bluuebunny/crossref_metadata_2025 huggingface dataset:
Effects of higher-order interactions on the evolution of cooperative behavior in hyperbolic scale-free network
Authors: Chen Kenan , Zhang Yingqing , Luo Ming , Zheng Xiaojing
Published: February 2025

This study proposes an innovative hypergraph model to explore the effects of higher-order interactions on the evolution of cooperative behavior in a hyperbolic scale-free network. By adjusting the heterogeneity coefficient and clustering coefficient of the hyperbolic scale-free network, four distinct network structures with notable differences can be obtained. We then map pairwise and three-way interactions to 2-hyperedges and 3-hyperedges, constructing a hypergraph model with higher-order interactions. Our findings reveal that when the proportion of three-way interactions exceeds a critical threshold, cooperative tendencies exhibit explosive growth, leading to a bistable phenomenon of coexisting cooperation and defection. The system's average degree significantly influences the critical mass of initial cooperators needed to maintain stable cooperative behavior. The network structure shows complex, non-linear effects on cooperation. In low-conditions, increasing heterogeneity acts to suppress the appearance of bistable phenomena, while in high clustering conditions, it acts to promote. Increasing clustering tends to suppress the appearance of bistable phenomena in both low and high heterogeneity conditions. Furthermore, the effects of heterogeneity, clustering, and noise factors on cooperation are non-monotonic, exhibiting inverted U-shaped patterns with critical transition points. These findings provide new theoretical perspectives for understanding diverse cooperation patterns in real-world scenarios and suggest the need for dynamic, context-specific approaches when designing strategies to promote cooperation.
Complex dynamics in chain HNN with parameter-relied equilibria and memristive electromagnetic induction
Authors: Qin Minghong , Lai Qiang , Wang Huangtao , Wan Zhiqiang
Published: February 2025

Investigating the dynamics of neural networks under electromagnetic induction contributes to understanding the complex electrical activity in the brain. This paper proposes a memristive chain Hopfield neural network (MCHNN) containing unidirectional synaptic connections, where a flux-controlled memristor mimics the electromagnetic induction between neurons. Under different parameters, the equilibria of MCHNN have different numbers and properties, thus producing diverse dynamics. Numerical analysis shows that there are diverse coexisting attractors, such as point attractors and periodic and chaotic attractors, which are yielded from different initial conditions. Moreover, the memristor’s internal parameter can be considered as a special signal controller. It acts on the oscillation amplitude of the neuron’s output signal, along with amplitude control and offset-boosting about the flux. By building a feasible hardware platform, the numerical analysis outcomes are supported, and the existence of the proposed MCHNN is verified. In addition, the NIST test outcomes indicate that MCHNN has good pseudo-randomness and is suitable for engineering applications.
Capillary flow simulation with the phase-field-based lattice Boltzmann solver
Authors: Thirumalaisamy R. , Kim S. , Otomo H. , Jilesen J. , Zhang R.
Published: February 2025

The phase-field-based lattice Boltzmann (LB) model has been developed to perform high fidelity multiphase flow simulations. Its ability to accurately handle high density ratio and surface tension effects is expected to be beneficial for capillary flow simulation, leading to accurate reproduction of flow patterns, such as slug flow, droplet flow, and film flow. This is critical in many engineering cases because the flow patterns significantly affect the velocity and pressure fields. In this study, on top of the LB models based on the conservative Allen–Cahn equation and the volumetric boundary conditions for the complex geometries, an optimized wettability and friction model are implemented. With these models, we conducted a set of benchmark test cases, including static and dynamic multiphase flow scenarios, such as the droplet on the curved surfaces, water-filling channel for the Lucas–Washburn law, and the critical pressure in the three-dimensional channel, an air-driven multiphase flow in the experiments. In all of these cases, the solver produces results that are consistent with both theory and experiment, even with respect to the pressure field accuracy, which has often been overlooked in many previous studies.
Deformation and fragmentation of a water drop in collision with a thin cylinder
Authors: Bazilevsky A. V. , Rozhkov A. N.
Published: February 2025

We experimentally study the collision of a falling water drop with a thin horizontal cylinder, focusing on phenomena that play a crucial role in the hydrodynamics of the collision but have not been studied previously. We address two different collision scenarios. In the first case, the drop is split into two separate parts connected by a liquid sheet oriented transversely to the cylinder. The surface tension of the sheet causes the parts to approach and merge, or the sheet breaks up and each part continues to move independently. The second scenario is realized at low collision velocities. In this case, the sheet is not formed, and the separation or merging of the drop parts is controlled by their surface tension driven deformations. We analyze the factors (drop velocity, cylinder diameter, and cylinder wettability) that govern the realizability of these scenarios. We also propose a simple criterion that predicts the transition from one scenario to the other.
Migration of magnetic microparticles through a liquid–liquid interface under an external magnetic field
Authors: Mondal Sourav , Abbasi Niki , Tsai Scott S. H. , Griffiths Ian M.
Published: February 2025

Liquid–liquid interfaces play a pivotal role in various microfluidic processes involving microparticles, including coating, dissolution, controlled release of polyelectrolytes or drugs, and self-assembly processes. In all of these cases, noninvasive techniques to manipulate the microparticle transport are essential. Magnetic manipulation offers an accessible and straightforward means of controlling the motion of magnetic particles within microfluidic devices. Magnetic microparticles are commonly used for conformal polyelectrolyte coating and drug encapsulation by passing them through a liquid–liquid interface due to their high saturation magnetization, stability, and low toxicity. In this work, we draw inspiration from the lack of studies on the behavior of magnetic particles near a liquid–liquid interface under conditions of low Reynolds numbers and high capillary action, despite its engineering relevance in microfluidic systems. We consider a canonical flow configuration in which particle motion is driven by the stagnation-point flow that is generated when two different liquids flow toward one another. We show how the operating conditions dictate whether the particle will pierce the interface and become coated or not and illustrate this via parameter-space plots. We use the results of this analysis to understand how the operating conditions influence the fraction of particles that pass through the liquid–liquid interface and are conformally coated, which may be used to guide a variety of industrial processes.
Three-phase flow induced vibration characteristics for mining riser in deep-sea natural gas hydrate extraction
Authors: Guo Xiaoqiang , Yang Juxing , Li Xinye , Liu Xiuquan , Li Yanlong
Published: February 2025

During the lifting process of deep-sea natural gas hydrate, the mining riser is susceptible to complex fatigue failure due to the combined effects of internal gas–liquid–solid three-phase flow, external ocean loads, and the large aspect ratio of itself. The gas–liquid–solid three-phase flow induced vibration (FIV) model for deep-sea hydrate extraction riser is established using finite element method and Hamiltonian principle. The simulation experimental device for nonlinear vibration of mining riser under internal and external flow excitation is developed and conducted a simulation experiment for gas–liquid–solid three-phase FIV of vertical pipes. The experimental test results are compared with the theoretical model calculation results to verify the correctness of the model. Using frequency domain and time domain analysis methods, the influences of external environmental parameters and multiphase flow parameters on the nonlinear FIV response of the riser are explored. The results showed that the vibration amplitude in the cross-flow (CF) direction of the riser is larger, and the change in internal flow parameters had a more significant impact on the CF vibration. The longitudinal vibration of the riser is composed of low-frequency high-amplitude vibration caused by gravity and platform heave, and high-frequency low-amplitude vibration induced by internal and external flow field loads. The increase in shear flow velocity will significantly increase the displacement of the riser along in-line flow direction, thereby suppressing CF vibration. The research results can be widely applied to the vibration problems caused by gas liquid solid three-phase flow in the energy collection process, which has important research significance and significant engineering value for improving the safety of operation columns.
Fluidic FitzHugh-Nagumo oscillator
Authors: Fromm Matthias , Grundmann Sven , Seifert Avraham “Avi”
Published: February 2025

Fluidic oscillators display a unique feature: from a constant input flow, they generate an output that alternates both temporally and spatially, all without the necessity for any moving components. However, there have been varying theories proposed to explain the underlying mechanisms. In this study, we provide experimental arguments that the functioning of a single-feedback loop fluidic oscillator can be effectively modeled and interpreted using the Fitzhugh-Nagumo equations. We explore the connection between the Fitzhugh-Nagumo system and the momentum equations, as well as a fluid capacitance. Our findings reveal a complex interplay of secondary flows within the oscillator, which appears to facilitate the self-sustained oscillation.
Comparative analysis on the normal vector features of the conventional inverted flame and ultra-lean residual flame
Authors: Yang Wenquan , Wan Jianlong
Published: February 2025

Residual behavior often appears near the flammability limit for lean premixed flames. Insight into it is beneficial to improve the lean premixed combustion performance further. Identifying the normal vector of the residual flame front plays a key role in revealing its structure and evaluating the weights of some main effects quantitatively. For the present residual flame with a high curvature, as the polynomial curve fitting method causes a noticeable error due to the Runge phenomenon, a non-equidistant central difference method is proposed. Subsequently, the normal vectors of the bluff-body stabilized residual flame and conventional inverted flame of 40%H2–60%CH4–air are obtained accurately and their differences are compared quantitatively. It is found that there are significant differences in the normal vector and flow structure features between the conventional inverted flame and residual flame behaviors. The normal vector of the conventional inverted flame always points at the upstream direction. However, the normal vector at the middle section of the residual flame front is almost parallel to the horizontal direction, and it points at the downstream direction next to the residual flame tip. In addition, the flow structure around the residual flame front is revealed. This is the first time the normal vector of the residual flame has been accurately visualized and the difference in normal vector feature between the residual flame and conventional inverted flame revealed. This work provides a universal and accurate method to identify the normal vector of the flame front and new insight into residual flame dynamics.
Sloshing in tanks with an internal horizontal perforated plate based on an immersed-boundary generalized harmonic polynomial cell method
Authors: Yu Xueying , Wu Yuxiao , Wang Qingfeng , Li Qiyan
Published: February 2025

This study explores the challenges of sloshing in rectangular tanks, focusing on the use of an internal perforated plate to mitigate its adverse effects. A novel numerical flow model is developed to examine sloshing dynamics involving these plates. The Navier–Stokes equations are solved using the projection method on a Cartesian grid, while a newly adapted immersed-boundary generalized harmonic polynomial cell method is employed to solve the Poisson equation within this framework. The solver's accuracy is validated through comparisons of free surface profiles, pressure, and velocity data with experimental results, demonstrating reliable performance in simulating sloshing phenomena. The research emphasizes optimizing the placement of perforated plates within the tank to maximize energy dissipation and minimize structural forces. The findings demonstrate that strategically positioning perforated plates effectively mitigates violent resonant sloshing caused by horizontal excitation, highlighting the critical influence of plate location on tank performance. These insights are instrumental for enhancing tank stability in engineering applications such as liquid cargo transportation, offshore storage facilities, and fuel tank design for aerospace and automotive industries, where precise control of fluid dynamics is essential for safety and operational efficiency.
CO
2
potentiates echinocandin efficacy during invasive candidiasis therapy via dephosphorylation of Hsp90 by Ptc2 in condensates
Authors: Zhang Mao , Zhao Youzhi , Cui Hao , Huang Wenqiang , Xiong Kang , Yang Shan , Duan Yuanyuan , He Yong , Yang Lianjuan , Su Chang , Lu Yang
Published: February 2025

Carbon dioxide is a signaling cue critical for fungal pathogenesis. Ptc2, a type 2C protein phosphatase (PP2C), serves as a conserved CO
2
sensor in fungi. By combining phosphoproteomic and biochemical assays, we identified Hsp90 as a direct target of Ptc2 at host CO
2
concentrations and Ssb1 as a Ptc2 target protein regardless of CO
2
levels in
Candida albicans
, the most prevalent human fungal pathogen. Ptc2 forms reversible condensates at elevated CO
2
, which enables the recruitment of Hsp90, but not Ssb1, to condensates, allowing efficient dephosphorylation. This process confers an enhanced susceptibility to caspofungin in vitro and during in vivo infection therapy. Importantly, we demonstrate this phenomenon in non-albicans
Candida
species. Sequential passages of
C. albicans
in mice with caspofungin treatment readily induce in vivo drug tolerance, causing therapeutic failure. These evolved strains display increased resistance to caspofungin under host concentrations of CO
2
but remain susceptible in air. Collectively, our study reveals a profound impact of host concentrations of CO
2
on antifungal drug susceptibility and connects this phenotype to therapeutic outcomes and highlights condensate formation as an efficient means that enables selective recruitment of substrates for certain signaling events.
Revisiting the dynamics of a charged spinning body in curved spacetime
Authors: Andrzejewski K
Published: February 2025

We analyse the motion of the spinning body (in the pole–dipole approximation) in the gravitational and electromagnetic fields described by the Mathisson–Papapetrou–Dixon–Souriau equations. First, we define a novel spin condition for the body with the magnetic dipole moment proportional to spin, which generalizes the one proposed by Ohashi–Kyrian–Semerák for gravity. As a result, we get the whole family of charged spinning particle models in the curved spacetime with remarkably simple dynamics (momentum and velocity are parallel). Applying the reparametrization procedure, for a specific dipole moment, we obtain equations of motion with constant mass and gyromagnetic factor. Next, we show that these equations follow from an effective Hamiltonian formalism, previously interpreted as a classical model of the charged Dirac particle.
Stability analysis of inverse problems for coupled magnetic Schrödinger equations
Authors: Hamrouni Mohamed , Khenissi Moez , Soccorsi Éric
Published: February 2025

We consider the inverse coefficient problem of simultaneously determining the space dependent electromagnetic potential, the zero-th order coupling term and the first order coupling vector of a two-state Schrödinger equation in a bounded domain of

R

d

,

d
⩾
1

, from finitely many partial boundary measurements of the solution. We prove that these

3
d
+
3

unknown scalar coefficients can be Hölder stably retrieved by d + 2 times suitably changing the initial condition attached at the system.
Time-frequency information-based variational mode decomposition and its application in prediction models
Authors: Gu Ziwen , Wang Zijian , Shen Yatao , Huang Chun , Jiang Yaqun , Li Peng
Published: February 2025

In the field of power systems, the variational mode decomposition (VMD) algorithm is widely utilized in the data preprocessing stage of prediction models for wind power, photovoltaic power, load, and other related areas. However, if the two key parameters of VMD—mode number and penalty factor—are not properly set, the decomposition results may suffer from mode mixing and mode repetition. Moreover, the boundary effect issue in VMD causes numerical oscillations at the boundaries of the decomposition results, thereby leading to numerical distortion. To address these problems, this paper proposes a novel time-frequency information-based variational mode decomposition (TFI-VMD) algorithm. Firstly, TFI-VMD clusters the time-frequency information of the data using an improved density peak clustering algorithm, with the number of cluster centers serving as the mode numbers in VMD. Then, by minimizing the mode mixing energy, the optimal value for the penalty factor is determined. Finally, TFI-VMD uses decomposition results free from boundary effects to train the broad learning neural network, thereby correcting the decomposition data that contain boundary effects. Case study results indicate that the proposed algorithm can effectively identify the parameters in the original VMD and address the boundary effect issues in the decomposition results.
A method for improving calibration accuracy of the inertial navigation system based on gravity disturbance compensation
Authors: Guo Yusheng , Hu Huawei , Wang Wanzheng , Li Yan , Li Qihang
Published: February 2025

Accurate calibration of the inertial navigation system (INS) is essential for enhancing its performance. However, gravity disturbance significantly impacts the calibration accuracy of the INS, directly reducing the navigation performance of the high-precision INS. This paper investigates a method for further improving the calibration accuracy of the INS through compensating gravity disturbance. A full-parameter error model incorporating gravity disturbance for INS is established, the impact of gravity disturbance on INS calibration accuracy is analyzed, and gravity disturbance compensation method is proposed. Finally, the 19-position calibration experiment, the static-based navigation experiment and vehicle experiment are carried out to validate the effectiveness of the proposed method. Experiment results indicate that a 20″ meridian vertical deviation introduces a calibration error of 0.001° h−1 in the X-axis and Y-axis gyro drift, a 10 mGal gravity anomaly results in a 10 ppm calibration error in the three-axis accelerometer scale factor, while the impact of the prime vertical deviation on the INS calibration accuracy can be ignored. After compensating gravity disturbance during the INS calibration process, the 60 min static navigation accuracy improved from RMS (root mean square) 332.81 m to 272.80 m and the 70 min dynamic navigation accuracy improved from RMS 1371.8 m to 1067.2 m.
Unseen rail damage diagnosis framework using mechanism-embedded generative network
Authors: Guo Zhibin , Li Weicheng , Xie Jingsong , Wang Tiantian , Niu Buzhao , Yang Jinsong
Published: February 2025

Rail damage can pose a tremendous hazard for high-speed trains, making damage diagnosis critical in the field of engineering. Currently, deep learning enables an end-to-end approach for rail damage diagnosis. However, the training and test data in real applications are often out of distribution, and the test data may even represent fault categories that were previously unseen. To address this situation, an unseen damage diagnosis framework (UDDF) that effectively embeds the mechanism damage features from the simulation signals of all possible damage categories has been proposed. In particular, the mechanism-embedded generative adversarial networks in the UDDF utilize a hierarchical embedding technique to ensure the stability of the mechanism embedding process. In addition, a k-means clustering discriminator uses an unsupervised method to guarantee the minimum intra-category sample spacing of the generated unseen categories. After the generation of all types of damage categories, the generated and existing original data are included as a new dataset for the training of a diagnostic model. The trained diagnostic model can perform classification tasks without acquiring all types of damage signals in real situations. Finally, the effectiveness of our proposed diagnostic framework is validated through comparative and ablation studies on a dataset that contains finite element simulation and experimental data of ultrasonic guided wave signals with damage at different locations and depths of rails.
Demonstration of ultra-high dose rate electron irradiation at FLASHlab@PITZ
Authors: Li X-K , Amirkhanyan Z , Grebinyk A , Gross M , Komar Y , Riemer F , Asoyan A , Boonpornprasert P , Borchert P , Davtyan H , Dmytriiev D , Frohme M , Hoffmann A , Krasilnikov M , Loisch G , Lotfi Z , Müller F , Schmitz M , Obier F , Oppelt A , Philipp S , Richard C , Vashchenko G , Villani D , Worm S , Stephan F
Published: February 2025

Objective. The photo injector test facility at DESY in Zeuthen (PITZ) is building up an R&D platform, known as FLASH
lab
@PITZ, for systematically studying the FLASH effect in cancer treatment with its high-brightness electron beams, which can provide a uniquely large dose parameter range for radiation experiments. In this paper, we demonstrate the capabilities by experiments with a reduced parameter range on a startup beamline and study the potential performance of the full beamline by simulations. Approach. To measure the dose, Gafchromic films are installed both in front of and after the samples; Monte Carlo simulations are conducted to predict the dose distribution during beam preparation and help understand the dose distribution inside the sample. Plasmid DNA is irradiated under various doses at conventional and ultra-high dose rate (UHDR) to study the DNA damage by radiations. Start-to-end simulations are performed to verify the performance of the full beamline. Main results. On the startup beamline, reproducible irradiation has been established with optimized electron beams and the delivered dose distributions have been measured with Gafchromic films and compared to FLUKA simulations. The functionality of this setup has been further demonstrated in biochemical experiments at conventional dose rate of 0.05 Gy s−1 and UHDR of several 105 Gy s−1 and a varying dose up to 60 Gy, with the UHDR experiments finished within a single RF pulse (less than 1 millisecond); the observed conformation yields of the irradiated plasmid DNA revealed its dose-dependent radiation damage. The upgrade to the full FLASH
lab
@PITZ beamline is justified by simulations with homogeneous radiation fields generated by both pencil beam scanning and scattering beams. Significance. With the demonstration of UHDR irradiation and the simulated performance of the new beamline, FLASH
lab
@PITZ will serve as a powerful platform for studying the FLASH effects in cancer treatment.
Energy boosting and scaling of self-guided hybrid laser-plasma wakefield acceleration in a single uniform plasma
Authors: Chang Xinyuan , Zeng Ming , Wang Jia , Li Dazhang
Published: February 2025

In laser-driven plasma wakefield acceleration (LWFA), laser pump depletion and electron dephasing are the major constraints of the electron energy gain. Hybrid laser-PWFA, which uses the laser-accelerated electron beam to drive a beam-driven plasma wakefield in separated stages, has been proposed to increase the beam brightness. However, the overall electron energy gain in hybrid acceleration is even lower than single-stage laser acceleration. In this paper, we report the observation of the energy boosting effect of the hybrid acceleration in single uniform plasmas through a series of particle-in-cell simulations. The self-injected electron beam from the laser-driven wakefield automatically moves forward to drive the beam-driven wakefield after laser depletion. The electrons at the beam tail are then accelerated by the beam-driven plasma wakefield, and the energy gain is at least doubled compared to previous single-stage experiments with the same laser energy. We also propose the scaling of the electron energy gain and the acceleration distance with the laser energy. For example, with a 9.1 J energy laser pulse and a 3.5 cm long plasma of

1.6
×

10

18

 

cm

−
3

density, one can produce a quasi-monoenergetic electron beam at 3.5 GeV energy with 23 pC charge.
Effect of 90 MeV oxygen and carbon ion irradiation on structural, optical and dielectric properties of ZnO embedded in PVA matrix
Authors: Behera A , Khan S A , Singh F , Kumar Prabhukrupa C , Naik R , Biswal R , Mallick P
Published: February 2025

ZnO nanoparticles embedded in polyvinyl alcohol (PVA) matrix have been synthesized by solution casting method. The samples were irradiated with 90 MeV O7+ and C6+ ions to different fluences. The structural characterization indicated the semicrystalline nature of PVA along with the formation of ZnO/PVA nanocomposite. The reduction in the peak intensity of the sample upon ion irradiation is due to the chain scissions as well as creation of free-radicals. The crystallite size of the sample increased from ∼5.57 to 6.47 nm with increasing fluence. The morphological study reveals the uniform distribution of the ZnO nanoparticles inside the PVA matrix upon ion irradiation. UV–visible absorption study indicated the presence of both direct and indirect band gap transitions in the sample. Although, the direct band gap of the sample decreased slightly at the initial ion fluence, but the same increased from ∼2.95 to 3.82 eV and ∼2.95 to 3.73 eV with increasing ion fluence from 0 to 1 × 1012 ion cm−2, for 90 MeV O7+ and C6+ ion irradiation, respectively. Similar features have also evidenced for the indirect band gap transition. This could possibly be due to the chain scission or cross linking of polymer. The reduction in both real part and imaginary part of impedance with the progress of frequency along with the shifting of impedance peak upon ion irradiation demonstrates the presence of a relaxation process in ZnO/PVA nanocomposite. Both the ion irradiation led to the alteration in dielectric constant and loss, electric modulus, impedance and AC electrical conductivity of the sample. The increase of electrical conductivity of the ZnO/PVA nanocomposite at certain ion fluences of irradiation could be due to uniform dispersion of ZnO inside the PVA matrix as well as the increase of free radicals, unsaturation, loss volatile fragments, etc due to breaking of chemical bonds.
Three-dimensional boundary turbulence simulations of a RFX-mod plasma in the presence of voltage biasing
Authors: Giacomin M. , Vianello N. , Cavazzana R. , Molisani S. , Spolaore M. , Zuin M.
Published: February 2025

Three-dimensional turbulence simulations of a RFX-mod diverted plasma are performed in the presence of a biasing electrode. The simulations show a strong suppression of turbulent transport caused by the induced

E

×

B

flow shear, which leads to the formation of an edge transport barrier with a pedestal-like structure, in qualitative agreement with RFX-mod experiments. The strong

E

×

B

flow shear turbulence suppression with edge voltage biasing is also observed in the proximity of the density limit crossing, suggesting that edge voltage biasing may allow for larger maximum achievable density values. By leveraging the simulation results, the theoretical scaling law of the edge pressure gradient length derived in (Giacomin and Ricci 2020 J. Plasma Phys.
86 5) is extended here to account for the

E

×

B

flow shear turbulence suppression caused by voltage biasing. The improved theoretical scaling with typical RFX-mod shearing rate values predicts a factor of two increase of the pressure gradient at the separatrix, which is comparable to RFX-mod experiments in the presence of voltage biasing. The implications of the flow shear turbulence suppression due to voltage biasing on the density limit in RFX-mod are also discussed.
Hyperuniformity in mass transport processes with center-of-mass conservation: some exact results
Authors: Hazra Animesh , Mukherjee Anirban , Pradhan Punyabrata
Published: February 2025

We characterize the steady-state static and dynamic properties in a broad class of mass transport processes on a periodic hypercubic lattice of volume Ld
, where both the mass and center-of-mass (CoM) remain conserved and the detailed balance is violated in the bulk; we specifically consider the models in the d = 1 and 2 dimensions. Using a microscopic approach, we exactly determine the decay (or growth) exponents for various dynamic and static correlation functions. We show that despite constrained dynamics due to CoM conservation (CoMC), the density relaxation is indeed diffusive. However, the fluctuation properties are strikingly different from those in diffusive systems with a single (mass) conservation law as dynamic and static fluctuations are more suppressed in systems with CoMC, resulting in an extreme (‘class-I’) hyperuniformity in certain cases. In the thermodynamic limit, the steady-state variance

⟨

Q

2

(
T
)

⟩
c

of time-integrated bond current

Q

(
T
)

across a bond in the time interval T exhibits the following long-time behavior:

⟨

Q

2

(
T
)

⟩
c

≃

A
1

T
+

A
2

+

A
3

T

−
d

/

2

. The exponents governing the small-frequency behavior of the power spectra

S
J

(
f
)
≃

A
1

+

Const
.

f

ψ
J

for bond current are exactly determined as

ψ
J

=
3

/

2

and 2 in d = 1 and 2 dimensions, respectively; the corresponding unequal-time current-current correlation function decay as

t

−
5

/

2

and t
−3 as a function of time t in d = 1 and 2 dimensions, respectively. Remarkably, depending on the dimensions and microscopic details, the prefactor A
1 can vanish, causing the variance to eventually saturate, implying a class-I ‘dynamic hyperuniformity’. We also compute the static structure factor S(q), which varies as the square of the wave number q in the small-q limit, i.e.

S
(
q
)
∼

q
2

, implying a class-I spatial hyperuniformity.
A wing-flapping robot with a bio-inspired folding mechanism derived from the beetle’s hind wing
Authors: Li Xin , Zheng Yu , Shen Huan
Published: February 2025

When the beetle lands on the target, the hind wings fold regularly to form smaller wing packages and are hidden on the ventral side of the elytra due to the interaction between the elytra and abdomen. Its complex folding pattern is attributed to the flexibility of the hind wings, the super-elasticity of the folding joints, and the special geometric morphology of the veins. The corrugation and folding pattern of the hind wings can provide new insights for the design of folding anti-collision mechanisms and the improvement of aerodynamic performance of ornithopter. This paper first proposes a beetle-type ornithopter with foldable wings based on the folding mechanism and kinematic characteristics of the beetle’s hind wings. Subsequently, a series of numerical simulations were conducted on flapping wing robot to explore its flapping kinematics, folding stability, structural stiffness. Finally, the force generation of flapping wings was tested on the fabricated prototype.
A recent study of natural hydrogels: improving mechanical properties for biomedical applications
Authors: Shukla Atharva , Syaifie Putri Hawa , Jaya Syahnanda , Rochman Nurul Taufiqu , Jauhar Muhammad Miftah , Mardliyati Etik
Published: February 2025

Natural polymer-based hydrogels, generally composed of hydrophilic polymers capable of absorbing large amounts of water, have garnered attention for biomedical applications because of their biocompatibility, biodegradability, and eco-friendliness. Natural polymer-based hydrogels derived from alginate, starch, cellulose, and chitosan are particularly valuable in fields such as drug delivery, wound dressing, and tissue engineering. However, compared with synthetic hydrogels, their poor mechanical properties limit their use in load-bearing applications. This review explores recent advancements in the enhancement of the mechanical strength of natural hydrogels while maintaining their biocompatibility for biomedical applications. Strategies such as chemical modification, blending with stronger materials, and optimized cross-linking are discussed. By improving their mechanical resilience, natural hydrogels can become more suitable for demanding biomedical applications, like tissue scaffolding and cartilage repair. Additionally, this review identifies the ongoing challenges and future directions for maximizing the potential of natural polymer-based hydrogels in advanced medical therapies.
Development and characterization of milled carbon fiber-reinforced polypropylene filaments for fused deposition modeling: mechanical performance, moisture absorption, and morphological insights
Authors: Abdelhafeez Alhassan , Abdelrhman Yasser , Soliman M-Emad S , Ahmed Shemy M
Published: February 2025

This study explores the production, characterization, and performance evaluation of carbon fiber-reinforced polymer (CFRP) composite filaments designed for fused deposition modeling (FDM) applications. The primary objective was to investigate the influence of milled carbon fiber (MCF) content on the mechanical, moisture absorption, and morphological properties of polypropylene (PP)-based composites. Composite filaments were produced by blending micro-sized MCFs with PP granules, followed by a two-step extrusion process to create filaments with varying MCF contents (1–24 wt%). Test specimens were fabricated using 3D printing to evaluate the performance of the composite materials. The results demonstrated a significant enhancement in mechanical properties compared to neat PP. The composite with 9.09 wt% MCF achieved optimal performance, exhibiting increases in tensile and flexural strengths by 74% and 99%, respectively, relative to neat PP. However, higher MCF contents (16 and 24 wt%) led to reduced mechanical properties due to insufficient fiber-matrix adhesion, resulting in fiber pull-out. Moisture absorption studies revealed that the inclusion of MCFs increased the water uptake of the composites, with higher fiber concentrations correlating to greater moisture absorption. These findings underline the potential of MCF-reinforced PP composites for applications requiring improved mechanical performance, such as lightweight structural components. The study identifies an optimal fiber content of 9.09 wt% for maximizing strength while minimizing moisture-related trade-offs. Future efforts could focus on enhancing fiber-matrix bonding to improve performance at higher fiber concentrations.
Multi-directional reconfigurable ultra-low frequency vibration energy harvester
Authors: Zhang Zhenming , Liu Libo , Qiu Jing , Nong Aocheng , Li Mingyu , Xinjie Zeng , Xiong Chao , Cao Cong , Huang Shuanglong
Published: February 2025

Converting vibrational energy from the environment into electrical energy more efficiently has been one of the key research topics of this century. The majority of vibrations observed in the natural environment occur at low frequencies and are not fixed in direction. This work presents a design of a piezoelectric vibration energy harvester supported by an elastic rod (R-PVEH), which allows for multidirectional responses to capture vibrational energy. The distinctive configuration of the structure allows for a reduction in vibration attenuation in the presence of low-frequency, unstable excitation. Further, in instances where a complex vibration source is present, multiple units can be reconfigurable to achieve a higher and more stable voltage output. The device can output an average voltage of 15.2 V in response to horizontal low-frequency vibrations of 0.3 Hz, representing a 193.3% increase compared to the conventional cantilever beam structure. The device is expected to be employed in multidirectional low-frequency scenarios such as ocean wave energy harvesting.
Estimating the carbon footprint of post-war reconstruction: toward a ‘greener’ recovery of Ukraine
Authors: Kobayakawa Toru
Published: February 2025

The substantial investment required for Ukraine’s post-war reconstruction will likely result in significant carbon dioxide (CO2) emissions. This study quantitatively estimates the carbon footprint (CF) of the reconstruction process using an environmentally extended multi-region input–output analysis. Results indicate that the projected total CF over a ten-year reconstruction period will amount to 741 Mt-CO2 exceeding Ukraine’s pre-war annual territorial emissions by more than fourfold. With the construction industry accounting for 77% of total emissions, there is an urgent need to modernize the sector and enhance its efficiency to achieve significant emission reductions. Key mitigation strategies include modernizing construction processes and implementing large-scale recycling of construction materials like concrete and steel. Beyond reducing emissions, these measures have the potential to foster industrial innovation, generate employment, and align Ukraine’s development trajectory with European Union environmental standards. This research highlights the necessity of integrating sustainability into Ukraine’s recovery pathway to ensure a greener, more sustainable future for Ukraine.
The trajectories of depression and multimorbidity affect the patterns of cognitive decline: a prospective cohort study
Authors: Cui Kai , Zhao Ronghui , Ding Ning , Xu Xiaonuo , Gu Weiqi , He Jing , Ge Xiaoyan
Published: February 2025

Background

The course of decline of global cognitive function is heterogeneous, with patterns varying among individuals. The influence of depression, functional limitations and multimorbidity on patterns of decline of global cognitive function among community-dwelling older adults remains unknown. This study examines the relationship between these health conditions and the course of decline of global cognitive function.

Methods

A prospective cohort of 4032 participants aged ≥60 years at baseline was constructed using the Health and Retirement Study (2006–20), through surveys every 2 years. Information on depression, functional limitations and multimorbidity was collected from 2006 to 2012. Data on cognitive function were collected between 2012 and 2020. Latent class trajectory models were used to identify the latent trajectories. Multinomial logistic regression models were employed to analyse the association between the latent trajectories and cognitive function.

Results

Different trajectories were identified for depression (low-stable, decline-rise and rise-decline), activities of daily living (ADLs; low-stable, decline-rise and rise-decline), instrumental activities of daily living (IADLs; low-stable and rise), and multimorbidity (stable and rise). Three trajectories of cognitive decline were identified: high stable, modest and later steep. The trajectories of depression (for decline-rise: OR: 1.47, 95% CI: 1.11–1.95; for rise-decline: OR: 1.72, 95% CI: 1.24–2.38) and multimorbidity (OR: 1.63, 95% CI: 1.08–2.46) increased the risk of cognitive decline.

Conclusion

The trajectories of depression and multimorbidity affect cognitive decline patterns. Longitudinal surveillance of health conditions can be used to identify different patterns of cognitive decline and predict faster cognitive decline.
Religious service attendance is protective against the diseases of despair: evidence from regression, sibling-fixed effects, and instrumental variables analyses
Authors: Lebenbaum Michael , Fletcher Jason
Published: February 2025

It is unclear whether the large secular decline in religiosity has contributed to the dramatic rise in the “deaths of despair.” We contribute to the recent epidemiologic literature estimating more rigorous effects of religiosity on health by examining the association between religiosity and the diseases of despair via regression, sibling fixed effects (SFE) analyses, instrumental variable (IV), and cross-lag analyses. We used the US Add Health sample when respondents were in Waves (W) 3-5 (ages: 18-43). We measured religious service attendance and a composite outcome consisting of painkiller abuse, past-year suicidal ideation, and weekly binge drinking. We estimated linear probability models, SFE, IV, and cross-lag models. Confounders included parental socio-demographics, community/school characteristics, and individual socio-demographics. Greater religious service attendance was negatively associated with the composite outcome in the pooled sample (β =-0.031; p < .5) and at each wave (W3 β=-0.025; W4 β=-0.040; W5 β=-0.028; all p < .5). Conclusions were similar in SFE models (W3-5 pooled β=-0.013), IV models (W4 β=-0.081; W3-5 pooled β=-0.064, all p < .5, F>100, and overidentification p > .10) and cross-lag models (W3-5 pooled β=-0.023, p < .5). The consistent results across models suggests that the large decline in religious service attendance likely contributed to the rise in the deaths of despair.
Montefiore Einstein Robust Geriatric Normative Project: Robust Age- and Regression-Based Demographic Norms for the Repeatable Battery for Neuropsychological Status and Select Neuropsychological Tests in Older Adults
Authors: Freilich Bryan M , Scharaga Elyssa , Holtzer Roee
Published: February 2025

Objective

This study introduces the Montefiore Einstein Robust Geriatric Normative Project (MERGER-NP), which provides robust normative data for older adults on the Repeatable Battery for Neuropsychological Status (RBANS) and other select neuropsychological tests.

Method

Age-stratified and regression-based demographic norms were derived from a robust sample of older adults (n = 420, mean age = 75.55, 68% female). The study included assessments using the RBANS, Trail Making Test, Digit-Symbol Coding, a 15-item version of the Boston Naming Test, and verbal fluency tests, along with word reading measures. Regression-based norms were generated by analyzing predictors of test performance, integrating demographic variables and measurable social determinants of health (SDOH), specifically word reading ability and occupational attainment.

Results

Normative data include convenient look-up tables for the RBANS and other tests. Findings indicate that word reading measures significantly predict neuropsychological performance, accounting for up to 41% of the variance when included with demographic variables. Notably, our analyses revealed that race often did not contribute unique variance when controlling for reading ability. Additionally, occupation was identified as a significant predictor of test performance, with Job Zone scores retained in approximately 60% of regression models.

Conclusions

The MERGER-NP enhances existing normative data by integrating robust norms with regression-based methods, facilitating more precise assessments for older adults. The findings underscore the utility of including SDOH such as reading ability and occupation into normative approaches, with important implications for improving diagnostic accuracy and patient care in clinical settings. Future research should explore the generalizability of these norms to more diverse populations.
The DNA binding of plant-specific GROWTH-REGULATING FACTOR transcription factors is stabilized by GRF-INTERACTING FACTOR coactivators
Authors: Nosaki Shohei , Ohtsuka Masae
Published: February 2025

The plant-specific GROWTH-REGULATING FACTOR (GRF) transcription factor family proteins play crucial role in regulating diverse aspects of plant life. The transcriptional activity of GRFs is known to be enhanced through direct interaction with the GRF-INTERACTING FACTOR (GIF) coactivators. However, it remains unclear how the binding to GIF affects the biochemical ability of GRFs. Herein, we present evidence that GIFs also stabilize the DNA binding of GRFs. A combination of biochemical experiments and AlphaFold-predicted structural models suggests that the GIF-binding domain in GRFs may partially restrict their own DNA binding through direct interaction with the DNA-binding domain. These findings deepen our understanding of the GRF:GIF module in plant regulation and provide a basis for strategies to manipulate this module for agricultural and biotechnological applications.
DeepPFP: a multi-task-aware architecture for protein function prediction
Authors: Wang Han , Ren Zilin , Sun Jinghong , Chen Yongbing , Bo Xiaochen , Xue JiGuo , Gao Jingyang , Ni Ming
Published: February 2025

Deriving protein function from protein sequences poses a significant challenge due to the intricate relationship between sequence and function. Deep learning has made remarkable strides in predicting sequence-function relationships. However, models tailored for specific tasks or protein types encounter difficulties when using transfer learning across domains. This is attributed to the fact that protein function relies heavily on structural characteristics rather than mere sequence information. Consequently, there is a pressing need for a model capable of capturing shared features among diverse sequence-function mapping tasks to address the generalization issue. In this study, we explore the potential of Model-Agnostic Meta-Learning combined with a protein language model called Evolutionary Scale Modeling to tackle this challenge. Our approach involves training the architecture on five out-domain deep mutational scanning (DMS) datasets and evaluating its performance across four key dimensions. Our findings demonstrate that the proposed architecture exhibits satisfactory performance in terms of generalization and employs an effective few-shot learning strategy. To explain further, Compared to the best results, the Pearson’s correlation coefficient (PCC) in the final stage increased by ~0.31%. Furthermore, we leverage the trained architecture to predict binding affinity scores of the DMS dataset of SARS-CoV-2 using transfer learning. Notably, training on a subset of the Ube4b dataset with 500 samples resulted in a notable improvement of 0.11 in the PCC. These results underscore the potential of our conceptual architecture as a promising methodology for multi-task protein function prediction.
Fever and Rash in a Traveler
Authors: Reddy Maithri , Curtin John
Published: February 2025

This Photo Quiz article presents a case of acute dengue fever in an American traveler returning from Puerto Rico. In addition to the relevant epidemiologic and medical history, a key finding that allowed clinical diagnosis of the infection was the characteristic rash (“isles of white in a sea of red”) that our patient manifested. This physical exam finding was documented and is featured prominently in the article. Despite the fact that initial testing for dengue virus infection was negative, the characteristic exposures, incubation period, and clinical syndrome our patient presented with allowed us to provide directed care for the most likely illness. Our clinical diagnosis was later confirmed on convalescent serologic testing. We use this case to illustrate and discuss highlights of diagnosis, treatment, and prevention of both non-severe and severe dengue virus infection.