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Unstable emotions, particularly anger, have been identified as significant contributors to traffic accidents. To address this issue, driver emotion recognition emerges as a promising solution within the realm of cyber-physical-social systems (CPSS). In this paper, we introduce SVGG, an emotion recognition model that leverages the attention mechanism. We validate our approach through comprehensive experiments on two distinct datasets, assessing the model's performance using a range of evaluation metrics. The results suggest that the proposed model exhibits improved performance across both datasets. [ABSTRACT FROM AUTHOR]

This paper offers a concise overview regarding ancient Chinese mathematics, centering on the Ying Buzu Shu, He Chengtian inequality, and the frequency formulation stemming from them. Moreover, it delves into the Max-min approach and Chunhui He's iterative algorithm. What's more, the spotlight is cast on ancient Chinese mathematics, which bears certain similarities to the ancient Babylonian mathematical tradition. Subsequently, the old Babylonian algorithm for computing square roots is adapted to tackle the hurdle of nonlinear differential equations. To showcase the potential of this approach, a set of Micro-Electro-Mechanical systems (MEMS) problems are utilized to exemplify the effectiveness of the modified old Babylonian algorithm in attaining high-precision analytical solutions, accompanied by an exploration of its prospective applications. [ABSTRACT FROM AUTHOR]

To address the trade-off between segmentation performance and model lightweighting in computer-aided skin lesion segmentation, this paper proposes a lightweight network architecture, Multi-Conv Attention Network (MCAN). The network consists of two key modules: ISDConv (Inception-Split Depth Convolution) and AEAM (Adaptive Enhanced Attention Module). ISDConv reduces computational complexity by decomposing large kernel depthwise convolutions into smaller kernel convolutions and unit mappings. The AEAM module leverages dimensional decoupling, lightweight multi-semantic guidance, and semantic discrepancy alleviation to facilitate the synergy between channel attention and spatial attention, further exploiting redundancy in the spatial and channel feature maps. With these improvements, the proposed method achieves a balance between segmentation performance and computational efficiency. Experimental results demonstrate that MCAN achieves state-of-the-art performance on mainstream skin lesion segmentation datasets, validating its effectiveness. [ABSTRACT FROM AUTHOR]

The AMoRE-II experiment will search for the 0 νββ decay of 100Mo nuclei using molybdate crystal scintillators, operating at milli-Kelvin (mK) temperatures, with a total of 80 kg of 100Mo. The background goal for the experiment is 10–4 counts/keV/kg/year in the region of interest around the 0 νββ decay Q-value of 3,034 keV. To achieve this level, the rate of background signals arising from emissions produced by decays of radioactive impurities in the detector and shielding materials must be strictly controlled. To do this, concentrations of such impurities are measured and are controlled through materials selection and purification. In this paper, we describe the design and the construction materials used to build the AMoRE-II detector and shielding system, including active and passive shielding, the cryostat, and the detector holders and instrumentation, and we report on measurements of radioactive impurities within candidate and selected materials. [ABSTRACT FROM AUTHOR]

Satellite navigation interference monitoring is an important means to effectively evaluate interference and ensure the normal operation of global navigation satellite system (GNSS). Once interference is detected, this monitoring can identify the type of it, perform direction-finding and localization, evaluate its impact on GNSS, and guide the implementation of effective countermeasures. With the continuous progress of interference technology, the power required to cause the same jamming effect to the navigation system is getting smaller and smaller. Traditional radio monitoring system has been unable to meet the needs of the current satellite navigation monitoring in terms of sensitivity and accuracy. It is of great significance to develop and improve the dedicated satellite navigation monitoring system. This paper introduces the basic concept of satellite navigation interference monitoring and the composition of the system, analyzes the key technologies and finally gives an outlook on the development trends in this field. [ABSTRACT FROM AUTHOR]

This paper presents the design of a high-efficiency spot size converter (SSC) for photonic crystal fiber (PCF) to silicon-based waveguides, utilizing composite optical waveguide structures. The SSC is composed of a series of waveguide cores, with one core progressively widening to enable all cores to collectively serve as a composite input port, while the broader core functions as the output port. Simulation results reveal that the proposed design efficiently facilitates mode and energy transfer between the waveguide and the PCF, achieving a coupling efficiency of up to 93.99% over a length of 66 μm. Additionally, the SSC exhibits polarization insensitivity, with the high circularity of the output beam enhancing vertical alignment tolerance, and maintaining excellent performance over a wide wavelength range. In sum, this SSC demonstrates excellent coupling efficiency and holds significant promise for applications in optical communication systems and optical integrated circuit systems. [ABSTRACT FROM AUTHOR]

This paper proposes a statistical physics-based data assimilation model for the mobility of water-bound hydrogen nuclear spins in the brain in the context of diffusion weighted magnetic resonance imaging (DWI or DW-MRI). Point of departure is a statistical hopping model that emulates molecular motion in the presence of static and stationary microscale obstacles, statistically reflected in the apparent inhomogeneous anisotropic DWI signal profiles. Subsequently, we propose a Riemann–Finsler geometric interpretation in terms of a metric transform that simulates this molecular process as free diffusion on a vacuous manifold with all diffusion obstacles absorbed in its geometry. The geometrization procedure supports the reconstruction of neural tracts (geodesic tractography) and their quantitative characterization (tractometry). The Riemann-DTI model for geodesic tractography based on diffusion tensor imaging (DTI) arises as a limiting case. The genuine Finslerian case is a geometric representation of high-angular resolution DTI , i.e., a generalized rank-two DTI framework without the quadratic restriction implied by a simplifying Gaussianity assumption on local diffusion or a second-order harmonic approximation of local orientation distributions. [ABSTRACT FROM AUTHOR]

Pile supported geogrid-reinforced embankment are widely used to treat soft soil. This paper proposes a novel method for reinforcing foundations using non-foamed polyurethane-bonded gravel (NPBG) porous piles. Through triaxial compression tests, the mechanical properties and strength parameters, such as strength and stiffness, of the polyurethane-bonded gravel material are determined. Finite element numerical simulations are then employed to investigate the bearing characteristics of NPBG porous pile under embankment loads. The experimental results indicate that, under the same confining pressure, the strength and stiffness of the NPBG material are significantly higher than those of plain gravel material, while still retaining large pore characteristics. Increasing the polyurethane content enhances the strength and stiffness of the porous material, but the improvement diminishes as the confining pressure increases. A systematic comparative analysis was conducted on the bearing characteristics of NPBG porous pile versus concrete pile, cement-mixed pile, and gravel pile under embankment in terms of load sharing ratio and settlement deformation. The permeability coefficient and modulus of the pile significantly influence the dissipation rate of excess pore water pressure in the foundation soil. The NPBGP pile exhibited the fastest consolidation speed, effectively controlling total settlement and reducing the proportion of post-construction settlement of the embankment. Compared to concrete pile and cement-mixed pile, the NPBG porous pile has a lower uniform settlement surface height in embankment, making it suitable for low-height embankment accelerated construction projects. [ABSTRACT FROM AUTHOR]

The segmentation of gastrointestinal (GI) organs, including the stomach, small intestine, and large intestine, is crucial for radio oncologists to plan effective cancer therapy. This study presents an innovative semantic segmentation approach that integrates the Swin Transformer Block with the U-Net model to delineate healthy GI organs accurately using MRI data. The paper presents a novel approach that merges the Swin Transformer and U-Net models to leverage global context learning capabilities and fine-grained spatial resolution. Incorporating this integration greatly enhances the model's capacity to achieve precise and comprehensive semantic segmentation, specifically in accurately outlining the gastrointestinal tract in MRI data. It utilizes the Swin Transformer, incorporating a shift-based windowing technique to gather contextual information efficiently while ensuring scalability. This novel architecture effectively balances local and global contexts, improving performance across various computer vision tasks, especially in medical imaging for segmenting the gastrointestinal tract. The model was trained and tested on the UW Madison GI Tract dataset, which comprises 38,496 MRI images from actual cancer cases. By leveraging the self-attention mechanisms of the Swin Transformer to capture global context and long-term dependencies, this approach combines the strengths of both models. The proposed architecture achieved a loss of 0.0949, a dice coefficient of 0.9190, and an Intersection over Union (IoU) score of 0.8454, demonstrating its effectiveness in providing high accuracy and robust performance. This technology holds significant potential for integration into clinical processes, enhancing the precision of radiation therapy for GI cancer patients. [ABSTRACT FROM AUTHOR]

This paper presents the Scene Adaptive Acoustic Encoder (SAAE) method, which is tailored to diverse acoustic environments for adaptive design. Hand-crafted acoustic encoders often struggle to adapt to varying acoustic conditions, resulting in performance degradation in end-to-end speech recognition tasks. To address this challenge, the proposed SAAE method learns the differences in acoustic features across different environments and accordingly designs suitable acoustic encoders. By incorporating neural architecture search technology, the effectiveness of the encoder design is enhanced, leading to improved speech recognition performance. Experimental evaluations on three commonly used Mandarin and English datasets (Aishell-1, HKUST, and SWBD) demonstrate the effectiveness of the proposed method. The SAAE method achieves an average error rate reduction of more than 5% compared with existing acoustic encoders, highlighting its capability to deeply analyze speech features in specific scenarios and design high-performance acoustic encoders in a targeted manner. [ABSTRACT FROM AUTHOR]

The Compact Linear Accelerator for Research and Applications (CLARA) is a test facility at the STFC Daresbury Laboratory, which is capable of delivering ultra-bright electron bunches of up to 250 MeV beam energy. A new beam line has been designed to maximise the exploitation of the CLARA facility in a variety of scientific fields, including novel acceleration and new modalities of radiotherapy. In this paper, we present the specification and design of this beam line to explore the possibilities of pursuing R&D in very high-energy electron (VHEE) radiotherapy and VHEE with FLASH radiotherapy. We describe the beam line design, including the flexibility of the beam optics to focus the beam in a range of locations and present results from preliminary start-to-end simulations using Monte Carlo tracking codes. We highlight advantages of this beam line, including rapid access to the shielded experimental hutch and the possibilities for the installation of different experimental setups in two dedicated chambers. The beam line includes focussing magnets, extensive diagnostics, and allows in-air installation within the experimental beam line. The facility will allow an experimental programme towards addressing many outstanding issues related to this new radiotherapy modality. We also describe the available flexibility in beam parameters for both conventional dose rates and those entering into the FLASH regime. The possibility of both living cells and treatment planning studies is anticipated to be conducted at this world-class facility. [ABSTRACT FROM AUTHOR]

The existence of dark matter in the universe is inferred from abundant astrophysical and cosmological observations. The Global Argon Dark Matter Collaboration (GADMC) aims to perform the searches for dark matter in the form of weakly interacting massive particles (WIMPs), whose collisions with argon nuclei would produce nuclear recoils with tens of keV energy. Argon has been considered an excellent medium for the direct detection of WIMPs as argon-based scintillation detectors can make use of pulse shape discrimination (PSD) to separate WIMP-induced nuclear recoil signals from electron recoil backgrounds with extremely high efficiency. However, argon-based direct dark matter searches must confront the presence of intrinsic 39Ar as the predominant source of electron recoil backgrounds (it is a beta-emitter with an endpoint energy of 565 keV and half-life of 269 years). Even with PSD, the 39Ar activity in atmospheric argon (AAr), mainly produced and maintained by cosmic ray-induced nuclear reactions, limits the ultimate size of argon-based detectors and restricts their ability to probe very-low-energy events. The discovery of argon from deep underground wells with significantly less 39Ar than that in AAr was an important step in the development of direct dark matter detection experiments using argon as the active target. Thanks to pioneering research and successful R&D, in 2012, the first 160 kg batch of underground argon (UAr) was extracted from a CO2 well in Cortez, Colorado. The DarkSide-50 experiment at the Gran Sasso National Laboratory (LNGS) in Italy, the first liquid argon detector ever operated with a UAr target, demonstrated a ∼ 1,400 suppression of the 39Ar activity with respect to the atmospheric argon. An even larger suppression is expected for 42Ar (another intrinsic beta-emitter with the 42K daughter isotope, also a beta-emitter) as its production is expected mainly in the upper atmosphere. Following the results of DarkSide-50, the GADMC initiated the UAr project for extraction from underground and cryogenic purification of 100 t of argon to be used as a target in the next-generation experiment DarkSide-20k. This paper contains a description of the Urania Plant in Cortez, Colorado, where UAr is extracted; the Aria Plant in Sardinia, Italy, an industrial-scale plant comprising a 350-m state-of-the-art cryogenic isotopic distillation column, designed for further purification of the extracted argon and further reduction of the isotopic abundance of 39Ar; and DArT, a facility for UAr radiopurity qualification at the Canfranc Underground Laboratory (LSC), Spain. Moreover, the high radiopurity of UAr leads to other possible applications, for instance, for those neutrinoless double-beta decay experiments using argon as shielding material or, more generally, for all those activities on argon-based detectors in high-energy physics or nuclear physics, which will be briefly discussed. [ABSTRACT FROM AUTHOR]

This paper presents a low-carbon optimal scheduling model for distribution networks with wind and photovoltaic (PV), accounting for supply and demand uncertainties. The model optimizes thermal generation costs, wind and PV maintenance costs, and carbon emissions using a chance-constrained approach with fuzzy variables. The clear equivalent class method simplifies these constraints for easier problem-solving. Validation on the IEEE-30 node system shows the model reduces costs by 32.9% and carbon emissions by 19.2% compared to traditional scheduling, effectively lowering both costs and the carbon footprint. This real-world optimization approach tackles uncertainty in renewable energy supply and improves system efficiency and sustainability. [ABSTRACT FROM AUTHOR]

To deal with the threat of image privacy leakage in the Internet of things, this paper presents a novel batch images encryption algorithm using the counter mode and a multi-channel processing scheme. We employ multi-thread technique combined with an adapter to construct a novel multi-channel processing scheme, which can encrypt four different sized images in one round. Moreover, the counter encryption mode, which can compute round keys from a plaintext related session key, is introduced to decrease the difficulty of session key management when dealing with batch images. The security tests demonstrate the exceptional performance of the proposed algorithm in terms of security, as evidenced by P -values of statistical tests far larger than 0.01, correlation coefficients and entropies of cipher images close to 0 and greater than 7.99. Additionally, the results of NPCR and UACI tests closely approximate the theoretical values 99.6094% and 33.4635%, the proposed algorithm can better resist statistical, exhaustive, differential, or even chosen plaintext attacks. Moreover, due to the novel parallel scheme with a linear time complexity of O(2 W +2 H), which demonstrates an acceleration of over 300% compared to existing algorithms, it only takes 2.1sto encrypt one hundred images with varying sizes. Therefore, the proposed algorithm succeeds in exceeding existing algorithms in meeting the efficiency and security requirements for encrypting batch images. [ABSTRACT FROM AUTHOR]

In this paper, we present the results of further PiC simulations in the full electromagnetic code of the processes leading to the proton-boron reactions in a single device for plasma confinement, based on miniature nanosecond vacuum discharge (NVD) in a cylindrical geometry. In particular, we present and discuss in more detail the α particle output for the real electrodes geometry used in the first aneutronic proton–boron fusion experiments with NVD. It follows from them that the total yield of α particles was accumulated in the initial experiments due to only single head-on converging of protons and boron ions accelerated in a very narrow potential well to the discharge axis. Further, in search of the ways for optimizing of proton–boron fusion in NVD, we study the scaling of fusion power depending on the size of the virtual cathode (or the inner radius of the anode space). The results of the PiC simulations by KARAT code show that the number of the proton-boron reactions at anode space of NVD increases with the anode volume grow, and the α particles output turns out to be proportional to the value of anode radius in the range RA ≈ 0.1–0.5 cm. However, the number of proton-boron reactions reaches some saturation under RA growing at the fixed time of high voltage applied and value of the energy input. In general, the formation of a more voluminous potential well (wider in radius and extended along the discharge axis), with well–defined oscillations of protons and boron ions in it, provides a noticeable increase in the output of α particles. [ABSTRACT FROM AUTHOR]

Signal beam combiners play a pivotal role in enhancing the output power of fiber lasers, which have wide-ranging applications from industrial processing to medical and military uses. This paper explores the influence of fiber twist angle and non-uniform symmetric arrangement on the performance of 19 × 1 fiber signal combiners. A simulation model was developed to analyze the impact of these parameters under adiabatic tapering conditions and the principle of brightness conservation. The model allowed for a systematic investigation of how varying twist angles and non-uniform spacings affect the combiner's performance metrics, such as transmission efficiency and beam quality. The study found that an optimal balance between high transmission efficiency (up to 98.5%) and good beam quality (minimum M2 factor of 1.06) can be achieved when the twist angle is kept below 60° and the non-uniform spacing is maintained within 10–30 μm. These conditions ensure minimal degradation of the beam quality while maximizing the transmission efficiency of the combiner. These findings offer valuable insights into the optimization of signal combiner design, which is critical for advancing high-power fiber laser systems. By carefully controlling the fiber twist angle and non-uniform spacing, designers can achieve superior performance in fiber laser applications, thereby enhancing the overall efficiency and reliability of these systems. This research contributes to the broader field of optical engineering by providing a deeper understanding of the underlying principles that govern the performance of signal combiners. [ABSTRACT FROM AUTHOR]

Introduction: External beam radiotherapy (RT) is one of the most common treatments against cancer, with photon-based RT and particle therapy being commonly employed modalities. Very high energy electrons (VHEE) have emerged as promising candidates for novel treatments, particularly in exploiting the FLASH effect, offering potential advantages over traditional modalities. Methods: This paper introduces a Deep Learning model based on graph convolutional networks to determine dose distributions of therapeutic VHEE beams in patient tissues. The model emulates Monte Carlo (MC) simulated doses within a cylindrical volume around the beam, enabling high spatial resolution dose calculation along the beamline while managing memory constraints. Results: Trained on diverse beam orientations and energies, the model exhibits strong generalization to unseen configurations, achieving high accuracy metrics, including a δ -index 3% passing rate of 99.8% and average relative error < 1% in integrated dose profiles compared to MC simulations. Discussion: Notably, the model offers three to six orders of magnitude increased speed over full MC simulations and fast MC codes, generating dose distributions in milliseconds on a single GPU. This speed could enable direct integration into treatment planning optimization algorithms and leverage the model's differentiability for exact gradient computation. [ABSTRACT FROM AUTHOR]

In this paper we explore the possibility of boosting the reactivity of non-thermal proton-boron fusion triggered by an external proton beam in a plasma at densities near and lower than solid density and temperature characteristic to laser plasma interaction. Suprathermal protons generated by collisions with alpha particles, as well as energetic protons created by the beam protons that do not undergo fusion during the stopping down in the bulk plasma, are accounted for. In addition, we conduct calculations for non-neutral plasma, motivated by recent suggestion that the number of fusion events in such system may be increased. [ABSTRACT FROM AUTHOR]

The off-axis three-mirror anastigmatic (TMA) optical system has the advantages of eliminating all primary aberrations, having no aperture obscuration, no chromatic aberration, and good environmental adaptability, making it essential in high-performance optical instruments. Freeform surfaces, with their rich degrees of freedom (DOFs) in mathematical representation, positively affect the image quality and sensitivity of optical systems. When applied correctly to off-axis TMA optical systems, they can achieve fast F -numbers, large fields of view (FOVs), and long focal lengths. There are dozens of configurations for off-axis TMA optical systems, and freeform surfaces can be combined in numerous ways within these systems. This paper first analyzes the sensitivity of off-axis TMA optical systems with different configurations, selects the configuration with the lowest sensitivity, and combines it with a low sensitivity freeform surface combination to achieve high-performance freeform off-axis TMA optical systems with low sensitivity. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Introduction: This 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. Methods: Utilizing 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. Results: The 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. Discussion: These 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This document is an editorial published in the journal Frontiers in Physics, discussing advances in nonlinear systems and networks. Nonlinear systems have nonlinear relationships between input and output, and studying them is important for understanding natural phenomena and improving engineering design. The editorial highlights 10 articles in this research topic, covering various aspects of nonlinear systems and networks, including dynamic displacement estimation, complex propagation networks, representation learning, electricity scheduling, ternary logic gates, chaotic oscillators, vibration control, brain-like coupling synchronization, pinning synchronization, and medical image encryption. The authors emphasize the need for continuous advancement in the field and express gratitude to the authors, reviewers, editors, and staff involved in the publication. [Extracted from the article]

Cubic fuzzy graphs (CFGs) offer greater utility as compared to interval-valued fuzzy graphs and fuzzy graphs due to their ability to represent the degree of membership for vertices and edges using both interval and fuzzy number forms. The significance of these concepts motivates us to analyze and interpret intricate networks, enabling more effective decision making and optimization in various domains, including transportation, social networks, trade networks, and communication systems. This paper introduces the concepts of vertex and edge connectivity in CFGs, along with discussions on partial cubic fuzzy cut nodes and partial cubic fuzzy edge cuts, and presents several related results with the help of some examples to enhance understanding. In addition, this paper introduces the idea of partial cubic α-strong and partial cubic δ-weak edges. An example is discussed to explain the motivation behind partial cubic α-strong edges. Moreover, it delves into the introduction of generalized vertex and edge connectivity in CFGs, along with generalized partial cubic fuzzy cut nodes and generalized partial cubic fuzzy edge cuts. Relevant results pertaining to these concepts are also discussed. As an application, the concept of generalized partial cubic fuzzy edge cuts is applied to identify regions that are most affected by trade deficits resulting from street crimes. Finally, the research findings are compared with the existing method to demonstrate their suitability and creativity. [ABSTRACT FROM AUTHOR]

Structured light refers to an optical field with modulated phase and amplitude, characterized by distinct spatial patterns. It has applications in optical manipulation, 3D imaging, remote sensing, and communications. The Generalized Lorenz-Mie Theory (GLMT) extends foundational Mie theory to accommodate complex structured lights, enabling precise characterization of structured light-particle interactions. GLMT has emerged as a central theoretical framework for analyzing interactions between spherical particles and arbitrary structured light. This paper introduces ABSphere, simulation software utilizing GLMT to model structured light-spherical particle interactions. It then comprehensively reviews representative structured lights, including Laguerre-Gaussian, Bessel, and Airy beams, elucidating their interactions with spherical particles. Understanding structured light scattering behavior is crucial for elucidating underlying interaction mechanisms with spherical particles. The paper also emphasizes the significance of modeling structured light scattering by particles and discusses future directions for ABSphere software. Through continuous theoretical refinements and advancements, deeper understanding of structured light-particle interaction mechanisms can be achieved, enabling innovations in optical applications and technologies. [ABSTRACT FROM AUTHOR]

Introduction: Hair loss has always bothered many people, with numerous individuals potentially facing the issue of sparse hair. Methods: Due to a scarcity of accurate research on detecting sparse hair, this paper proposes a sparse hair cluster detection model based on improved object detection neural network and medical images of sparse hair under dermatoscope to optimize the evaluation of treatment outcomes for hair loss patients. A new Multi-Level Feature Fusion Module is designed to extract and fuse features at different levels. Additionally, a new Channel-Space Dual Attention Module is proposed to consider both channel and spatial dimensions simultaneously, thereby further enhancing the model's representational capacity and the precision of sparse hair cluster detection. Results: After testing on self-annotated data, the proposed method is proven capable of accurately identifying and counting sparse hair clusters, surpassing existing methods in terms of accuracy and efficiency. Discussion: Therefore, it can work as an effective tool for early detection and treatment of sparse hair, and offer greater convenience for medical professionals in diagnosis and treatment. [ABSTRACT FROM AUTHOR]

This article presents a novel target tracking algorithm for hyperspectral low altitude UAV, combining deep learning with an improved Kernelized Correlation Filter (KCF). Initially, an image noise reduction method based on principal component analysis with Block-Matching 3D (BM3D), is employed to process redundant information. Subsequently, an image fusion method is utilized to merge the processed hyperspectral image and the high-resolution panchromatic band image to obtain a high spatial resolution image for target enhancement. Following this, YOLOv5 is used to detect the coordinate information of the UAV target in the current frame. Then, The KCF algorithm is used for target tracking in the current frame using kernel correlation filtering. Finally, the Discriminative Scale Spatial Tracker (DSST) is employed to determine the scale information to achieve a multi-scale tracking effect. The experimental results demonstrate that the algorithm presented in this paper surpasses CSK, HLT, and the conventional KCF algorithm in hyperspectral UAV datasets. On average, there is a significant increase in accuracy which is over 17% when using our algorithm. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Infrared thermography (IRT) is a non-destructive testing technique that can detect the internal defects of materials. In the detection of austenitic stainless-steel pipes with large curvature, image noise caused by uneven heating is difficult to avoid. Traditional image processing methods are less effective. According to previous works, a supervised neural network was proposed in this paper using Unet network and convolutional block attention module. Existing image processing method and networks were used to compare with the proposed method. The results show that the proposed method can remove the noise caused by uneven heating, and detect all subsurface defects in stainless-steel pipe. [ABSTRACT FROM AUTHOR]

Markovian jump Hopfield NNs (MJHNNs) have received considerable attention due to their potential for application in various areas. This paper deals with the issue of state estimation concerning a category of MJHNNs with discrete and distributed delays. Both time-invariant and time-variant discrete delay cases are taken into account. The objective is to design full-order state estimators such that the filtering error systems exhibit exponential stability in the mean-square sense. Two sufficient conditions on the mean-square exponential stability of MJHNNs are established utilizing augmented Lyapunov-Krasovskii functionals, the Wirtinger-based integral inequality, the Bessel-Legendre inequality, and the convex combination inequality. Then, linear matrix inequalities-based design methods for the required estimators are developed through eliminating nonlinear coupling terms. The feasibility of these linear matrix inequalities can be readily verified via available Matlab software, thus enabling numerically tractable implementation of the proposed design methods. Finally, two numerical examples with simulations are provided to demonstrate the applicability and less conservatism of the proposed stability criteria and estimators. Lastly, two numerical examples are given to demonstrate the applicability and reduced conservatism of the proposed stability criteria and estimator design methods. Future research could explore further refinement of these analysis and design results, and exporing their extention to more complex neural network models. [ABSTRACT FROM AUTHOR]

Introduction: This work explores the use of eXplainable artificial intelligence (XAI) to analyze a convolutional neural network (CNN) trained for disruption prediction in tokamak devices and fed with inputs composed of different physical quantities. Methods: This work focuses on a reduced dataset containing disruptions that follow patterns which are distinguishable based on their impact on the electron temperature profile. Our objective is to demonstrate that the CNN, without explicit training for these specific mechanisms, has implicitly learned to differentiate between these two disruption paths. With this purpose, two XAI algorithms have been implemented: occlusion and saliency maps. Results: The main outcome of this paper comes from the temperature profile analysis, which evaluates whether the CNN prioritizes the outer and inner regions. Discussion: The result of this investigation reveals a consistent shift in the CNN's output sensitivity depending on whether the inner or outer part of the temperature profile is perturbed, reflecting the underlying physical phenomena occurring in the plasma. [ABSTRACT FROM AUTHOR]

This paper presents an innovative approach for achieving rapid synchronization between two memristor chaotic circuits (MCCs), both with and without noise perturbations. The proposed adaptive control strategy effectively handles the uncertainty in control gains by adhering to predesigned update law. Additionally, this protocol is non-chattering and differentiable, avoiding the use of conventional discontinuous functions such as signum and absolute value functions. This method successfully mitigates the tremors caused by discontinuous functions. We derive two sufficient criteria using finite-time Lyapunov and stochastic finite-time Lyapunov stability methods. Numerical results validate the theoretical analysis and demonstrate the influence of noise intensity on convergence speed. Furthermore, the results have an application in image encryption transmission. [ABSTRACT FROM AUTHOR]

The combination of high-order modulation formats and linewidth-tolerant carrier phase estimation (CPE) can effectively improve spectrum efficiency and relax the limitation of laser linewidth. This paper presents a simple two-stage CPE algorithm for polarization-multiplexed (PM) 32-quadrature amplitude modulation (32-QAM) coherent optical communication systems. The algorithm uses an enhanced QPSK partitioning algorithm combined with a simplified 4th power CPE method for coarse estimation in the initial stage and maximum likelihood (ML) detection in the subsequent fine stage. The CPE algorithm significantly increases the number of symbols used in the first stage of coarse estimation. This results in a significant increase in the stability and reliability of the phase estimation, and the CPE algorithm significantly reduces the computational complexity. The optimal parameters, phase estimation performance, and system performance of the algorithm were investigated by building a 22 Gbaud PM 32-QAM coherent system simulation platform and a 5 Gbaud PM 32-QAM coherent system experimental platform. The results show that the proposed two-stage CPE algorithm has a stronger linewidth tolerance difference than the conventional QPSK, and the two-stage CPE algorithm with an optimal block length of 105 performs comparable to blind phase search (BPS). The optical signal noise Ratio (OSNR) value is 21.2 dB and the bit error rate (BER) is 1.8 χ 10-3 for the optimal block length of 105. The receiving-end DSP unit with a flexible scheme and good communication performance will have potential applications in adaptive elastic optical networks. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive overview of the development of flux-driven Josephson Parametric Amplifiers (JPAs) as Quantum Noise Limited Amplifier for axion search experiments conducted at the Center for Axion and Precision Physics Research (CAPP) of the Institute for Basic Science. It focuses on the characterization, and optimization of JPAs, which are crucial for achieving the highest sensitivity in axion particle detection. We discuss various characterization techniques, methods for improving bandwidth, and the attainment of ultra-low noise temperatures. JPAs have emerged as indispensable tools in CAPP's axion search endeavors, playing a significant role in advancing our understanding of fundamental physics and unraveling the mysteries of the Universe. [ABSTRACT FROM AUTHOR]

Introduction: Medical phantoms play a crucial role in medical imaging and therapy. However, the successful development of these phantoms heavily relies on a comprehensive understanding of the requirements specific to each application. Methods: In this paper, we emphasize the significance of requirement analysis in medical phantom development and develop a novel approach for gathering and classifying requirements specific for phantom development. Results: The implemented survey tool is designed to accommodate the diverse needs of stakeholders involved in phantom development, including medical staff, physicists, engineers, and product developers. To validate the effectiveness of the approach, we conduct the development of a multimodal deformable pelvic phantom, providing insights into the process and its applicability. Discussion: The results demonstrate the utility and reliability of our approach in systematically gathering, categorizing, and prioritizing requirements, thus facilitating the streamlined and efficient development of medical phantoms. [ABSTRACT FROM AUTHOR]

The paper proposes a low-pass filter adaptive iterative learning control (LPF-AILC) strategy for unmatched, uncertain, time-varying, non-parameterized nonlinear systems (NPNL systems). To address the difficulty of nonlinear parameterization terms in system models, a new function approximator (FSE-RBFNN), which combines the radial basis function neural network (RBFNN) and Fourier series expansion (FSE), is introduced to model each time-varying nonlinear parameterized function. The adaptive backstepping method is used to design control laws and parameter adaptive laws. In the process of controller design, we may encounter the problem of too many derivatives, which can cause parameter explosions after derivatives. Therefore, we introduce a first-order low-pass filter to solve this problem and simplify the structure of the controller. As the number of iterations increases, the maximum tracking error gradually decreases until it converges to the nearby region, approaching zero within the entire given interval [0, T], according to the Lyapunov-like synthesis. To mitigate the impact of initial state errors, a dynamically changing boundary layer is introduced, along with a series to deal with the unknown error upper bounds. Finally, two simulation examples prove the correctness of the proposed control method. [ABSTRACT FROM AUTHOR]

The near-eye display (NED) systems, designed to project content into the human eye, are pivotal in the realms of augmented reality (AR) and virtual reality (VR), offering users immersive experiences. A small volume is the key for a fashionable, easy-to-wear, comfortable NED system for industrial and consumer use. Freeform surfaces can significantly reduce the system volume and weight while improving the system specifications. However, great challenges still exist in further reducing the volume of near-eye display systems as there is also a limit when using only freeform optics. This paper introduces a novel method for designing compact freeform NED systems through a powerful optical-digital joint design. The method integrates a geometrical freeform optical design with deep learning of an image compensation neural network, addressing off-axis nonsymmetric structures with complex freeform surfaces. A design example is presented to demonstrate the effectiveness of the proposed method. Specifically, the volume of a freeform NED system is reduced by approximately 63% compared to the system designed by the traditional method, while still maintaining high-quality display performance. The proposed method opens a new pathway for the design of a next-generation ultra-compact NED system. [ABSTRACT FROM AUTHOR]

Quantum computing provides a revolution in computational competences, leveraging the principles of quantum mechanics to process data in fundamentally novel ways. This paper explores the profound implications of quantum computing on cryptography, focusing on the vulnerabilities it introduces to classical encryption methods such as RSA and ECC, and the emergence of quantum-resistant algorithms. We review the core principles of quantum mechanics, including superposition and entanglement, which underpin quantum computing and cryptography. Additionally, we examine quantum encryption algorithms, particularly Quantum Key Distribution (QKD) protocols and post-quantum cryptographic methods, highlighting their potential to secure communications in the quantum era. This analysis emphasizes the urgent need for developing robust quantum-resistant cryptographic solutions to safeguard sensitive information against the imminent threats posed by advancing quantum technologies. [ABSTRACT FROM AUTHOR]

Identifying cable insulation defects is crucial for preventing system failures and ensuring the reliability of electrical infrastructure. This paper introduces a novel method leveraging the Markov transition field (MTF) and Transformer network to improve the precision of cable insulation defect identification and enhance the algorithm's noise resistance. Firstly, the algorithm performs modal transformation on the time series data acquired by the ultrasonic probe through MTF, generating corresponding images. Following this, the image data are input into a pre-trained Transformer network to achieve automated feature extraction. Subsequently, a multi-head attention mechanism is introduced, which assigns weights to the features extracted by the Transformer network, thereby emphasizing the most critical information for the identification task. Finally, more accurate defect identification is achieved based on the weighted features. The results demonstrate that this method achieves higher accuracy and stronger noise resistance compared to traditional image processing and recognition methods, making it a robust solution for cable insulation defect identification. [ABSTRACT FROM AUTHOR]

Introduction: The emergence of collective behavior often depends on the adequate interaction of individuals through self-organization and the exchange of local information. When facing external threats, communication among individuals requires both rapid and effective information exchange to characterize sudden events. In this paper, we introduce the mechanism of emotions into the modeling of dynamics to study collective avoidance behavior in response to threats. Methods: A scenario involving a hidden dynamic threat is constructed to test the avoidance and survival capabilities of the collective when faced with a lack of effective information. By employing the activation and spread of emotion in modeling, the collective may self-organized and adeptly mitigate risks and enhance their own benefits. Results: Through adjustments to the intensity of emotional activation, spread, and decay, rich behaviors emerge. Relying on the regulation of emotion, the collective exhibits different response strategies and action patterns when facing threats, in which the optimal performance from the macroscopic level is expectable. Discussion: By analyzing these phenomena, it can enhance our understanding of the emotional states of collective in response to threats and the methods of controlling in intelligent collective motion. [ABSTRACT FROM AUTHOR]

Compound semiconductors are playing a major role in the production of X-ray pixel detectors for the application in laboratories and beamlines at photon sources. The performance of these detectors has constantly been improved for the last decades but experiments are still limited by the properties of the detector material, especially under high flux illumination. The fast development of perovskite crystals opens the possibility for new materials to be used as highly efficient X-ray pixel detectors. The published data until now, of the transport properties, demonstrate the large potential of perovskite semiconductors. The achieved values are comparable with the ones of CdTe-based detectors. This paper presents potential perovskite-based detector materials and compares their performance with the state-of-the-art CdTe-based detectors. The perspectives of perovskite semiconductors are promising for the production of large area X-ray detectors but still some challenges remain. [ABSTRACT FROM AUTHOR]

The safety of SCUBA divers remains at high risk in deep-sea owing to multiple factors such as dangerous surrounding, rely upon technical equipment necessary for life support, decreased underwater navigation, and communication infrastructure. Gradual decrease and increase in water temperature and pressure corresponding to depth are among the most common problems that cause most of the fatalities in deep-sea diving. Therefore, different gadgets for accurate measurement of vital parameters, reliable navigation, and seamless communication are of prime importance. In this paper, we propose an all-optical technique for local and remote monitoring of underwater temperature and pressure for deep-sea SCUBA divers based on fiber Bragg grating (FBG) sensors and underwater optical communication-single mode fiber (UWOCSMF) integrated transmission system. The proposed technique is implemented using two FBG temperature and pressure sensors fixed over diver's suit and UWOC-SMF integrated transmission system for simultaneous local and remote monitoring of underwater temperature and pressure. Remote monitoring of underwater temperature and pressure is achieved at ship station through a remotely operated underwater vehicle (ROV) and UWOC-SMF integrated transmission system by means of shifts in the original Bragg wavelengths of sensors due to temperature and pressure variations. The performance of the sensors is analyzed for pressure and temperature in the range of 0 to 6.4 MPa (≈0 to 655mH2O) and 40 to -2°C, respectively corresponding to different depths. The results show that the proposed technique can work well in the deep ocean over a range of pressures and temperatures of 0-7 MPa and 40 to -2°C while achieving a temperature sensitivity of 4.3 p.m./°C and a pressure sensitivity of 30.5 p.m./MPa. Clear spectra of reflected signals from FBG sensors at ship station are achieved after signal transmission over UWOC-SMF hybrid link. [ABSTRACT FROM AUTHOR]

An effective formula for the shape-sensitivity analysis of electromagnetic scattering is presented in this paper. First, based on the boundary element method, a new electromagnetic scattering formula is derived by combining the traditional electromagnetic scattering formula with the non-uniform rational B-spline (NURBS) curve, and the geometric model is represented by NURBS, which ensures the geometric accuracy, avoids the heavy grid division in the optimization process, and realizes the fast calculation of high-fidelity numerical solutions. Second, by deducing the sensitivity variables, the electromagnetic scattering equation of shape optimization is obtained, which can provide reliable data references for shape optimization. Finally, the effectiveness and accuracy of the algorithm are demonstrated by an example, and the sensitivity data of some examples are given. [ABSTRACT FROM AUTHOR]

This study highlights the drawbacks of current quantum classifiers that limit their efficiency and data processing capabilities in big data environments. The paper proposes a global decision tree paradigm to address these issues, focusing on designing a complete quantum decision tree classification algorithm that is accurate and efficient while also considering classification costs. The proposed method integrates the Bayesian algorithm and the quantum decision tree classification algorithm to handle incremental data. The proposed approach generates a suitable decision tree dynamically based on data objects and cost constraints. To handle incremental data, the Bayesian algorithm and quantum decision tree classification algorithm are integrated, and kernel functions obtained from quantum kernel estimation are added to a linear quantum support vector machine to construct a decision tree classifier using decision directed acyclic networks of quantum support vector machine nodes (QKE). The experimental findings demonstrate the effectiveness and adaptability of the suggested quantum classification technique. In terms of classification accuracy, speed, and practical application impact, the proposed classification approach outperforms the competition, with an accuracy difference from conventional classification algorithms being less than 1%. With improved accuracy and reduced expense as the incremental data increases, the efficiency of the suggested algorithm for incremental data classification is comparable to previous quantum classification algorithms. The proposed global decision tree paradigm addresses the critical issues that need to be resolved by quantum classification methods, such as the inability to process incremental data and the failure to take the cost of categorization into account. By integrating the Bayesian algorithm and the quantum decision tree classification algorithm and using QKE, the proposed method achieves high accuracy and efficiency while maintaining high performance when processing incremental sequences and considering classification costs. Overall, the theoretical and experimental findings demonstrate the effectiveness of the suggested quantum classification technique, which offers a promising solution for handling big data classification tasks that require high accuracy and efficiency. [ABSTRACT FROM AUTHOR]

The symmetry features of fractional differential equations allow effective explanation of physical and biological phenomena in nature. The generalized form of the fractional differential equations is the variable-order fractional differential equations that describe the physical and biological applications. This paper discusses the closed-form traveling wave solutions for the nonlinear space-time variable-order fractional modified Kawahara and (2 + 1)-dimensional Burger hierarchy equations. The variable-order fractional differential equation has a derivative operator in the Caputo sense that is converted into the integer-order ordinary differential equation (ODE) by fractional transformation. The obtained ODE is solved by the exponential rational function method, and as a result, new exact solutions are constructed. Two problems are proposed to confirm the solutions of the space-time variable-order fractional differential equations. [ABSTRACT FROM AUTHOR]

This article discusses the field of origami-inspired metamaterials and metastructures. Metamaterials and metasurfaces are artificial structures that exhibit unique properties not commonly found in nature, offering solutions for engineering problems. The designable and reconfigurable properties of these materials are achieved through active material and geometric variations, with origami-inspired designs allowing for bending-dominating objects. The article features four peer-reviewed papers that explore the challenges and opportunities in this field, focusing on the design, actuation, and application of origami and kirigami-based metamaterials. These papers contribute to the advancement of metamaterials and metasurfaces, offering theoretical insights and practical applications that could revolutionize various industries. The integration of origami-inspired designs into metamaterials shows promise for creating highly adaptable and multifunctional systems. [Extracted from the article]

Hierarchical buckling is a novel phenomenon observed in elastic fibers subjected to transverse confinement; however, the deformation mechanisms and modal transitions of this unique phenomenon remain to be elucidated. This paper investigates the hierarchical buckling of elastic fibers with elliptical (circular) cross-sections under transverse confinement through analytical derivations and numerical simulations. Various magnitudes of hierarchical buckling of fibers are observed with the variation of the controlled elastic matrix stiffness. An analytical solution is first derived for the fiber's buckling phenomenon, and the hierarchical buckling is accomplished through the superposition of buckling at various modes. The theoretical results are validated against the finite element simulations with good agreement. It is demonstrated from the parametric results that the hierarchical buckling phenomenon is primarily influenced by the stiffness of the external transverse confinement (matrix), which is defined as a dimensionless parameter. It is thus illustrated from the computational results that the buckling of elastic fibers within a solid or fluid matrix can be controlled and customized. The present work provides theoretical guidance for the application of elastic fibers in stretchable conductor fibers and flexible electronic devices. [ABSTRACT FROM AUTHOR]