element and the corresponding area-intensive coupling architecture. Our work shows that noise can be utilized as an effective knob to optimize the implementation of coupled oscillator-based computing platforms. [ABSTRACT FROM AUTHOR]

Refractive index stands as an inherent characteristic of a material, allowing non-invasive exploration of the three-dimensional (3D) interior of the material. Certain materials with different refractive indices produce a birefringence phenomenon in which incident light is split into two polarization components when it passes through the materials. Representative birefringent materials appear in calcite crystals, liquid crystals (LCs), biological tissues, silk fibers, polymer films, etc. If the internal 3D shape of these materials can be visually expressed through a non-invasive method, it can greatly contribute to the semiconductor, display industry, optical components and devices, and biomedical diagnosis. This paper introduces a novel approach employing deep learning to generate 3D birefringence images using multi-viewed holographic interference images. First, we acquired a set of multi-viewed holographic interference pattern images and a 3D volume image of birefringence directly from a polarizing DTT (dielectric tensor tomography)-based microscope system about each LC droplet sample. The proposed model was trained to generate the 3D volume images of birefringence using the two-dimensional (2D) interference pattern image set. Performance evaluations were conducted against the ground truth images obtained directly from the DTT microscopy. Visualization techniques were applied to describe the refractive index distribution in the generated 3D images of birefringence. The results show the proposed method's efficiency in generating the 3D refractive index distribution from multi-viewed holographic interference images, presenting a novel data-driven alternative to traditional methods from the DTT devices. [ABSTRACT FROM AUTHOR]

Choroideremia (CHM) is an X-linked retinal degeneration leading to loss of the photoreceptors, retinal pigment epithelium (RPE), and choroid. Adaptive optics optoretinography is an emerging technique for noninvasive, objective assessment of photoreceptor function. Here, we investigate parafoveal cone function in CHM using adaptive optics optoretinography and compare with cone structure and clinical assessments of vision. Parafoveal cone mosaics of 10 CHM and four normal-sighted participants were imaged with an adaptive optics scanning light ophthalmoscope. While acquiring video sequences, a 2 s 550Δ10 nm, 450 nW/deg2 stimulus was presented. Videos were registered and the intensity of each cone in each frame was extracted, normalized, standardized, and aggregated to generate the population optoretinogram (ORG) over time. A gamma-pdf was fit to the ORG and the peak was extracted as ORG amplitude. CHM ORG amplitudes were compared to normal and were correlated with bound cone density, ellipsoid zone to RPE/Bruch's membrane (EZ-to-RPE/BrM) distance, and foveal sensitivity using Pearson correlation analysis. ORG amplitude was significantly reduced in CHM compared to normal (0.22 ± 0.15 vs. 1.34 ± 0.31). In addition, CHM ORG amplitude was positively correlated with cone density, EZ-to-RPE/BrM distance, and foveal sensitivity. Our results demonstrate promise for using ORG as a biomarker of photoreceptor function. [ABSTRACT FROM AUTHOR]

Silicon (Si) nanoparticles were coated inflight with silver (Ag) atoms using a novel method to prepare multicomponent heterostructured metal-semiconductor nanoparticles. Molecular dynamics (MD) computer simulations were employed, supported by high-resolution bright field (BF) transmission electron microscopy (HRTEM) and aberration-corrected scanning transmission electron microscopy (STEM) with a resolution ⩽0.1 nm in high angle annular dark field (HAADF) mode. These studies revealed that the alloying behavior and phase dynamics during the coating process are more complex than when attaching hetero-atoms to preformed nanoparticles. According to the MD simulations, Ag atoms condense, nucleate and diffuse into the liquid Si nanoparticles in a process that we term "inoculation", and a phase transition begins. Subsequent solidification involves an intermediate alloying stage that enabled us to control the microstructure and crystallinity of the solidified hybrid heterostructured nanoparticles. [ABSTRACT FROM AUTHOR]

How does a train of TMS pulses modify neural activity in humans? Despite adoption of repetitive TMS (rTMS) for the treatment of neuropsychiatric disorders, we still do not understand how rTMS changes the human brain. This limited understanding stems in part from a lack of methods for noninvasively measuring the neural effects of a single TMS train—a fundamental building block of treatment—as well as the cumulative effects of consecutive TMS trains. Gaining this understanding would provide foundational knowledge to guide the next generation of treatments. Here, to overcome this limitation, we developed methods to noninvasively measure causal and acute changes in cortical excitability and evaluated this neural response to single and sequential TMS trains. In 16 healthy adults, standard 10 Hz trains were applied to the dorsolateral prefrontal cortex in a randomized, sham-controlled, event-related design and changes were assessed based on the TMS-evoked potential (TEP), a measure of cortical excitability. We hypothesized that single TMS trains would induce changes in the local TEP amplitude and that those changes would accumulate across sequential trains, but primary analyses did not indicate evidence in support of either of these hypotheses. Exploratory analyses demonstrated non-local neural changes in sensor and source space and local neural changes in phase and source space. Together these results suggest that single and sequential TMS trains may not be sufficient to modulate local cortical excitability indexed by typical TEP amplitude metrics but may cause neural changes that can be detected outside the stimulation area or using phase or source space metrics. This work should be contextualized as methods development for the monitoring of transient noninvasive neural changes during rTMS and contributes to a growing understanding of the neural effects of rTMS. [ABSTRACT FROM AUTHOR]

Photonics-based computing approaches in combination with wavelength division multiplexing offer a potential solution to modern data and bandwidth needs. This paper experimentally takes an important step towards wavelength division multiplexing in an integrated waveguide-based photonic reservoir computing platform by using a single set of readout weights for up to at least 3 ITU-T channels to efficiently scale the data bandwidth when processing a nonlinear signal equalization task on a 28 Gbps modulated on-off keying signal. Using multiple-wavelength training, we obtain bit error rates well below that of the 1.5 × 10 - 2 forward error correction limit at high fiber input powers of 18 dBm, which result in high nonlinear distortion. The results of the reservoir chip are compared to a tapped delay line filter and clearly show that the system performs nonlinear equalization. This was achieved using only limited post processing which in future work can be implemented in optical hardware as well. [ABSTRACT FROM AUTHOR]

This work presents a hydrothermal-based facile method for synthesizing ZnFe2O4, whose size can be controlled with the concentration of sodium acetate used as a fuel and its physical changes at nanoscales when exposed to two different gases. The structural, morphological, compositional, and electronic properties of the synthesized samples are also presented in this paper. The crystal structure of the synthesized samples was determined using an X-ray Diffractometer (XRD). The results revealed fluctuations in the size, lattice parameter, and strain in the nanoparticles with increasing the concentration of sodium acetate. Field-Emission Scanning Electron Microscopy (FESEM) was used to determine synthesized materials' morphology and particle size. It revealed that the particles possessed approximately spherical morphology whose size decreased significantly with the increasing amount of sodium acetate. Transmission Electron Microscopy (TEM) was utilized to determine the structure, morphology, and elemental distributions in particles at the nanoscale, and it confirmed the findings of XRD and FESEM analyses. The high-resolution TEM (HRTEM) imaging analysis of the nanoparticles in our studied samples revealed that the particles predominantly possessed (001) type facets. X-ray photoelectron spectroscopy (XPS) and core-loss electron energy loss spectroscopy (EELS) showed an increasing fraction of Fe2+ with the decreasing size of the particles in samples. The Brunauer, Emmett, and Tellers (BET) analysis of samples revealed a higher surface area as the particle size decreases. In addition, the determined surface area and pore size values are compared with the literature, and it was found that the synthesized materials are promising for gas-sensing applications. The ab initio calculations of the Density of States (DOS) and Band structure of (001) surface terminating ZnFe2O4 were carried out using Quantum Espresso software to determine the bandgap of the synthesized samples. They were compared to their corresponding experimentally determined bandgap values and showed close agreement. Finally, in-situ TEM measurement was carried out on one of the four studied samples with robust properties using Ar and CO2 as reference and target gases, respectively. It is concluded from the presented study that the size reduction of the ZnFe2O4 nanoparticles (NPs) tunes the bandgap and provides more active sites due to a higher concentration of oxygen vacancies. The in-situ TEM showed us a nanoscale observation of the change in one of the crystal structure parameters. The d spacing of ZnFe2O4 NPs showed a noticeable fluctuation, reaching more than 5% upon exposure to CO2 and Ar gases. [ABSTRACT FROM AUTHOR]

Modeling global net ecosystem exchange is essential to understanding and quantifying the complex interactions between the Earth's terrestrial ecosystems and the atmosphere. Emphasizing the inter-relatedness between the global net ecosystem exchange, global sea surface temperature, and atmospheric CO 2 levels, intuitively suggests that all three systems may exhibit collective environmental memory. Motivated by this, we explicitly identified a collective memory function and showed a similar non-Markovian stochastic behavior for these systems exhibiting superdiffusive behavior in short time intervals. We obtained the values of the memory parameter, μ , and the characteristic frequencies, ν , for global net ecosystem exchange (GNEE) ( μ = 0.94 ± 0.03 , ν = 0.67 ± 0.08 / m o . ), global sea surface temperature (GSST) ( μ = 0.68 ± 0.11 , ν = 0.30 ± 0.18 / m o . ), and atmospheric CO 2 ( μ = 0.78 ± 0.08 , ν = 0.66 ± 0.13 / w k . ). The values of the memory parameter are within the range, 0 < μ < 1 , and thus all three systems are in the superdiffusive regime. We emphasize, further, that these results were consistent with our previous analyses at the ecosystem level (i.e. Great Barrier Reef) suggesting scale invariance for these phenomena. Thus, the observed superdiffusive behavior operating at different scales suggests universality of the collective memory function for these systems., (© 2024. The Author(s).)

Paranormal believers' thinking is frequently biased by intuitive beliefs. Lack of inhibition of these tempting beliefs is considered a key element in paranormal believers' thinking. However, the brain activity related to inhibitory control in paranormal believers is poorly understood. We examined EEG activities at resting state in alpha, beta, and gamma bands with inhibitory control in paranormal believers and skeptics. The present study shows that paranormal belief is related to the reduced power of the alpha, beta, and gamma frequency bands, and reduced inhibitory control. This study may contribute to understanding the differences between believers and skeptics in brain activity related to inhibitory control in paranormal believers. [ABSTRACT FROM AUTHOR]

In this study, we introduce a design of a feedback-type fluidic oscillator with elastic structures surrounding its feedback channel. By employing phase reduction theory, we extract the phase sensitivity function of the complex fluid–structure coupled system, which represents the system's oscillatory characteristics. We show that the frequency of the oscillating flow inside the fluidic oscillator can be modulated by inducing synchronization with the weak periodic forcing from the elastic structure vibration. This design approach adds controllability to the fluidic oscillator, where conventionally, the intrinsic oscillatory characteristics of such device were highly determined by its geometry. The synchronization-induced control also changes the physical characteristics of the oscillatory fluid flow, which can be beneficial for practical applications, such as promoting better fluid mixing without changing the overall geometry of the device. Furthermore, by analyzing the phase sensitivity function, we demonstrate how the use of phase reduction theory gives good estimation of the synchronization condition with minimal number of experiments, allowing for a more efficient control design process. Finally, we show how an optimal control signal can be designed to reach the fastest time to synchronization. [ABSTRACT FROM AUTHOR]

There seems to be no information on the incidence of injury and associated risk factors for academy football players in Ghana. We determine the risk factors associated with match and training injuries among male football players at an academy in Ghana. Preseason measurements of players' height, weight, and ankle dorsiflexion (DF) range of motion (ROM) were measured with a stadiometer (Seca 213), a digital weighing scale (Omron HN-289), and tape measure, respectively. The functional ankle instability (FAI) of players was measured using the Cumberland Ankle Instability Tool (CAIT), and dynamic postural control was measured with the Star Excursion Balance Test. Injury surveillance data for all injuries were collected by resident physiotherapists throughout one season. Selected factors associated with injury incidence were tested using Spearman's rank correlation at a 5% significance level. Age was negatively associated with overall injury incidence (r = − 0.589, p = 0.000), match (r = − 0.294, p = 0.008), and training incidence (r = − 0.314, p = 0.005). Previous injury of U18s was associated with training injuries (r = 0.436, p = 0.023). Body mass index (BMI) was negatively associated with overall injury incidence (r = − 0.513, p = 0.000), and training incidence (r = − 0.395, p = 0.000). CAIT scores were associated with overall injury incidence (n = 0.263, p = 0.019) and match incidence (r = 0.263, p = 0.029). The goalkeeper position was associated with match incidence (r = 0.241, p = 0.031) while the U16 attacker position was associated with training incidence. Exposure hours was negatively associated with overall injury incidence (r = − 0.599, p = 0.000). Age, BMI, previous injury, goalkeeper and attacker positions, ankle DF ROM, and self-reported FAI were associated with injury incidence among academy football players in Ghana. [ABSTRACT FROM AUTHOR]

Respiratory brain pulsations pertaining to intra-axial hydrodynamic solute transport are markedly altered in focal epilepsy. We used optical flow analysis of ultra-fast functional magnetic resonance imaging (fMRI) data to investigate the velocity characteristics of respiratory brain impulse propagation in patients with focal epilepsy treated with antiseizure medication (ASM) (medicated patients with focal epilepsy; ME, n = 23), drug-naïve patients with at least one seizure (DN, n = 19) and matched healthy control subjects (HC, n = 75). We detected in the two patient groups (ME and DN) several significant alterations in the respiratory brain pulsation propagation velocity, which showed a bidirectional change dominated by a reduction in speed. Furthermore, the respiratory impulses moved more in reversed or incoherent directions in both patient groups vs. the HC group. The speed reductions and directionality changes occurred in specific phases of the respiratory cycle. In conclusion, irrespective of medication status, both patient groups showed incoherent and slower respiratory brain impulses, which may contribute to epileptic brain pathology by hindering brain hydrodynamics. [ABSTRACT FROM AUTHOR]

To achieve high-efficiency combustion of heavy fuel oil (HFO), this study investigated the combustion characteristics of methanol/HFO droplets with methanol content from 10 to 30% using the suspension method under ambient temperature from 923 to 1023 K. The combustion of methanol/HFO droplets was summarized as a two-phase process consisting of six typical stages, emphasizing liquid phase. Especially, the fluctuation evaporation stage, induced by frequent and intense puffing, was identified as prominent character. Both the ignition delay and lifetime of HFO and methanol/HFO droplets decreased with increasing ambient temperatures. For the methanol/HFO droplet, the ignition delay and droplet lifetime increased with the increasing methanol content. Prominently, compared to HFO, HM10 had the most significant reduction in droplet lifetime and TINL under the same operating conditions, which indicated that the addition of 10% methanol accelerated the combustion process and reduced soot generation. Additionally, the thermos-dynamic characteristics of methanol/HFO droplets were investigated. Puffing was primarily attributed to superheating of methanol and pyrolysis of heavy components in HFO, which resulted in active and passive rupture of bubbles. Similarity and maximum deformation were employed to qualitatively distinguish between them. The obtained findings aimed to develop a promising alternative fuel to reduce emissions and preserve energy., (© 2024. The Author(s).)

Brain circuits display modular architecture at different scales of organization. Such neural assemblies are typically associated to functional specialization but the mechanisms leading to their emergence and consolidation still remain elusive. In this paper we investigate the role of inhibition in structuring new neural assemblies driven by the entrainment to various inputs. In particular, we focus on the role of partially synchronized dynamics for the creation and maintenance of structural modules in neural circuits by considering a network of excitatory and inhibitory θ -neurons with plastic Hebbian synapses. The learning process consists of an entrainment to temporally alternating stimuli that are applied to separate regions of the network. This entrainment leads to the emergence of modular structures. Contrary to common practice in artificial neural networks—where the acquired weights are typically frozen after the learning session—we allow for synaptic adaptation even after the learning phase. We find that the presence of inhibitory neurons in the network is crucial for the emergence and the post-learning consolidation of the modular structures. Indeed networks made of purely excitatory neurons or of neurons not respecting Dale's principle are unable to form or to maintain the modular architecture induced by the stimuli. We also demonstrate that the number of inhibitory neurons in the network is directly related to the maximal number of neural assemblies that can be consolidated, supporting the idea that inhibition has a direct impact on the memory capacity of the neural network. [ABSTRACT FROM AUTHOR]

A novel concept of utilizing the kinetic energy from ocean currents/wind by means of internal resonance is proposed to address the increasing global energy demand by generating clean and sustainable power. In this work, a non-linear rotative gravity pendulum is employed to autoparametrically excite the elastically mounted cylinder for a wide range of flow velocities. This concept is adopted to increase the oscillation amplitude of the cylinder due to vortex-induced vibration (VIV) in the de-synchronized region for energy harvesting. In this regard, a VIV-based energy harvesting device is proposed that consists of a cylinder with an attached pendulum, and energy is harvested with bottom-mounted piezoelectric transducers. The cylinder undergoes VIV when it is subjected to fluid flow and this excites the coupled fluid-multibody cylinder-pendulum system autoparametrically. In the de-synchronized region, when the vortex shedding frequency becomes two times the natural frequency of the pendulum, an internal resonance occurs. This helps in achieving a higher oscillation amplitude of the cylinder which does not happen otherwise. This study is focused on the two degree-of-freedom (2-DoF) cylinder-pendulum system where the cylinder is free to exhibit cross-flow vortex-induced vibrations subjected to the fluid. The objective of this work is to numerically investigate the effect of a non-linear rotative gravity pendulum (NRGP) on the VIV characteristics and piezoelectric efficiency of the system. The numerical model is based on the wake-oscillator model coupled with the piezoelectric constitutive equation. The influence of the frequency ratio, mass ratio, torsional damping ratio, and ratio of cylinder diameter to pendulum length of the NRGP device on response characteristics due to VIV is also investigated. A detailed comparative analysis in terms of electric tension and efficiency is performed numerically for flows with a wide range of reduced velocities for the cylinder with and without NRGP. A comprehensive study on the implications of internal resonance between the pendulum and a cylinder undergoing VIV on generated electric tension is also reported. [ABSTRACT FROM AUTHOR]

Proton activity is the master variable in many biogeochemical reactions. To control pH, laboratory studies involving redox-sensitive minerals like manganese (Mn) oxides frequently use organic buffers (typically Good's buffers); however, two Good's buffers, HEPES and MES, have been shown to reduce Mn(IV) to Mn(III). Because Mn(III) strongly controls mineral reactivity, avoiding experimental artefacts that increase Mn(III) content is critical to avoid confounding results. Here, we quantified the extent of Mn reduction upon reaction between Mn oxides and several Good's buffers (MES, pKa = 6.10; PIPES, pKa = 6.76; MOPS, pKa = 7.28; HEPES, pKa = 7.48) and TRIS (pKa = 8.1) buffer. For δ-MnO2, Mn reduction was rapid, with up to 35% solid-phase Mn(III) generated within 1 h of reaction with Good's buffers; aqueous Mn was minimal in all Good's buffers experiments except those where pH was one unit below the buffer pKa and the reaction proceeded for 24 h. Additionally, the extent of Mn reduction after 24 h increased in the order MES < MOPS < PIPES < HEPES << TRIS. Of the variables tested, the initial Mn(II,III) content had the greatest effect on susceptibility to reduction, such that Mn reduction scaled inversely with the initial average oxidation number (AMON) of the oxide. For biogenic Mn oxides, which consist of a mixture of Mn oxides, bacterial cells and extracelluar polymeric substances, the extent of Mn reduction was lower than predicted from experiments using abiotic analogs and may result from biotic re-oxidation of reduced Mn or a difference in the reducibility of abiotic versus biogenic oxides. The results from this study show that organic buffers, including morpholinic and piperazinic Good's buffers and TRIS, should be avoided for pH control in Mn oxide systems due to their ability to transfer electrons to Mn, which modifies the composition and reactivity of these redox-active minerals. [ABSTRACT FROM AUTHOR]

Information flow among nodes in a complex network describes the overall cause-effect relationships among the nodes and provides a better understanding of the contributions of these nodes individually or collectively towards the underlying network dynamics. Variations in network topologies result in varying information flows among nodes. We integrate theories from information science with control network theory into a framework that enables us to quantify and control the information flows among the nodes in a complex network. The framework explicates the relationships between the network topology and the functional patterns, such as the information transfers in biological networks, information rerouting in sensor nodes, and influence patterns in social networks. We show that by designing or re-configuring the network topology, we can optimize the information transfer function between two chosen nodes. As a proof of concept, we apply our proposed methods in the context of brain networks, where we reconfigure neural circuits to optimize excitation levels among the excitatory neurons. [ABSTRACT FROM AUTHOR]

Previous single-brain studies suggested interoception plays a role in interpersonal synchronization. The aim of the present study was to assess the electrophysiological intersubject coherence through electrophysiological (EEG) hyperscanning recording during simple dyadic synchronization tasks when the participants focused on their breath. To this aim, the neural activity of 15 dyads of participants was collected during the execution of a cognitive and motor synchronization task in two distinct IA conditions: focus and no focus on the breath condition. Individuals' EEG frequency bands were recorded through EEG hyperscanning and coherence analysis was performed. Results showed greater EEG coherence was observed for the alpha band in frontopolar brain regions (Fp1, Fp2) and also in central brain regions (C3, C4) within the dyads, during the focus on the breath condition for the motor compared to the cognitive synchronization task; during the same experimental condition, delta and theta band showed augmented inter-individual coherence in the frontal region (Fz) and central areas (C3, C4). To conclude, the current hyperscanning study highlights how the manipulation of the interoceptive focus (obtained through the focus on the breath) strengthens the manifestation of the EEG markers of interpersonal tuning during a motor synchronization task in specific brain areas. [ABSTRACT FROM AUTHOR]

Ising Machines (IMs) have the potential to outperform conventional Von-Neuman architectures in notoriously difficult optimization problems. Various IM implementations have been proposed based on quantum, optical, digital and analog CMOS, as well as emerging technologies. Networks of coupled electronic oscillators have recently been shown to exhibit characteristics required for implementing IMs. However, for this approach to successfully solve complex optimization problems, a highly reconfigurable implementation is needed. In this work, the possibility of implementing highly reconfigurable oscillator-based IMs is explored. An implementation based on quasiperiodically modulated coupling strength through a common medium is proposed and its potential is demonstrated through numerical simulations. Moreover, a proof-of-concept implementation based on CMOS coupled ring oscillators is proposed and its functionality is demonstrated. Simulation results show that our proposed architecture can consistently find the Max-Cut solution and demonstrate the potential to greatly simplify the physical implementation of highly reconfigurable oscillator-based IMs. [ABSTRACT FROM AUTHOR]

Rhythmic activity is ubiquitous in biological systems from the cellular to organism level. Reconstructing the instantaneous phase is the first step in analyzing the essential mechanism leading to a synchronization state from the observed signals. A popular method of phase reconstruction is based on the Hilbert transform, which can only reconstruct the interpretable phase from a limited class of signals, e.g., narrow band signals. To address this issue, we propose an extended Hilbert transform method that accurately reconstructs the phase from various oscillatory signals. The proposed method is developed by analyzing the reconstruction error of the Hilbert transform method with the aid of Bedrosian's theorem. We validate the proposed method using synthetic data and show its systematically improved performance compared with the conventional Hilbert transform method with respect to accurately reconstructing the phase. Finally, we demonstrate that the proposed method is potentially useful for detecting the phase shift in an observed signal. The proposed method is expected to facilitate the study of synchronization phenomena from experimental data. [ABSTRACT FROM AUTHOR]

Realizing compact and scalable Ising machines that are compatible with CMOS-process technology is crucial to the effectiveness and practicality of using such hardware platforms for accelerating computationally intractable problems. Besides the need for realizing compact Ising spins, the implementation of the coupling network, which describes the spin interaction, is also a potential bottleneck in the scalability of such platforms. Therefore, in this work, we propose an Ising machine platform that exploits the novel behavior of compact bi-stable CMOS-latches (cross-coupled inverters) as classical Ising spins interacting through highly scalable and CMOS-process compatible ferroelectric-HfO2-based Ferroelectric FETs (FeFETs) which act as coupling elements. We experimentally demonstrate the prototype building blocks of this system, and evaluate the scaling behavior of the system using simulations. Our work not only provides a pathway to realizing CMOS-compatible designs but also to overcoming their scaling challenges. [ABSTRACT FROM AUTHOR]

Deliberate control of the breath (breathwork) has recently received an unprecedented surge in public interest and breathing techniques have therapeutic potential to improve mental health. Our meta-analysis primarily aimed to evaluate the efficacy of breathwork through examining whether, and to what extent, breathwork interventions were associated with lower levels of self-reported/subjective stress compared to non-breathwork controls. We searched PsycInfo, PubMed, ProQuest, Scopus, Web of Science, ClinicalTrials.gov and ISRCTN up to February 2022, initially identifying 1325 results. The primary outcome self-reported/subjective stress included 12 randomised-controlled trials (k = 12) with a total of 785 adult participants. Most studies were deemed as being at moderate risk of bias. The random-effects analysis yielded a significant small-to-medium mean effect size, g = − 0.35 [95% CI − 0.55, − 0.14], z = 3.32, p = 0.0009, showing breathwork was associated with lower levels of stress than control conditions. Heterogeneity was intermediate and approaching significance, χ211 = 19, p = 0.06, I2 = 42%. Meta-analyses for secondary outcomes of self-reported/subjective anxiety (k = 20) and depressive symptoms (k = 18) showed similar significant effect sizes: g = − 0.32, p < 0.0001, and g = − 0.40, p < 0.0001, respectively. Heterogeneity was moderate and significant for both. Overall, results showed that breathwork may be effective for improving stress and mental health. However, we urge caution and advocate for nuanced research approaches with low risk-of-bias study designs to avoid a miscalibration between hype and evidence. [ABSTRACT FROM AUTHOR]

Synchronization is a major concept in nonlinear physics. In a large number of systems, it is observed at long times for a sinusoidal excitation. In this paper, we design a transiently non-sinusoidal driving to reach the synchronization regime more quickly. We exemplify an inverse engineering method to solve this issue on the classical Van der Pol oscillator. This approach cannot be directly transposed to the quantum case as the system is no longer point-like in phase space. We explain how to adapt our method by an iterative procedure to account for the finite-size quantum distribution in phase space. We show that the resulting driving yields a density matrix close to the synchronized one according to the trace distance. Our method provides an example of fast control of a nonlinear quantum system, and raises the question of the quantum speed limit concept in the presence of nonlinearities. [ABSTRACT FROM AUTHOR]