bluuebunny/crossref_metadata_2025 huggingface dataset:
A bibliometric analysis of publications on the ethical considerations of sex robots (2003‒2022)
Authors: Tang Yan , Zhang Na , Liu Shen
Published: February 2025

Robots play a crucial role in society, with pioneers like Joseph Engelberger envisioning their use beyond factory settings as early as 1989. The development of sex robots, stemming from the sex industry’s adoption of new technologies, represents one of the most ethically complex applications of robotics. Integrating them into society raises numerous moral challenges, intersecting with sociology, philosophy, and psychology. To address these ethical dilemmas, this study employed bibliometric tools to map research trends and patterns, aiming to provide a clearer understanding of the issues surrounding sex robots and propose potential solutions. The Web of Science database contained 1010 publications on sex robots published between 2003 and 2022 that met the search criteria. The annual publication volume on the topic of sex robots has increased significantly over the 2003‒2022 timeframe. Jan Peters and Atsuo Takanishi are the authors who have published the most research on the topic of sex robots. The journal with the highest publication volume on sex robots among the top 10 journals is the International Journal of Social Robotics. Over the past 20 years, the Centre National de la Recherche Scientifique has published the most research on the topic of sex robots. The United States has the highest influence in the field of sex robot research. Emerging keywords such as “sex robot,” “social robot,” “therapy,” and “trust” have gained significant popularity, as evidenced by an increase in references and citations in the literature. Utilizing visual mapping techniques and an analysis of key publications, this study describes the latest ideas and development directions within the field of sex robots research, providing insights into the growing trends in this domain.
Grid fins and landing gear responsible for Falcon-9 booster’s unique sonic boom
Authors: Liebendorfer Adam
Published: February 2025

Researchers combine sonic boom theory with computational fluid dynamics to explain the role of forward-migrating compression wave and rearward-migrating rarefaction wave.
Microscale insights into shear viscosity scaling of granular suspensions across the jamming transition
Authors: Zhang Zaohui , Man Teng , Huppert Herbert E. , Galindo-Torres Sergio A.
Published: February 2025

We present evidence of the jamming transition across semi-dilute to dense regimes in suspensions from a thorough study combining both experiments and numerical results. Following an extensive experimental study, where such a transition was observed, we perform constant-volume shearing simulations of non-Brownian granular suspensions using the discrete element method coupled with the lattice Boltzmann method. We choose a wide range of solid fractions, shear rates, fluid viscosities, particle sizes, particle stiffness, and inter-particle frictional coefficients to obtain a scaling solution for the viscous behavior of suspensions in both semi-dilute and dense regimes. This result demonstrates that, with the semi-dilute-dense jamming transitional solid fraction, ϕd, there exists a strong correlation between the inverse relative viscosity and the shear stress. The transitional behavior of suspensions closely corresponds to the microstructure development, especially the largest particle cluster. Suspensions undergo a jamming transition when the contact network across the whole system is formed. This work incorporates both the ϕ-dependence and the γ̇-dependence of suspension viscosity in a universal framework, which provides a scaling solution for granular suspensions across semi-dilute and dense regimes and sheds light on the jamming transition mechanisms.
Symmetry breaking and nonlinear transformation of two-layer eastward propagating dipoles
Authors: Crowe Matthew. N. , Sutyrin Georgi. G.
Published: February 2025

We study the evolution of eastward propagating dipoles (modons) in a two-layer quasi-geostrophic β-plane model using high-resolution numerical simulations. Various combinations of background gradients of potential vorticity in the upper and lower layers (which may include sloping topography) shed light on the recently discovered breakdown mechanisms and rich dynamics of dipolar vortices. We consider dipoles with a circular separatrix in the upper layer and, owing to the β-effect in the same layer, the symmetry of the flow breaks due to an exponentially growing, rotating, asymmetric mode of linear instability associated with Rossby wave radiation. Further nonlinear transformation is found to consist of two phases: fast partner separation, resulting in a deceleration of the eastward drift, and subsequent slow separation with a saturated asymmetric mode accompanied by much weaker, shorter Rossby waves. This weakly radiating phase—with pulsating partners and homogenized potential vorticity between the partners and the separatrix—can be considered as a new type of long-lived dipole. Conversely, when no β-effect is present in the upper layer, the dipoles remain nearly symmetric, even when the topographic β-effect is present in the active lower layer. In this case, the development of a weak asymmetric mode is related to a small meridional shift of the dipole center on the numerical grid.
Pressurized diesel diffusion flame topology detection by three-dimensional computed tomography
Authors: Gao Yu , Chen Haiyan , Wu Yue
Published: February 2025

In this work, diffusion diesel flames injected by a single-hole nozzle under different injection pressures and ambient temperatures in a constant volume combustion chamber (CVCC) are reconstructed using three-dimensional (3D) computed tomography (CT), representing the first sets of 3D diffusion flame topology within the pressurized combustor. Initially, an optical measurement system comprising fiber bundle endoscopes and high-speed cameras is arranged around the CVCC to capture line-of-sight flame projections from multiple different orientations. Moreover, 3D flames are reconstructed based on the CT technique with enhanced accuracy designed for measuring facilities with restricted optical access. The instantaneous 3D flame topology and its evolution process are analyzed under different injection pressures and ambient temperatures. Compared to two-dimensional analysis, the 3D reconstructions manage to provide in depth investigations on spatial flame intensity evolution, representative turbulent flame patterns, ignition flame kernels merging process and internal void region detection, thereby facilitating a deeper understanding of the combustion process in CVCCs.
Distinctive impact responses of extraterrestrial regolith simulants: Insights from crater morphology classification and granular dynamics through machine learning and x-ray computed tomography
Authors: Ishii Takuma , Kioka Arata , Huang Jyh-Jaan Steven , Tsuji Takeshi , Yamada Yasuhiro
Published: February 2025

Impact responses of granular materials remain poorly understood, posing significant challenges to extraterrestrial exploration activities such as landing, sampling, drilling, and construction. We studied the crater morphology formed in the granular media and their granular behavior on low-velocity impact cratering by integrating three-dimensional surface scanning, machine learning-based classification, and x-ray computed tomography (X-CT) imaging. Our laboratory experiments focused on lunar (LHS-1 and LMS-1) and Martian regolith simulants (MGS-1) and terrestrial fine silica sand (T-8) as studied granular materials. The profiles of formed craters were analyzed under various experimental conditions, defined by different spherical projectile diameters and fall heights, accounting for the low kinetic energy at the granular surface contact of 0.1–0.5 mJ. In machine learning, we applied logistic regression models to classify target granular materials based on crater profile features, revealing primary morphological differences influenced by the given granular properties. The particle distribution from X-CT imaging revealed notable differences in the granular behavior of regolith simulants due to impact compared with T-8. These results underscored significant differences between the granular properties of terrestrial fine sand and regolith simulants. Additionally, our X-CT data emphasized that the high cohesion of LMS-1 significantly enhanced its resistance to impact, resulting in the porosity decline beneath the crater bottom of 0.20 and 0.03 on average in the LHS-1 and LMS-1 media, respectively, when the kinetic energy was 0.3 mJ. Our findings highlight the distinctive granular properties of regolith particles, advancing our understanding of their granular responses to impact.
Rapid airfoil design based on ellipse direct method and prediction model
Authors: Wang Zhen , Yuan Qi , Zhu Yi , Xie Xingbo , Gu Wenbin , Chen Heng , Hu Yifan
Published: February 2025

The rapid, efficient, and rational design of airfoils is crucial for enhancing the flight efficiency of aircraft. At present, traditional two-dimensional airfoil design methods are mostly based on the indirect transformation of the features of existing airfoil libraries, which makes it challenging to develop new airfoil characteristics to meet the increasingly stringent flight index requirements. Therefore, a direct airfoil design path with less time consumption and high efficiency and a new method integrating direct airfoil generation with predicting aerodynamic performance parameters were put forward. First, an in-depth theoretical analysis of the turbulent characteristics of airfoils was conducted, which can more accurately predict performance parameters, such as the lift-drag ratio, lift coefficient, and drag coefficient of airfoils. Second, an airfoil generation strategy based on the “direct ellipse method” was proposed. Taking the ellipse as the geometric carrier, the upper curve of the airfoil is rapidly generated by combining the random three-point descent curve and the parameter calculation theory, and the overall design of the airfoil is efficiently accomplished through coordinate transformation. Finally, based on the airfoil coordinate points and a large amount of lift–drag ratio data calculated by Python programs and computational fluid dynamics programs, an airfoil lift–drag ratio prediction model based on deep learning multilayer perceptron theory was constructed. Compared with the traditional finite element calculation method, this model significantly accelerates the calculation speed of the lift–drag ratio, and the error rate is only 2.783%. Combining the new airfoil generation method and the lift–drag ratio prediction model can complete the overall airfoil design in a diverse, rapid, and accurate way, providing a reliable approach and valuable reference for efficient and rapid airfoil design.
Data-driven investigation and modeling of flow in swept compressor cascades
Authors: Zhang Jiancheng , Jin Donghai , Guo Hanwen , Yu Hao
Published: February 2025

Swept blades are widely adopted in fan and compressor designs to balance high load, high throughflow, and wide stall margins. However, their complex effects on the incoming flow field often result in inefficient trial-and-error design processes. To address this challenge, this study introduces a data-driven approach to analyze cascade flow fields and reveal the spanwise migration effects induced by swept blade geometry. A novel concept, the “actual sweep angle,” is proposed to quantify these effects, particularly their impact on incidence angle variations. Leveraging this concept, a predictive model is developed, achieving a high coefficient of determination (R2 = 0.9821). The swept blade design methodology based on this model significantly reduces the design cycle and improves aerodynamic performance in high-load fan and compressor applications, providing an efficient and reliable data-driven solution for modern swept blade optimization.
Implementation of fundamental equations of state in a lattice Boltzmann model
Authors: Zheng Zhaoqi , Huang Yonghua
Published: February 2025

In the pseudopotential lattice Boltzmann (LB) model, the physical behaviors of fluids are modeled through interparticle forces, which are closely tied to the equation of state (EOS). Existing simulations mainly rely on cubic EOS, which significantly lags behind modern multiparameter EOS in terms of the prediction of thermodynamic properties. However, there have been no reports on the application of such a high-precision EOS in LB simulations. In this study, a method for implementing fundamental equations of state in Helmholtz energy form (HEOS) in the LB framework is proposed. A novel unit conversion approach is developed, which enables the appropriate conversion of all information between lattice and physical units, overcoming the limitations of existing methods that fail to correctly convert energy information. This approach allows the direct conversion of the pressure between the lattice and physical units without the need to specify the lattice unit values for each parameter in the equation of state. The HEOS of water is used as an example to validate the feasibility of the proposed method and unit conversion approach. The average error of liquid–vapor coexistence densities obtained from the LB simulations using the HEOS is 0.46%, significantly lower than 22.5% by using the typical cubic Peng–Robinson (PR) EOS. Although the computational resource consumption tripled that of the PR EOS, the incorporation of HEOS demonstrated much stronger capabilities in simulations with phase-change phenomena, accurately predicting the specific latent heat of water in film evaporation from 100 to 341.6 °C where the one with PR EOS failed.
High-performance GaSb-based interband cascade lasers with a top hybrid cladding
Authors: Huang Wenxiang , Hu Shiyu , Tu Junjie , Wang Peng , Ma Wenquan , Zhang Yanhua , Huang Jianliang
Published: February 2025

We report high-performance GaSb-based interband cascade lasers (ICLs) with a top hybrid cladding and operating around 3.5 μm at room temperature. Unlike the conventional superlattice (SL) claddings in GaSb-based ICLs, the top cladding of the present ICLs consisted of an n-SL, followed by an n+-InAs0.91Sb0.09 layer, while their bottom claddings entirely consisted of an n-SL. This “asymmetric” waveguide design is potentially advantageous, since it improves the active-core optical confinement factor, as well as the overall thermal conductance, especially for epitaxial-side-down mounted devices. When operated in pulsed mode, the broad-area ICL featured a characteristic temperature of 57 K and a threshold current density as low as 95 A/cm2 at 25 °C, which is the lowest ever reported for an ICL operating at a similar temperature. The narrow-ridge device processed from the same wafer, operated in continuous-wave (CW) mode up to a temperature as high as 100 °C, with a CW threshold current density of only 150 A/cm2 at 20 °C, although the current leakage was appreciable at the sidewalls.
Revealing underlying mechanisms of ambient air mass entrainment around subcooled and flash boiling fuel sprays by a hybrid data-driven framework
Authors: Zhao Fengnian , Meng Yangrui , Zhou Ziming , Zhai Qiaozhi , Hung David L. S. , Li Xuesong , Xu Min
Published: February 2025

Fuel–air interaction inside a gasoline direct injection engine is a key factor affecting the fuel mixture formation and combustion efficiency. In this study, fluorescent particle image velocimetry and Mie scattering are applied to capture the air velocity field and spray morphology simultaneously. This work aims to reveal the dominant factors and underlying mechanisms affecting air mass entrainment under various superheated conditions ranging from subcooled to flash boiling. First, the spray-induced air velocity and mass entrainment rate are examined at the spray boundary. Next, a hybrid data-driven framework consisting of eXtreme Gradient Boosting (XGBoost) and data fitting models is proposed. The XGBoost model identifies two dimensionless parameters, superheated degree (Pa/Ps) and ambient-to-100 kPa density ratio (ρa/ρ100), as the major factors affecting the entrained air mass. Afterward, the data fitting results demonstrated that the air mass entrainment rate can be expressed by a generalized equation using the above two dimensionless parameters, Pa/Ps and ρa/ρ100. The influence of Pa/Ps is more significant, with the air mass entrainment rate decaying exponentially with respect to Pa/Ps. At last, a critical value of 1.7 for Pa/Ps is experimentally determined to distinguish the influence of superheated degree on the air mass entrainment. When Pa/Ps is less than 1.7, Pa/Ps strongly affects the air mass entrainment. Otherwise, the dependence of air entrainment on Pa/Ps is much reduced. The quantitative correlation between air mass entrainment rate, superheated degree, and air density ratio provides good insights into the impact of flash boiling on ambient air mass entrainment.
Polar–nonpolar collector hybrids boosted nanoemulsion for improving low-rank coal flotation
Authors: Kong Yibo , Lu Hang , Li Yunchang , Liu Jincheng , Zhou Bobo , Yang Xuesong , Wang Lei , Xia Yangchao , Li Zhe , Xing Yaowen , Gui Xiahui
Published: February 2025

Low-rank coal (LRC), with abundant oxygen-containing functional groups and strong hydrophilic polarity, is challenging for the effective surface adsorption of traditional nonpolar collectors and high flotation efficiency. In this study, a boosted nanoemulsion with average droplet size of 130.3 nm was prepared for improving the LRC flotation, using the low-energy emulsification method with hybrids of polar (methyl oleate) and nonpolar (diesel) collectors. The difficult-to-float mechanisms of LRC were first investigated through surface morphology and elemental composition. The dynamic stability, macroscopic appearance, and droplets size distribution of nanoemulsions boosted by different collectors were systematically explored by multiple-light and dynamic-light scattering methods, respectively. The results indicated that the nanoemulsion with hybrids of polar–nonpolar collectors had stronger stability and relatively low droplet size, compared to those of nanoemulsion with individual polar collector. In addition, the LRC flotation experiments identified that the hybrids boosted nanoemulsion had better combustible matter recovery rate (89.44%) than those collectors without emulsification process (74.90%). Moreover, the wrap angles and adhesion/desorption forces of particles-bubbles, as well as adhesion behaviors and wetting heat variations of particles-nanoemulsions, were measured to identify the improving mechanisms of nanoemulsions for LRC flotation. The polar groups of emulsifier and collector could synergistically adsorb on the oxygen-containing functional groups of LRC surface to reduce the hydrophilic polarity, thereby accelerating the adsorption of nonpolar groups and improving the flotation efficiency. This research can provide new insight into the role of nanoemulsion with different collectors in LRC flotation.
Characterization and reduction of RF loss up to 110 GHz by optimizing the UID-GaN layer in N-polar GaN material
Authors: Qi Yu , Zhou Yu , Li Qian , Cheng Sheng , Zhan Xiaoning , Zhang Xinkun , Wang Qingru , Liu Jianxun , Sun Qian , Yang Hui
Published: February 2025

In this work, the mechanism of RF loss up to 110 GHz for N-polar GaN has been studied. With the assistance of S-parameter characterization combined with secondary ion mass spectroscopy analyses, the incorporated oxygen impurity has been identified to be the main source bringing about the severe RF loss of N-polar GaN. The compensation of Fe-doping enables an effective reduction in RF loss. Moreover, the unintentionally doped (UID) GaN layer grown on top of the Fe-doped GaN buffer requires a careful design due to the distinct memory effect of Fe-doping in N-polar GaN. With an optimization of its thickness, a very low RF loss of 0.36 dB/mm at 94 GHz has been attained. Furthermore, by fitting the Fe concentration profile of UID-GaN according to the mass balance rate equation, it is found that the desorption of Fe on the N-polar GaN surface is significant. A bond-based model is introduced to elucidate the difference of the Fe memory effect between Ga-polar and N-polar GaN.
Exploring residents’ and fellows’ confidence in sexually transmitted infection management and confidential care for adolescents
Authors: Wynn Cameron L. , Wong Catherine , Lee MinJae , Tiro Jasmin A. , Francis Jenny K. R.
Published: February 2025

Background In the United States, many residents and fellows lack confidence in their ability to confidentially counsel adolescents regarding sexual health topics. Little is known about how personal and professional experiences may affect medical residents’ and fellows’ confidence in counselling adolescents about sexual health services. Methods We invited 361 residents and fellows (trainees) from paediatric, obstetrics and gynaecology, as well as family medicine programs at an academic medical centre in Texas to complete a survey about confidence in counselling about sexual health services. We describe respondents’ demographics, medical training, personal sexual healthcare experiences, and confidence in confidential care and STI management measured on a four-point Likert scale. We calculated average confidence scores, dichotomised responses into confident (≥3 points) and not-so-confident (<3 points), and performed two multivariable regression analyses to explore factors associated with confidence. Results Among 228 trainees (63% response rate), 54% were confident in confidential care and 81% were confident in STI management. Those who received training in aspects of confidential care and STI management reported significantly greater confidence in many of these areas. Respondents who recalled private discussions with their personal healthcare provider as an adolescent reported confidence in STI management (P = 0.02) compared to those who did not have that experience. Conclusions Medical training should focus on communication training to address gaps in confidence, particularly regarding adolescent confidentiality. Clinicians’ personal experiences may impact aspects of confidence, highlighting the importance of encouraging trainees to reflect on how their background might influence their clinical practice.
The antimicrobial activity of ETD151 defensin is dictated by the presence of glycosphingolipids in the targeted organisms
Authors: Kharrat Ons , Yamaryo-Botté Yoshiki , Nasreddine Rouba , Voisin Sébastien , Aumer Thomas , Cammue Bruno P. A. , Madinier Jean-Baptiste , Knobloch Thomas , Thevissen Karin , Nehmé Reine , Aucagne Vincent , Botté Cyrille , Bulet Philippe , Landon Céline
Published: February 2025

Fungal infections represent a significant global health concern, with a growing prevalence of antifungal drug resistance. Targeting glucosylceramides (GlcCer), which are functionally important glycosphingolipids (GSL) present in fungal membranes, represents a promising strategy for the development of antifungal drugs. GlcCer are associated with the antifungal activity of certain plant and insect defensins. The 44-residue ETD151 peptide, optimized from butterfly defensins, is active against several fungal pathogens. ETD151 has been shown to induce a multifaceted mechanism of action (MOA) in
Botrytis cinerea
, a multiresistant phytopathogenic fungus. However, the target has yet to be identified. Our findings demonstrate that the presence of GlcCer in membranes determines the susceptibility of
Pichia pastoris
and
Candida albicans
toward ETD151. To ascertain whether this is due to direct molecular recognition, we demonstrate that ETD151 selectively recognizes liposomes containing GlcCer from
B. cinerea
, which reveals a methylated-sphingoid base structure. The dissociation constant was estimated by microscale thermophoresis to be in the µM range. Finally, fluorescence microscopy revealed that ETD151 localizes preferentially at the surface of
B. cinerea
. Furthermore, the majority of prokaryotic cells do not contain GSL, which explains their resistance to ETD151. We investigated the susceptibility of
Novosphingobium capsulatum
, one of the rare GSL-containing bacteria, to ETD151. ETD151 demonstrated transient morphological changes and inhibitory growth activity (IC
50
~75 µM) with an affinity for the cell surface, emphasizing the critical importance of GSL as target. Understanding the MOA of ETD151 could pave the way for new perspectives in human health and crop protection.
High Q
2 Behavior of the Proton Structure Function through the Balitsky-Kovchegov Equation
Authors: Kou Wei , Xie Gang , Wang Xiaopeng , Han Chengdong , Chen Xurong
Published: February 2025

Numerous experimental and theoretical investigations have highlighted the power law behavior of the proton structure function F
2(x, Q
2), particularly the dependence of its power constant on various kinematic variables. In this study, we analyze the proton structure function F
2 employing the analytical solution of the Balitsky-Kovchegov equation, with a focus on the high Q
2 regime and small x domains. Our results indicate that as Q
2 increases, the slope parameter λ, which characterizes the growth rate of F
2, exhibits a gradual decrease, approaching a limiting value of λ ≈ 0.41 ±0.01 for large Q
2. We suggest that this behavior of λ may be attributed to mechanisms such as gluon overlap and the suppression of phase space growth. To substantiate these conclusions, further high-precision electron-ion collision experiments are required, encompassing a broad range of Q
2 and x.
A geometric derivation of Noether's theorem
Authors: Houchmandzadeh Bahram
Published: February 2025

Nother's theorem is a cornerstone of analytical mechanics, making the link between symmetries and conserved quantities. In this article, I propose a simple, geometric derivation of this theorem that circumvent the usual difficulties that a student of this field usually encounters. The derivation is based on the integration the differential form dS=pdq-Hdt, where S is the action function, p the momentum and H the Hamiltonian, over a closed path.
Enzyme-free dual-channel optical biosensor employing chitosan-coated ZnS: Mn nanomaterials for the ultra-sensitive detection of doxycycline hyclate
Authors: Nguyen Son Hai , Tran Mai Thi
Published: February 2025

Developing enzyme-free biosensors offers promising solutions for more effective antibiotic detection methods, especially for complex matrices and unstable environmental conditions where traditional enzymatic approaches may fail. This paper introduces an innovative dual-channel optical biosensor using chitosan-coated ZnS:Mn nanomaterials for the ultra-sensitive detection of doxycycline hyclate, a commonly used antibiotic from the tetracycline (TET) class. The developed sensors demonstrate remarkable sensitivity, exhibiting a linear working range from 13.1 to 72.2 femtomolar (fM), with an impressive detection limit of just 2.09 fM and a limit of quantification of 6.32 fM. They also exhibit excellent selectivity against a variety of common antibiotics, including ampicillin, penicillin, cephalexin, amoxicillin, TET, as well as non-antibiotic substances like glucose. Furthermore, the sensors maintain high stability over time and perform effectively in complex matrices such as tap water. These features suggest that our novel sensor could significantly improve antibiotic monitoring in clinical and environmental contexts, making a substantial contribution to the global fight against antibiotic resistance.
Instance segmentation from particle holograms
Authors: Senger Corey , Hong Jiarong
Published: February 2025

Holographic microscopy has emerged as a low-cost and highly compact technique for 3D imaging of microscopic particles in suspension. However, its broad application is largely limited by the inclusion of multiple steps in extracting the particles from the hologram image, which can be computationally expensive and often involves human intervention. We introduce HoloDINO, a transformative model that leverages instance segmentation for streamlined, end-to-end particle detection and contour extraction. By pre-training a data-intensive transformer model on synthetic particle contours and fine-tuning it on experimental data, our approach demonstrates robust performance across synthetic holograms of varying particle concentration, morphology, and optical properties, as well as experimental holograms of dental aerosols and water spray droplets. HoloDINO surpasses conventional methods, which typically involve multiple steps—such as reconstruction, autofocusing, and segmentation—by consolidating these into a single, efficient process that delivers precise morphological data for each particle in one forward pass. This advancement not only facilitates real-time applications but also significantly enhances the generalization capabilities across diverse settings, paving the way for broader adoption of holography in particle analysis.
Is every triangle a trajectory of an elliptical billiard?
Authors: Dragović Vladimir , Radnović Milena
Published: February 2025

Using Marden’s Theorem from geometric theory of polynomials, we show that for every triangle there is a unique ellipse such that the triangle is a billiard trajectory within that ellipse. Since 3-periodic trajectories of billiards within ellipses are examples of the Poncelet polygons, our considerations provide a new insight into the relationship between Marden’s Theorem and the Poncelet Porism, two gems of exceptional classical beauty. We also show that every parallelogram is a billiard trajectory within a unique ellipse. We prove a similar result for the self-intersecting polygonal lines consisting of two pairs of congruent sides, named ‘Darboux butterflies’. In each of three considered cases, we effectively calculate the foci of the boundary ellipses.
A deep learning approach for electric field profile reconstruction based on the E-FISH method
Authors: Yang Zhijian , Sugeng Edwin Setiadi , Chng Tat Loon
Published: February 2025

Electric field induced second harmonic generation (E-FISH) is a laser-based method that has become increasingly popular for measuring electric fields in non-equilibrium plasmas. A key limitation of this diagnostic is that the signal magnitude tends to be influenced by regions beyond the focal volume, and more generally by the entire electric field profile along the probe beam path. One approach to resolving this issue, is to make multiple measurements of the E-FISH signal by translating the laser beam focus with respect to the electric field distribution. The objective is to use this resulting signal profile to reconstruct the unknown electric field profile. Termed here as the inverse ‘E-FISH problem’, this work describes a machine learning (ML) approach to perform the reconstruction in a systematic and efficient manner. Two commonly used deep learning (DL) algorithms—artificial neural networks and convolutional neural networks are applied to this inverse problem, with both producing promising results. These DL networks accept an E-FISH signal profile described by a stipulated number of data points (sampled along the beam path), and provides the unknown electric field profile along the direction of the laser beam as an output. Excellent predictive accuracy and good overall generalizability is obtained on test data, extending to cases where the input profile is formed by a smaller sub-sample of data points or when noise is artificially introduced. More importantly, good performance is maintained when the model is applied to experimental data. In its present form however, the model is applicable only to a large subset of symmetric profiles typical of canonical electrode geometries, and does not generalize to asymmetric (as well as antisymmetric) distributions, or those of an entirely random nature. However, with the appropriate training, we expect that these issues can be overcome, thereby extending the model’s applicability. This supports the continued use of ML methods for solving inverse problems of this nature, and to the field of non-equilibrium and low temperature plasmas in general.
Confinement-driven enhancement of negative ion production in anode spot plasma
Authors: Choi Jaeyoung , Hwang Y S , Kim June Young , Chung Kyoung-Jae
Published: February 2025

This study presents a novel application of anode spot plasmas to enhance negative ion production within electronegative gas discharges, specifically focusing on negative hydrogen ions (

H

−

ions) in argon-hydrogen mixtures. By confining ions within a double layer structure, our experiments demonstrate a significant increase in

H

−

ion density within the anode spot plasma, with electronegativity (defined as the density ratio of negative ions to electrons) increasing by up to 20-fold. This enhancement in

H

−

ion production extends beyond conventional volume production mechanisms. We propose a model highlighting the role of the anode spot plasma’s double layer potential in ion confinement, introducing an ‘inflow’ process where negative ions from the surrounding plasma are effectively trapped. Our findings underscore the influence of key parameters, such as pressure and anode size, in optimizing negative ion density via tailored confinement. These insights provide a pathway for advancing negative ion source technology, particularly

H

−

ion sources, by refining ion transport and confinement strategies.
A random decomposition method for chaotic sequences to improve the security of image encryption
Authors: Xiao Qiumei , Yu Wenxin
Published: February 2025

The encryption efficiency of traditional encryption algorithms is relatively low, and they are no longer suitable for encrypting digital image information with large amounts of data and strong correlation. At present, encryption methods based on chaotic sequences can meet the demand for large amounts of image encryption data. However, the randomness of the chaotic sequences used in general chaotic encryption algorithms is strongly correlated with the initial state and parameters of the chaotic system, which may result in low complexity and chaotic degradation of the output sequence of the chaotic system. This paper proposes a sequence random decomposition algorithm that decomposes low complexity sequences into a large number of high complexity random decomposition sequences from the perspective of sequence decomposition. By using the sequence random decomposition algorithm to process the chaotic sequence output by the chaotic system, the random decomposition sequences with higher spectral entropy complexity were obtained. Finally, an image encryption communication system based on random sequence decomposition is constructed by combining random sequence decomposition with chaotic system. The security analysis of the image encryption communication system is carried out, and it is verified that the proposed system has better encryption performance.
Graphene optical phase modulators through quasi-bound states in the continuum
Authors: Wang Hong , Wang Jieliang , Sun Boyuan , Song Ge , Men Dandan
Published: February 2025

Spatial light modulators (SLMs), which control the amplitude, phase, and polarization of light are the core components of many optical applications. Integrating graphene with dielectric metasurfaces supporting quasi-bound states in the continuum (quasi-BIC) offers potential advantages of low energy consumption, high speed, and small size of modulators. In this paper, we propose a modulator with a 2π phase shift, by transforming the coupled system from an underdamped resonator to an overdamped one by tuning the Fermi level of graphene through simulation. Additionally, we demonstrate the application of this device in one-bit programmed holography. The proposed modulator with its combined advantages of low power consumption and compact footprint, enables fruitful applications in holographic display, beam steering in light detection, ranging and biomedical imaging systems.
Analysis of a novel 3D autonomous system with intermittent chaos and its finite-time sliding mode synchronization
Authors: Yan Shaohui , Wu Xinyu , Zhang Hanbing , Jiang Jiawei , Zhang Jiandong , Guo Zihao
Published: February 2025

By improving the Bao system, a new 3D autonomous chaotic system is obtained. This paper analyses the phase diagram, bifurcation diagram, Lyapunov exponents, and complexity of the new system, revealing its rich dynamical behaviours. The new system exhibits extreme sensitivity to both parameters and initial conditions. Specifically, the phenomena of transient chaos, intermittent chaos, and coexisting attractor are discussed. Furthermore, to verify the practical feasibility of the new system, an analogue simulation circuit is designed, and the digital circuit is implemented using the Field Programmable Gate Array (FPGA). The experimental results show that the simulation and actual circuit implementation are consistent with the theoretical analysis, verifying the system’s stability and feasibility. Finally, an improved nonlinear integral Sliding Mode (SM) surface is constructed, and a SM controller is designed based on this surface to achieve synchronous control of chaotic systems with external perturbations. The simulation results align with the theoretical analysis, demonstrating that the proposed SM control method has good control performance and is well-suited for practical applications.
Dual-domain image encryption scheme based on fractional wavelet transform and hyperchaotic system
Authors: Liu Ziyun , Li Chunmeng , Zhang Chenyang , Yang Xiaozhong
Published: February 2025

This paper presents a novel dual-domain encryption scheme that integrates hyperchaotic system with the fractional wavelet transform (FRWT). To enhance the security of the algorithm, a new two-dimensional cosine-coupled Logistic-Cubic mapping (2D-CLCM) is developed, which exhibits hyperchaotic characteristics across an extensive parameter range. By utilizing keys generated from plaintext-related SHA algorithm, the 2D-CLCM produces highly sensitive chaotic sequences for the encryption process. Improved scrambling and diffusion methods based on the FRWT are also proposed. Initially, a folded coding process is designed in the spatial domain, which decomposes the plaintext image into two smaller complex-valued images through pixel resampling and diagonal phase encoding. This process scrambles the image while reducing the computational load for subsequent stages. Next, the random phase mask is applied to perform FRWT decomposition on the complex-valued images. This step converts the images into the fractional wavelet domain, which helps in sparsifying and obscuring their pixel values. Finally, the proposed distribution-based diffusion method employs a uniform random matrix to conceal the distribution characteristics of the images’ fractional wavelet coefficients, ultimately reconstructing the images into an encrypted form. Security analysis results demonstrate that the ciphertext images effectively resist various attacks. Compared to other methods in the experiment, the proposed algorithm excels in both security and efficiency.
Assessment of battery energy storage uses in microgrid operation under varied demand uncertainties through a machine learning based stochastic optimization approach
Authors: Patty Sukriti , Malakar Tanmoy
Published: February 2025

The sustainability of Microgrid (MG) relies on improving reliability through effective energy management strategies. However, the presence of volatile Renewable Energy Sources (RES) and uncertain energy demand pose serious challenges. Although integrating Battery Energy Storage Systems (BES) is a promising solution, the utilization of BES under uncertain operating conditions remains underexplored. Given this, the present study aims to investigate BES operational behavior while minimizing the overall MG operation cost under varied operating conditions. Also, it evaluates the impact of prediction accuracy on overall MG operation costs and DER operation. Here, a Machine Learning (ML) -based Gaussian Process Regression (GPR) is utilized for predicting the volatile RES power output while the uncertain energy demand is estimated with Monte Carlo Simulation (MCS).The problem is formulated as a cost-minimization problem and solved using the physics-based Artificial Electric Field Algorithm (AEFA) at different MG operating conditions. Results show that MG operation cost varies approximately by 5%, with demand variations around the mean, and BES energy contribution is 1.3 times higher for the case with additional thermal load. Moreover, analysis confirms that the BES depreciation cost is faster with a lower initial State of Charge (SOC), and the proposed strategy ensures a higher SOC by day’s end. Additionally, GPR’s applicability is validated through comparison with the Gated Recurrent Unit (GRU), revealing lower BES depreciation and MG operation costs due to higher prediction accuracy. Further, a comparative analysis with 2m Point Estimate Method is done to highlight the superiority and robustness of the proposed approach.
High-speed full-color single-pixel imaging based on spatiotemporal multiplexing
Authors: Liu Chao , Wei Hong-Da , Zhao Ru , Zhao Xiao-Hui , Zhang Su-Heng
Published: February 2025

We present a full-color single-pixel imaging (SPI) system based on a commercial digital light processing (DLP) projector. By utilizing the side-by-side 3D and pulse width modulation (PWM) features of the DLP projector, the spatiotemporal multiplexing technology is effectively incorporated into the SPI system, enabling full-color image reconstruction with Hadamard basis patterns display at 120 frames per second in each color channel. By utilizing the PWM features of the DLP projector, signal tracking and identification are integrated into one color channel, thus incurring no additional temporal overhead. The experimental results demonstrated that the total imaging time for a full-color image with a resolution of

128
×
128

pixels is approximately 330 s. We provide an economical and efficient SPI solution, which is ideally suited for proof-of-principle and exhibition applications. Our solution also offers insights for other projector-based systems.
A novel multi-step methodology for stochastic simulation of streamflow time series using PcStream clustering
Authors: Balaram Shalini , Srivastav Roshan , Srinivasan K
Published: February 2025

A novel PcStream clustering-based single-site stochastic model is introduced for the simulation of daily streamflow time series. The PcStream clustering algorithm effectively manages real-time temporal data clusters and adjusts to concept drifts, enabling refined streamflow categorisation that accurately includes high values without misclassification. The methodology followed by the proposed model involves a series of steps that begin with fitting kappa and Generalized Extreme Value GEV distributions to model daily variations and extreme values, followed by clustering data using the PcStream algorithm. A Markov chain model regenerates cluster series while the nearest neighbour approach fills them with historical data. Additionally, flow series are classified into rising, falling or constant phases, and then flows are simulated using parametric distribution to reproduce observed dynamics in synthetic streamflow accurately. The methodology was tested by comparing the statistics of observed and simulated flows comparing five gage stations in the Pacific Northwest basin. The results confirm that the model successfully reproduces key aspects of streamflow, including seasonal patterns, low flows, autocorrelations, and flow duration curves. It also reproduces the basic statistics on daily, monthly and annual time scales well. The proposed streamflow model demonstrated exceptional accuracy with percent bias (PBIAS) ranging from −0.41% to +0.33% across all stations. The Index of Agreement (d) values were consistently high (0.93–1.00), while MAE varied from 458 to 37,361 cfs and RMSE from 805 to 56,042 cfs, with larger errors corresponding to stations with higher mean flows. The model effectively captured both low flows (7Q10) and high flows across stations ranging from small catchments (105 sq mi) to major catchments (59,700 sq mi), handling flow ranges spanning four orders of magnitude (0.3 to 492,000 cfs). It effectively captures the nuances of streamflow pulses through explicit modelling of different flow phases. The efficacy of the proposed model is also brought out through a comparison with the hybrid Modified Continuous Time Markov Chain (MCTMC) model .
Overcoming sparse datasets with multi-task learning as applied to high entropy alloys
Authors: Debnath Arindam , Reinhart Wesley F
Published: February 2025

The design of novel High Entropy Alloys for use in high-temperature applications is an area of active interest due to their potential to provide exceptional properties compared to conventional alloys. Since the increased popularity of machine learning, an important cog in the design process has been training surrogate models on alloy properties. However, these Single-Task models are trained on individual mechanical properties and do not take advantage of the relatedness between properties. Multi-Task models can capture the interdependencies between tasks, leading to potentially more accurate predictions for all tasks. In this paper, we investigate if Multi-Task models can show improvement over Single-Task models when used for predicting the mechanical properties of these alloys. To ensure fair evaluation between the models, we apply L
0 regularization and skip connections to the models, which allows them to adjust the number of model parameters and depth for optimal performance. We find that the Multi-Task models can leverage task relationships to perform better than Single-Task models, especially for high amounts of missing data in the tasks. Furthermore, adding simple auxiliary targets can boost Multi-Task performance even further despite not being effective as input descriptors to single-task models themselves. We anticipate that the proposed strategies can achieve more accurate predictions and consequently enable better design capabilities for such data-constrained domains without incurring much additional computational cost.
Readers of the Lost Ark
Authors: McGeough Kevin M
Published: February 2025

The Ark of the Covenant is the sacred chest that God commanded Moses to build when He appeared on Mount Sinai. According to the Bible, the Israelites carried the Ark with them as they wandered in the wilderness and entered the Promised Land. After the Temple of Solomon was erected, the Ark was kept in an inner sanctum of that structure where God made His divine presence felt to the Israelites. The Old Testament is unclear about what happened to the Ark after the destruction of the Temple and offers ambiguous accounts of its function. Despite (or because of) this ambiguity, the Ark continues to hold an important place in Jewish and Christian tradition, even in its absence. It has been imagined and reimagined by various communities and is perhaps best known to nonspecialists today as the object sought by Indiana Jones in Raiders of the Lost Ark. This book explores these diverse interpretations of the Ark of the Covenant, from ancient times to the present, in forms ranging from biblical literature, theological discourse, formal artwork, popular film, children’s toys, travel souvenirs, biblical wax museums, faith-healing events, and alternative histories. It recounts stories of people who have sought to find the Ark of the Covenant and examines how the Ark takes on new meanings in Europe, North America, East Asia, Ethiopia, and the modern Middle East.