bluuebunny/crossref_metadata_2025_split huggingface dataset:
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

Abstract
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

Abstract
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

Abstract
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

Abstract

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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract

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.