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Action potential generation underlies some of the most consequential dynamical systems on Earth, from brains to hearts. It is therefore interesting to develop synthetic cell-free systems, based on the same molecular mechanisms, which may allow for the exploration of parameter regions and phenomena not attainable, or not apparent, in the live cell. We previously constructed such a synthetic system, based on biological components, which fires action potentials. We call it “Artificial Axon”. The system is minimal in that it relies on a single ion channel species for its dynamics. Here we characterize the Artificial Axon as a dynamical system in time, using a simplified Hodgkin-Huxley model adapted to our experimental context. We construct a phase diagram in parameter space identifying regions corresponding to different temporal behavior, such as Action Potential (AP) trains, single shot APs, or damped oscillations. The main new result is the finding that our system with a single ion channel species, with inactivation, is dynamically equivalent to the system of two channel species without inactivation (the Morris-Lecar system), which exists in nature. We discuss the transitions and bifurcations occurring crossing phase boundaries in the phase diagram, and obtain criteria for the channels’ properties necessary to obtain the desired dynamical behavior. In the second part of the paper we present new experimental results obtained with a system of two AAs connected by excitatory and/or inhibitory electronic “synapses”. We discuss the feasibility of constructing an autonomous oscillator with this system.

We introduce a method for precise and accurate measurements of particle speeds in dense suspensions flowing at high rates and demonstrate the utility of the approach for revealing complex flow fluctuations during shearing in a setup that combines imaging with a confocal microscope and shearing with a rheometer. We scan the focal point in one dimension, aligned with direction of flow, producing absolute measurements of speed that are independent of suspension structure and particle shape. We compare this flow-direction line scanning approach with a complementary method we introduced previously, measuring speed using line scanning in the vorticity direction. By comparing results in various flow conditions, including shear-thinning and thickening regimes, we demonstrate the efficacy of our new approach. We find that both approaches exhibit qualitatively similar flow profiles, but a comparative analysis reveals a 15%–25% overestimation in speed measurement using vorticity line scanning, with discrepancies generated by anisotropic suspension microstructure under flow. Moreover, in the thickening regime where complex flow fields are present, both approaches capture local speed fluctuations. However, line scanning in the flow direction reveals and precisely captures stagnation and backflows, a capability not achievable with vorticity line scanning. The approach introduced here not only provides a refined technique for speed measurement in fast-flowing suspensions but also emphasizes the significance of accurate measurement techniques in advancing our understanding of flow behavior in dense suspensions, particularly in contexts where strong non-affine flows are prevalent.

The holographic duality (also known as AdS/CFT correspondence or gauge/gravity duality) postulates that strongly coupled quantum field theories can be described in a dual way in asymptotically anti-de Sitter space. One of the cornerstones of this duality is the description of thermal states as black holes with asymptotically anti-de Sitter boundary conditions. This idea has led to valuable insights into fields such as transport theory and relativistic hydrodynamics. In this context, the quasinormal modes of such black holes play a decisive role, and therefore their stability properties are of utmost interest for the holographic duality. We review recent results using the method of pseudospectra.

This paper features a survey of the periodic property of micro-electro-mechanical systems by the homotopy perturbation method, the variational iteration method, the variational theory, He’s frequency formulation, and Taylor series method. Fractal MEMS systems are also introduced, and future prospective is elucidated. The emphasis of this min-review article is put mainly on the developments in last decade, so the references, therefore, are not exhaustive.

As a high heat flux linear plasma device designed for studying divertor materials in future fusion reactors, HIT-PSI(Plasma Surface Interaction device at Harbin Institute of Technology) has been successfully constructed and has maintained stable operation since its completion. The characteristics of He plasma beams in HIT-PSI are investigated by emission spectroscopy and an infrared camera, with preliminary irradiation experiments conducted by bombarding tungsten with the beam. For relatively conservative discharge parameters, HIT-PSI achieved a steady-state heat flux capacity of ∼40 MW/m2 using infrared measurements, with the full width at half maximum (FWHM) of the heat flux beam reaching 4 mm. These characteristics make HIT-PSI an advanced platform for testing divertor materials and plasma-facing components, providing essential experimental supports for research and development of high-performance divertor materials.

In this study, we developed a new method for designing progressive addition-multifocus defocused freeform lenses. We used two independent meridians and achieved a smooth gradient transition of additional optical power from the center to the peripheral area of the lens, along with an asymmetric distribution of additional optical power on the nasal-temporal side of the lens. To improve the optical performance of the lenses, we developed three different designs based on the distribution of the additional optical power on the meridians. We conducted simulations and processing on the three different designs. The lenses designed using improved logistic regression and sine functions for meridian optical power distribution exhibited stable optical performance in the central focus area. They also met the design requirements for additional optical power. However, significant distortion was still observed in the peripheral region, which required further optimization. Lenses designed using piecewise linear functions for meridian optical power distribution exhibited relatively poor optical performance with significant optimization potential. Thus, combining the optical power distribution and surface-type factors for optimization is necessary. The proposed method enabled designing of defocus-free curved mirror lenses that satisfy the optical performance requirements. Thus, this method provides a new approach for the design of progressive addition lenses.

IntroductionThe development of science and technology has driven rapid changes in the social environment, especially the rise of the big data environment, which has greatly increased the speed at which people obtain information. However, in the process of big data processing, the allocation of information resources is often unreasonable, leading to a decrease in efficiency. Therefore, optimizing task scheduling algorithms has become an urgent problem to be solved.MethodsThe study optimized task scheduling algorithms using artificial intelligence (AI) methods. A task scheduling algorithm optimization model was designed using support vector machine (SVM) and K-nearest neighbor (KNN) combined with fuzzy comprehensive evaluation. In this process, the performance differences of different nodes were considered to improve the rationality of resource allocation.Results and DiscussionBy comparing the task processing time before and after optimization with the total cost, the results showed that the optimized model significantly reduced task processing time and total cost. The maximum reduction in task processing time is 2935 milliseconds. In addition, the analysis of query time before and after optimization shows that the query time of the optimized model has also been reduced. The experimental results demonstrate that the proposed optimization model is practical in handling task scheduling problems and provides an effective solution for resource management in big data environments. This research not only improves the efficiency of task processing, but also provides new ideas for optimizing future scheduling algorithms.

An environment interacting with a quantum system can enhance transport through the suppression of quantum effects responsible for localization. In this paper, we study the interplay between bulk dephasing and a linear potential in a boundary-driven tight-binding chain. A linear potential induces Wannier-Stark localization in the absence of noise, while dephasing induces diffusive transport in the absence of a tilt. We derive an approximate expression for the steady-state current as a function of both dephasing and tilt which closely matches the exact solution for a wide range of parameters. From it, we find that the maximum current occurs for a dephasing rate equal to the period of Bloch oscillations in the Wannier-Stark localized system. We also find that the current displays a maximum as a function of the system size, provided that the total potential tilt across the chain remains constant. Our results can be verified in current experimental platforms and represents a step forward in analytical studies of environment-assisted transport.

Pseudomodes of non-self-adjoint Schrödinger operators corresponding to large pseudoeigenvalues are constructed. The approach is non-semiclassical and extendable to other types of models including the damped wave equation and Dirac operators.

IntroductionThis paper investigates the impact of differential rotation on the bulk properties and onset of rotational instabilities in neutron stars at finite temperatures up to 50 MeV.MethodsUtilizing the relativistic Brueckner-Hartree-Fock (RBHF) formalism in full Dirac space, the study constructs equation of state (EOS) models for hot neutron star matter, including conditions relevant for high temperatures. These finite-temperature EOS models are applied to compute the bulk properties of differentially rotating neutron stars with varying structural deformations.ResultsThe findings demonstrate that the stability of these stars against bar-mode deformation, a key rotational instability, is only weakly dependent on temperature. Differential rotation significantly affects the maximum mass and radius of neutron stars, and the threshold for the onset of bar-mode instability shows minimal sensitivity to temperature changes within the examined range.DiscussionThese findings are crucial for interpreting observational data from neutron star mergers and other high-energy astrophysical events. The research underscores the necessity of incorporating differential rotation and finite temperature effects in neutron star models to predict their properties and stability accurately.

Incorporating medical text annotations compensates for the quality deficiencies of image data, effectively overcoming the limitations of medical image segmentation. Many existing approaches achieve high-quality segmentation results by integrating text into the image modality. However, these approaches require matched image-text pairs during inference to maintain their performance, and the absence of corresponding text annotations results in degraded model performance. Additionally, these methods often assume that the input text annotations are ideal, overlooking the impact of poor-quality text on model performance in practical scenarios. To address these issues, we propose a novel generative medical image segmentation model, Cap2Seg (Leveraging Caption Generation for Enhanced Segmentation of COVID-19 Medical Images). Cap2Seg not only segments lesion areas but also generates related medical text descriptions, guiding the segmentation process. This design enables the model to perform optimal segmentation without requiring text input during inference. To mitigate the impact of inaccurate text on model performance, we consider the consistency between generated textual features and visual features and introduce the Scale-aware Textual Attention Module (SATaM), which reduces the model’s dependency on irrelevant or misleading text information. Subsequently, we design a word-pixel fusion decoding mechanism that effectively integrates textual features into visual features, ensuring that the text information effectively supplements and enhances the image segmentation task. Extensive experiments on two public datasets, MosMedData+ and QaTa-COV19, demonstrate that our method outperforms the current state-of-the-art models under the same conditions. Additionally, ablation studies have been conducted to demonstrate the effectiveness of each proposed module. The code is available at https://github.com/AllenZzzzzzzz/Cap2Seg.

Over the past decade, spin-glass simulations have improved to the point that they now access time- and length-scales comparable to experiments at the mesoscale. A recent series of thin-film field-cooled/zero-field-cooled magnetization (FC/ZFC) experiments demonstrated activated spin dynamics, with a temperature-independent activation energy proportional to the logarithm of the film thickness and with coefficients in remarkable agreement with the simulation. These measurements require the application of small magnetic fields, which has been shown to affect the spin-glass energy landscape. Measurements of the 1/f noise in metallic spin-glasses have been previously shown to be a sensitive probe of the spin dynamics, and the measurements can be made without applying a magnetic field. In this mini-review, we review these techniques and discuss how transport measurements can fit into the current landscape of spin-glass measurements. We compare previous measurements to more recent measurements on similar films, made with ostensibly different cooling protocols, and compare both the previous and recent measurements to the magnetometry. The transport measurements—taken over a wider range of temperature than magnetometry—suggest that the maximum spin-glass energy barrier height is temperature-dependent, not fixed, possibly due to two-dimensional dynamics. We discuss this possibility, along with future measurements, which may be able to resolve this mystery.

With the rapid advancement of information technology and intelligent systems, autonomous driving has garnered significant attention and research in recent years. Key technologies, such as Simultaneous Localization and Mapping (SLAM), Perception and Localization, and Scene Segmentation, have proven to be essential in this field. These technologies not only evolve independently, each with its own research focus and application paths, but also complement and rely on one another in various complex autonomous driving scenarios. This paper provides a comprehensive review of the development and current state of these technologies, along with a forecast of their future trends.

The environmental noise have a negative influence on the quality of infrared thermal imaging of the rocket exhaust plume. In this study, the noise data of the unsteady rocket exhaust plume flow field was simulated using Gaussian white noise, and the infrared thermal image of the plume was numerically calculated using the narrow band method (SNB) and the line of sight (LOS) method. The denoising of infrared thermal imaging was achieved through the spectral proper orthogonal decomposition (SPOD) inversion method. Results indicate that Gaussian white noise leads to larger infrared thermal image residuals in the intrinsic core of the plume compared to the mixed regions. The infrared thermal image in the 2.7 μm band is greatly affected by the noise with an average error of 21.1%, and the average error in the 4.3 μm band is 17.6%. After SPOD denoising, the error of the plume infrared thermal image is reduced by more than 50%.

Thermal protection systems (TPSs) are important components of reusable spacecraft, and their assembly quality has a crucial impact on flight safety. Owing to the complex assembly process and variable states of spacecraft thermal protection systems, assembly parameters may vary under different assembly states. Therefore, to obtain assembly parameters accurately and efficiently under different assembly states, in this study, 3D point cloud data and fiber optic sensor data were fused to develop an assembly parameter update method for assembly process state changes. Firstly, based on the measured data of thermal protection components and load-bearing structure, the gap, flush and matching parameters solution model are proposed. Secondly, to address the deformation problem of the load-bearing structure caused by changes in assembly status, a fusion method based on laser scanning and sensor detection was devised to achieve deformation prediction of the assembly structure during the assembly process. Thirdly, based on the assembly parameter solution model and point cloud prediction model, a constraint-based assembly parameter optimisation model was established, and an improved quantum particle swarm optimisation (LQPSO) algorithm was employed to achieve assembly parameter updates oriented toward changes in assembly status. Finally, an experimental system for array-based thermal protection structure simulation was established to validate the proposed method. The results show that the proposed parameter update method can achieve ideal results for different assembly state simulation components.

Traffic congestion is a serious problem faced by many cities worldwide today. Congestion warning information is one of the important influencing factors of urban road congestion; To this end, based on the dynamics of infectious diseases, a congestion warning information dissemination model considering the attitudes of travelers and the network structure was constructed. The existence and stability of the equilibrium points of non congestion warning information and congestion warning information in the model were analyzed, and the optimal control strategy of the model was proposed. Numerical simulation was conducted to verify the results of theoretical analysis, simulate and analyze the impact of changes in various parameters in the model on the dissemination of congestion warning information, and perform sensitivity analysis on several parameters. The results indicate that travelers are more inclined towards “fast” modes of transportation and have a stronger willingness to share congestion warning information. The dissemination range of warning information is wider, which can play a positive role in reducing traffic congestion pressure.

Online tourism spot recommendations, as a key component of tourism services, aim to present travel options that align with users’ personal preferences. However, current recommendation systems often underperform due to the sparsity of tourism data and the wide variance in user preferences. To address this challenge, we propose a Semantic Analysis-Based Tourism Recommendation framework, abbreviated as SABTR (Semantic Analysis-Based Tourism Recommendation). The framework comprises two stages: Firstly, Latent Dirichlet Allocation (LDA) models are utilized to deeply mine data between users and attractions, constructing two core matrices: the user similarity matrix and the attraction similarity matrix. Secondly, based on the user similarity matrix, similarity calculation methods are applied to predict ratings for tourism spots that users have not yet evaluated. Simultaneously, within the attraction similarity matrix, probability distributions for each attraction across various thematic interests are calculated. When the system identifies a user’s interest in specific types of attractions, SABTR can select a series of related attractions from associated interest tags. Then, these candidate attractions are ranked according to both known and predicted user ratings, ultimately forming personalized attraction packages recommended to users. Extensive experiments have demonstrated that compared to existing tourism recommendation solutions, our method significantly improves the quality of attraction recommendations and enhances user satisfaction.

The impact of financial institution governance on systemic risk is crucial and controversial. In view of the important role of fund shareholding in the corporate governance of financial institutions, this paper takes financial institutions in China as the research object, constructs the association network of financial institutions based on fund shareholding, and empirically examines the impact of fund shareholding in financial institutions on systemic risk. The results show that fund shareholding significantly increases the systemic risk of financial institutions through the network. Convergence in funds’ choice of investment targets is an important mechanism for risk contagion among banks, insurance companies, securities companies, and across sectors. The governance externality created by fund holdings in financial institutions is thus corroborated. Further analysis shows the peer effect of funds’ investment behaviour is an important cause of share price convergence and governance convergence of held financial institutions, while majority shareholder monitoring and information transparency can effectively curb systemic risk contagion from governance externality. The study confirms the proposition of corporate governance externality in financial institutions, enriches the formation mechanism of systemic risk contagion from the perspective of corporate governance, and provides a theoretical guideline for effectively curbing the systemic risk caused by governance externality.

IntroductionThe SIR (Susceptible-Infected-Recovered) model is one of the simplest and most widely used frameworks for understanding epidemic outbreaks.MethodsA second-order dynamical system for the R variable is formulated using an infinite exponential series expansion, and a recursion relation is established between the series coefficients. A numerical approximation scheme for the R variable is also developed.ResultsThe proposed numerical method is compared to a double exponential (DE) nonlinear approximate analytic solution, which reveals two coupled timescales: a relaxation timescale, determined by the ratio of the model’s time constants, and an excitation timescale, dictated by the population size. The DE solution is applied to estimate model parameters for a well-known epidemiological dataset—the boarding school flu outbreak.DiscussionFrom a theoretical standpoint, the primary contribution of this work is the derivation of an infinite exponential, Dirichlet, series for the model variables. Truncating the series yields a finite approximation, known as a Prony series, which can be interpreted as a sequence of coupled exponential relaxation processes, each with a distinct timescale. This apparent complexity can be approximated well by the DE solution, which appears to be of main practical interest.

IntroductionAs 5G networks become widespread and their application scenarios expand, massive amounts of traffic data are continuously generated. Properly analyzing this data is crucial for enhancing 5G services.MethodsThis paper uses the visibility graph method to convert 5G traffic data into a visibility graph network, conducting a feature analysis of the 5G traffic data. Using the AfreecaTV dataset as the research object, this paper constructs visibility networks at different scales and observes the evolution of degree distribution with varying data volumes. The paper employs the Hurst index to evaluate the 5G traffic network and uses community detection to study the networks converted from 5G traffic data of different applications.ResultsExperimental results reveal significant differences in node degree distribution and topological structures of 5G traffic data across different application scenarios, such as star structures and multiple subnetwork structures. It is found that the node degree distribution of 5G traffic networks exhibits heterogeneity, reflecting the uneven growth of node degrees during network expansion. The Hurst index analysis discovers that the 5G traffic network retains the long-term dependence and trends of the original data. Through community detection, it is observed that networks converted from 5G traffic data of different applications exhibit diverse community structures, such as high centrality nodes, star-like community structures, modularity, and multilayer characteristics.DiscussionThese findings indicate that 5G traffic networks in different application scenarios exhibit complex and diverse characteristics. The heterogeneity of node degree distribution and differences in topological structures reflect the imbalance in node connection methods during network expansion. The results of the Hurst index show that the 5G traffic network inherits the long-term dependence of the original data, providing a basis for analyzing the dynamic characteristics of the network. The diverse community structures reveal the inherent modularity and hierarchy of the network, which helps to understand the performance and optimization directions of 5G networks in different applications.

Quantum cascade lasers (QCLs) in the mid-infrared (MIR) hold significant potential for widespread applications in both military and civilian contexts. However, the utility of present single-chip QCLs is hampered by issues such as low output power and subpar beam quality. This study addresses these limitations by employing spectral beam combining (SBC) based on a diffraction grating, with an aim to enhance both power and beam quality of MIR QCLs. Coaxial power synthesis of three single-chip QCLs (around 4.75 μm) is achieved experimentally, importantly, the beam quality did not decrease after combining, essentially maintaining the same quality as before combining. It is proposed that there are four main factors affecting the combining efficiency, namely operating optical power (or driving current) of the chips, individual differences in QCL chips, diffraction grating, and reflectivity of feedback mirror, and their effects on the combining efficiency are discussed separately. This study confirms that SBC is an effective way to obtain high-power and high-beam quality MIR QCL sources, and lays a research foundation for more beam spectral combining.

We briefly review the analytical continuation method for determining quasinormal modes (QNMs) and the associated frequencies in open systems. We explore two exactly solvable cases based on the Pöschl–Teller potential to show that the analytical continuation method cannot determine the full set of QNMs and frequencies of a given problem starting from the associated bound state problem in quantum mechanics. The root of the problem is that many QNMs are the analytically continued counterparts of solutions that do not belong to the domain where the associated Schrödinger operator is self-adjoint, challenging the application of the method for determining full sets of QNMs. We illustrate these problems through the physically relevant case of BTZ black holes, where the natural domain of the problem is the negative real line.

We report on the design and simulation of strip-loaded nanophotonic interfaces aimed at improving resonant coupling and photon routing efficiency. In our design, the guided mode is confined within a plane by a high-index thin film and is loosely confined laterally by a lower index strip. Using a hydrogen silsesquioxane (HSQ) strip, titanium dioxide core, and silicon dioxide substrate, we optimise the waveguide dimensions for maximum lateral confinement of light. Specifically, we propose a polymer-based Bragg grating cavity and ring resonator that achieve near-optimal mode volumes and high Q-factors. These may be further developed to achieve the even higher Q-factors demanded by quantum technologies. Our calculations suggest that a quantum dot embedded in a cavity with a mode volume of Veff∼7.0λ/n3 and a Q-factor of 7,000 can produce photons with 97% indistinguishability at 4K. Additionally, we investigate directional couplers for efficient photon routing, comparing photonic and plasmonic material structures. While pure photonic structures demonstrate lower loss and improved quality factors, they face practical limitations in terms of bending radius. Conversely, plasmonic structures offer shorter bending radii but higher propagation losses. This research lays the groundwork for future nanophotonic designs, aiming to enhance photon generation and routing capabilities for quantum optical applications.

Nitrogen fixation is crucial for plant growth and global agriculture, especially with the projected population growth requiring a significant increase in food production. Traditional nitrogen fixation relies on the Haber-Bosch (H-B) process, which is energy-intensive and environmentally harmful due to greenhouse gas emissions. Emerging technologies, such as cold plasma, offer promising alternatives with lower energy consumption. Cold plasma facilitates reactive nitrogen species generation under ambient conditions, potentially improving the production efficiency of nitrogen oxides (NOx). However, optimizing cold plasma nitrogen fixation requires a synergy between experimental and theoretical approaches. Accurate input data are essential for refining theoretical models, which can then guide the design of more efficient processes. This integrated approach can leverage renewable energy, operate on smaller scales, and minimize environmental impacts, making cold plasma a sustainable solution for future nitrogen fixation needs.

Accurate solar power forecasting is pivotal for the global transition towards sustainable energy systems. This study conducts a meticulous comparison between Quantum Long Short-Term Memory (QLSTM) and classical Long Short-Term Memory (LSTM) models for solar power production forecasting. The primary objective is to evaluate the potential advantages of QLSTMs, leveraging their exponential representational capabilities, in capturing the intricate spatiotemporal patterns inherent in renewable energy data. Through controlled experiments on real-world photovoltaic datasets, our findings reveal promising improvements offered by QLSTMs, including accelerated training convergence and substantially reduced test loss within the initial epoch compared to classical LSTMs. These empirical results demonstrate QLSTM’s potential to swiftly assimilate complex time series relationships, enabled by quantum phenomena like superposition. However, realizing QLSTM’s full capabilities necessitates further research into model validation across diverse conditions, systematic hyperparameter optimization, hardware noise resilience, and applications to correlated renewable forecasting problems. With continued progress, quantum machine learning can offer a paradigm shift in renewable energy time series prediction, potentially ushering in an era of unprecedented accuracy and reliability in solar power forecasting worldwide. This pioneering work provides initial evidence substantiating quantum advantages over classical LSTM models while acknowledging present limitations. Through rigorous benchmarking grounded in real-world data, our study illustrates a promising trajectory for quantum learning in renewable forecasting.

The notion of Fréchet mean (also known as “barycenter”) network is the workhorse of most machine learning algorithms that require the estimation of a “location” parameter to analyse network-valued data. In this context, it is critical that the network barycenter inherits the topological structure of the networks in the training dataset. The metric–which measures the proximity between networks–controls the structural properties of the barycenter. This work is significant because it provides for the first time analytical estimates of the sample Fréchet mean for the stochastic blockmodel, which is at the cutting edge of rigorous probabilistic analysis of random networks. We show that the mean network computed with the Hamming distance is unable to capture the topology of the networks in the training sample, whereas the mean network computed using the effective resistance distance recovers the correct partitions and associated edge density. From a practical standpoint, our work informs the choice of metrics in the context where the sample Fréchet mean network is used to characterize the topology of networks for network-valued machine learning.

This paper presents a technique for predicting noise generated by airfoil structures that combines deep learning techniques with traditional numerical methods. In traditional numerical methods, accurately predicting the noise of airfoil structures requires significant computational resources, making it challenging to perform low-noise optimization design for these structures. To expedite the prediction process, this study introduces Conditional Generative Adversarial Networks (CGAN). By replacing the generator and discriminator of CGAN with traditional regression neural network models, the suitability of CGAN for regression prediction is ensured. In this study, the data computation was accelerated by expanding the kernel function in the traditional boundary element method using a Taylor series. Based on the resulting data, an alternative predictive model for wing structure noise was developed by integrating Conditional Generative Adversarial Networks (CGAN). Finally, the effectiveness and feasibility of the proposed method are demonstrated through three case studies.

In 1995, the ATLAS Experiment at the Large Hadron Collider embarked on an ambitious journey, driven by a passion to ignite curiosity and inspire future scientists. Our quest wasn’t just about uncovering the universe’s mysteries; it was a cosmic odyssey to share the thrill of scientific exploration with all. Our exploratory collection included engaging classroom activities, a dynamic website, captivating videos, a vibrant YouTube channel, global social media connections, and a captivating planetarium show, all intertwined with the exciting search for the elusive Higgs Boson. This scientific program celebrates the enduring curiosity of humanity and the passion that fuels discovery.

The threat of spoofing interference has posed a severe challenge to the security application of Global Navigation Satellite System (GNSS). It is particularly urgent and critical to carry out in-depth defense research on spoofing interference. When combined with the inertial navigation system (INS), the GNSS/INS integrated navigation system offers distinct advantages in the field of anti-spoofing technology research, which has garnered significant attention in recent years. To summarize the current research achievements of GNSS/INS integrated navigation anti-spoofing technology, it is necessary to provide an overview of the three core technical aspects of spoofing attack principles and implementation strategies, spoofing detection, and spoofing mitigation. First, the principles and implementation strategies of spoofing interference attacks are introduced, and different classifications of spoofing interference attacks are given. Then, the performance characteristics and technical points of different spoofing detection and spoofing mitigation methods are compared and analyzed, and the shortcomings and challenges in the current development of GNSS/INS anti-spoofing technology are pointed out. Finally, based on the summary and shortcomings of the existing technology, a prospect for the future development of GNSS/INS integrated navigation anti-spoofing technology is discussed.

Understanding how neural networks process information is a fundamental challenge in neuroscience and artificial intelligence. A pivotal question in this context is how external stimuli, particularly noise, influence the dynamics and information flow within these networks. Traditionally, noise is perceived as a hindrance to information processing, introducing randomness and diminishing the fidelity of neural signals. However, distinguishing noise from structured input uncovers a paradoxical insight: under specific conditions, noise can actually enhance information processing. This intriguing possibility prompts a deeper investigation into the nuanced role of noise within neural networks. In specific motifs of three recurrently connected neurons with probabilistic response, the spontaneous information flux, defined as the mutual information between subsequent states, has been shown to increase by adding ongoing white noise of some optimal strength to each of the neurons. However, the precise conditions for and mechanisms of this phenomenon called ‘recurrence resonance’ (RR) remain largely unexplored. Using Boltzmann machines of different sizes and with various types of weight matrices, we show that RR can generally occur when a system has multiple dynamical attractors, but is trapped in one or a few of them. In probabilistic networks, the phenomenon is bound to a suitable observation time scale, as the system could autonomously access its entire attractor landscape even without the help of external noise, given enough time. Yet, even in large systems, where time scales for observing RR in the full network become too long, the resonance can still be detected in small subsets of neurons. Finally, we show that short noise pulses can be used to transfer recurrent neural networks, both probabilistic and deterministic, between their dynamical attractors. Our results are relevant to the fields of reservoir computing and neuroscience, where controlled noise may turn out a key factor for efficient information processing leading to more robust and adaptable systems.

Fossil fuels remain indispensable energy resources despite their non-renewable nature. Understanding the patterns of global fossil fuel consumption is essential for energy security and policy-making. This study employs complex network theory and fractal time series analysis to explore the underlying dynamics and patterns of fossil fuel consumption globally, with a focus on coal, oil, and gas consumption.The study applies the Hurst index to raw fossil fuel consumption data to identify fractal characteristics. Additionally, the visibility graph method is used to convert time series data into complex networks, allowing further analysis of consumption patterns. The study examines fossil fuel consumption in 38 countries to assess global trends and differences. The analysis reveals that global fossil fuel consumption follows a fractal time series pattern, with Hurst index values exceeding 0.9, indicating long-term memory characteristics. The application of the visibility graph method demonstrates variations in the Hurst index of degree distribution, enabling the differentiation of consumption patterns across regions. The method also uncovers distinct features of coal, oil, and gas consumption when viewed from a network perspective. The findings suggest that fossil fuel consumption has predictable long-term patterns, which are crucial for assessing future energy demands. The study highlights the importance of legislative measures to safeguard fossil fuel resources, especially for countries like China, where energy security and international competitiveness are paramount. Understanding these consumption patterns could guide future energy policies aimed at managing non-renewable resources more effectively.

The Pulsed Energetic Electrons for Research (PEER) beamline at the ANSTO Australian Synchrotron comprises a 100 MeV linac injector that is currently being developed for ultra-high dose-rate, very high-energy electron radiotherapy research. Previously, dosimetry studies discovered a lack of reliable charge measurement to the in-air end station, though no change in response was recorded in fast current transformer measurements, the only available diagnostic device for measuring charge. This work describes the process of simulating and then commissioning a purpose-built Faraday cup to ascertain absolute in-air charge measurements at PEER. By combining simulation data with experimental results, the PEER Faraday cup is shown to possess a primary electron capture efficiency of (99.22 ± 0.10)%, with a net capture efficiency due to secondary electron emission of (97.87 ± 0.24)%. These results show the PEER Faraday cup performs as intended and, when scaled against simulations, will be suitable for measuring absolute charge at PEER.

Composite structure is widely used in various technological fields because of its superior material properties. Composite structure detection technology has been exploring efficient and fast damage detection technology. In this paper, image-based NDT technology is proposed to detect composite damage using deep learning. A data set was established through literature, which contained images of damaged and non-damaged composite material structures. Then, five convolutional neural network models Alexnet, VGG16, ResNet-34, ResNet-50, and GoogleNet were used to automatically classify the damage. Finally, the performance of five pre-trained network architectures is evaluated, and the results show that RESNET-50 technology can successfully detect damage in a reasonable computation time with the highest accuracy and low complexity using relatively small image datasets.

With advancements in freeform surface design and manufacturing, applying freeform surfaces to space cameras is an effective method to further enhance imaging quality. However, existing design methods for freeform space cameras rarely consider imaging quality, system size, and manufacturing constraints simultaneously during design. This study proposes an adaptive design method for long-focal-length freeform off-axis reflective space cameras with lightweight and primary/three mirror integration, which balances system size and ease of manufacturing while ensuring good imaging quality. First, a method for adaptive configuration of the structural parameter search spaces is proposed, so that the search spaces for structural parameters are dynamically generated based on different design requirements of long-focal-length off-axis space cameras with various structures. Then, a multiple-parameter objective function is constructed, and the structural parameters for the off-axis space camera are determined through a search process to balance imaging quality, lightweight requirements and manufacturing demands. Finally, the improved Wassermann–Wolf (W-W) method is employed to optimize the mirrors into freeform surfaces, further enhancing the imaging quality of the designed space camera. Experimental results demonstrate that the proposed method can adaptively generate reasonable structural parameter search spaces while maintaining high imaging quality, facilitating the acquisition of a lightweight and easily manufacturable freeform off-axis reflective space camera. This method exhibits strong dynamic adaptability and low reliance on prior experience, providing a new insight for the design of space cameras.

In this work, we propose a structured-illumination lensless digital holographic microscopy (SI-LDHM). SI-LDHM illuminates a sample with 24 structured illuminations (8 orientations × 3 phase shifts) and records the defocused interferogram formed by two copies of object waves along the ±1st diffraction orders of each SI. The reconstructed object waves under different illumination orientations are respectively propagated to the sample plane along the +1st diffraction order and then averaged, thus yielding a clean image without the artifact of twin images. Experimental results demonstrated that thanks to the multi-oriented SI strategy, the twin image in SI-LDHM is sevenfold reduced compared to conventional DHM, while the spatial resolution is 1.15 times higher.

3D point clouds collected by low-channel light detection and ranging (LiDAR) are relatively sparse compared to high-channel LiDAR, which is considered costly. To address this, an outdoor large-scene point cloud reconstruction (LSPCR) technique based on transformer is proposed in this study. The LSPCR approach first projects the original sparse 3D point cloud onto a 2D range image; then, it enhances the resolution in the vertical direction of the 2D range image before converting the high-resolution range image back to a 3D point cloud as the final reconstructed point cloud data. Experiments were performed on the real-world KITTI dataset, and the results show that LSPCR achieves an average accuracy improvement of over 60% compared to non-deep-learning algorithms; it also achieves better performance compared to the latest deep-learning algorithms. Therefore, LSPCR is an effective solution for sparse point cloud reconstruction and addresses the challenges associated with high-resolution LiDAR point clouds.

Recent advancements in quantum computing and quantum-inspired algorithms have sparked renewed interest in binary optimization. These hardware and software innovations promise to revolutionize solution times for complex problems. In this work, we propose a novel method for solving linear systems. Our approach leverages binary optimization, making it particularly well-suited for problems with large condition numbers. We transform the linear system into a binary optimization problem, drawing inspiration from the geometry of the original problem and resembling the conjugate gradient method. This approach employs conjugate directions that significantly accelerate the algorithm’s convergence rate. Furthermore, we demonstrate that by leveraging partial knowledge of the problem’s intrinsic geometry, we can decompose the original problem into smaller, independent sub-problems. These sub-problems can be efficiently tackled using either quantum or classical solvers. Although determining the problem’s geometry introduces some additional computational cost, this investment is outweighed by the substantial performance gains compared to existing methods.

Electron firehose instabilities can be excited at dipolarization fronts and in the magnetic reconnection outflow in the terrestrial magnetotail, but their occurrence rate in the plasma sheet is unclear. Here, we investigate the characteristics of electron firehose unstable conditions in the magnetotail plasma sheet based on observations of the Magnetospheric Multiscale mission. We find an Alfvénic magnetic field fluctuation accompanied by a strong field-aligned current during a flapping motion. This fluctuation occurs where the local plasma is electron firehose unstable, indicating that the electron firehose instability in the plasma sheet can occur in the region besides dipolarization fronts and magnetic reconnection outflow. We statistically find that the local plasma near the neutral sheet has a small probability with the maximum value 0 (electron firehose unstable condition) is ∼1.36% (1.32%) at BXY/BL ≈ 0.05 (0.15) during fast (non-fast) flows. During fast flows, the plasma near the neutral sheet tends to have a higher probability of Tef > 0 when the local VT is larger. During non-fast flows, the plasma near the neutral sheet tends to have a higher probability of Tef > 0 when Te is larger. The probability of Tef > 0 shows a dawn-dusk asymmetry during fast flows and non-fast flows. In addition, the probability of Tef > 0 during fast flows tends to be larger when the ambient BZ is weak, which shows opposite characteristics during non-fast flows. These findings help to assess the importance of the role of electron firehose instabilities in the magnetotail plasma sheet.

Atomic force microscopy (AFM) is a part of the scanning probe microscopy family. It provides a platform for high-resolution topographical imaging, surface analysis as well as nanomechanical property mapping for stiff and soft samples (live cells, proteins, and other biomolecules). AFM is also crucial for measuring single-molecule interaction forces and important parameters of binding dynamics for receptor-ligand interactions or protein-protein interactions on live cells. However, performing AFM measurements and the associated data analytics are tedious, laborious experimental procedures requiring specific skill sets and continuous user supervision. Significant progress has been made recently in artificial intelligence (AI) and deep learning (DL), extending into microscopy. In this review, we summarize how researchers have implemented machine learning approaches so far to improve the performance of atomic force microscopy (AFM), make AFM data analytics faster, and make data measurement procedures high-throughput. We also shed some light on the different application areas of AFM that have significantly benefited from applications of machine learning frameworks and discuss the scope and future possibilities of these crucial approaches.

With the continuous progress of information acquisition technology, the volume of LiDAR point cloud data is also expanding rapidly, which greatly hinders the subsequent point cloud processing and engineering applications. In this study, we propose a point cloud simplification strategy utilizing probabilistic membership to address this challenge. The methodology initially develops a feature extraction scheme based on curvature to identify the set of feature points. Subsequently, a combination of k-means clustering and Possibilistic C-Means is employed to partition the point cloud into subsets, and to simultaneously acquire the probabilistic membership information of the point cloud. This information is then utilized to establish a rational and efficient simplification scheme. Finally, the simplification results of the feature point set and the remaining point set are merged to obtain the ultimate simplification outcome. This simplification method not only effectively preserves the features of the point cloud while maintaining uniformity in the simplified results but also offers flexibility in balancing feature retention and the degree of simplification. Through comprehensive comparative analysis across multiple point cloud models and benchmarking against various simplification methods, the proposed approach demonstrates superior performance. Finally, the proposed algorithm was critically discussed in light of the experimental results.

We present a collection of simulations of the Edwards–Anderson lattice spin glass at T=0 to elucidate the nature of low-energy excitations over a range of dimensions that reach from physically realizable systems to the mean-field limit. Using heuristic methods, we sample the ground states of instances to determine their energies while eliciting excitations through manipulating boundary conditions. We exploit the universality of the phase diagram of bond-diluted lattices to make such a study in higher dimensions computationally feasible. As a result, we obtain a variety of accurate exponents for domain wall stiffness and finite-size corrections, which allow us to examine their dimensional behavior and their connection with predictions from mean-field theory. We also provide an experimentally testable prediction for the thermal-to-percolative crossover exponent in dilute lattice Ising spin glasses.