bluuebunny/crossref_metadata_2025_split huggingface dataset:
Feasibility study on time-domain extraction method for zero-frequency pulse signal of nonlinear Lamb waves and early fatigue damage detection
Authors: Tang Weili , Wang Zhijiao , Li Xinxin , Wei Yuming , Ceng Xiaoyi , Bi Yiwen 
Published: October 2024

Abstract
The early damage can be effectively and sensitively characterized by zero-frequency component of nonlinear Lamb waves, which exhibits some more favorable properties than higher harmonics. However, in the process of extracting zero-frequency component, it is challenging to determine the time period that needs to be analyzed. In this study, the residual component obtained from the empirical mode decomposition of nonlinear Lamb waves was compared with the zero-frequency pulse signal after low-pass filtering. The results showed consistency between the two. Furthermore, a method for extracting the zero-frequency pulse signal in the time domain is developed, which is without signal decomposition. Furthermore, a time-domain index is established by zero-frequency pulse signal to indicate the severity of early damage in mechanical parts. Finite element simulation validates the accuracy of zero-frequency pulse signal extraction and evaluates nonlinear damage. Experimental validation is conducted by ultrasonic nonlinear testing on alloy-6061 plates with fatigue damage, affirming the effectiveness of early damage detection. This work proposes a method that allows for the intuitive analysis of the time domain zero-frequency pulse signal, providing a reference for determining the time duration in subsequent studies. Additionally, the established index provides a reference for early fatigue damage detection.
Improved wear and corrosion resistance of magnesium AZ80 alloy prepared by laser surface remelting
Authors: Zhang Yuan , Taheri Morteza , Shirvani Kourosh , Entezarian Mohammad Mahdi 
Published: October 2024

Abstract
Laser surface remelting (LSR) is a laser-based surface treatment method. In the LSR process, microstructural defects such as cracks and porosity are suppressed in addition to grain refinement, and the mechanical properties are improved. The present research investigated the effects of LSR parameters on the microstructure, wear, and corrosion behavior of Mg AZ80 alloy. The results showed that in LSR, the coarse-grained (29.8 μm) structure of AZ80 was transformed into a fine-grained structure (3.1 μm) with no microstructural defects. The evaporation of Mg during LSR and the formation of Al-rich and Mg-poor phases are the most important challenges in the surface treatment of AZ80. This limitation was solved by optimizing the LSR parameters, which included a gas flow rate of 2 L min−1, pulse duration of 3 ms, scanning speed of 3 mm s−1, pulse frequency of 8 Hz, and heat input of 64 J mm−1. The prevention of Mg evaporation was associated with the elimination of porosity and cracks, reducing of the solidification range, and uniform distribution of β-Mg17Al12 precipitation phases in α-Mg refined grains. The tribological behavior of the laser-treated region showed that the COF, depth of the wear scar, wear rate, and wear volume loss were reduced by 18%, 48%, 37%, and 66%, respectively, compared to AZ80. This result is attributed to the refinement of α-Mg grains and the uniform distribution of β-Mg17Al12 in the laser-treated region. The results of the polarization curves of the corrosion test in 3.5 wt% NaCl solution showed that the optimal laser-treated region with the lowest corrosion current density (34.68 × 10−6 μA.cm−2) and highest self-corrosion potential (1.425 V) exhibited the highest corrosion resistance. A slight change in the breakdown potential current slope in the laser-treated region indicates the formation of a protective film on the surface after the completion of LSR, which increases corrosion resistance.
Modular invariant slow roll inflation
Authors: Ding Gui-Jun , Jiang Si-Yi , Zhao Wenbin 
Published: October 2024

Abstract
We propose new classes of inflation models based on the modular symmetry, where the modulus field τ serves as the inflaton. We establish a connection between modular inflation and modular stabilization, wherein the modulus field rolls towards a fixed point along the boundary of the fundamental domain. We find the modular symmetry strongly constrain the possible shape of the potential and identify some parameter space where the inflation predictions agree with cosmic microwave background observations. The tensor-to-scalar ratio is predicted to be smaller than 10-6 in our models, while the running of spectral index is of the order of 10-4.
Significant impact of Galactic dark matter particles on annihilation signals from Sagittarius analogues
Authors: Vienneau Evan , Evans Addy J. , Hartl Odelia V. , Bozorgnia Nassim , Strigari Louis E. , Riley Alexander H. , Shipp Nora 
Published: October 2024

Abstract
We examine the gamma-ray signal from dark matter (DM) annihilation from analogues of the
Sagittarius (Sgr) dwarf spheroidal galaxy in the Auriga cosmological simulations. For
velocity-dependent annihilation cross sections, we compute emissions from simulated Sgr subhalos
and from the Milky Way (MW) foreground. In addition to the annihilation signals from DM particles
bound to Sgr, we consider for the first time the annihilation of DM particles bound to the MW that
overlap spatially with Sgr. For p-wave models this contribution can enhance the signal by over an
order of magnitude, while for d-wave models the enhancement can be over three orders of
magnitude. For Sommerfeld and s-wave models, the corresponding emission does not significantly
change. For the Sommerfeld model, the Sgr source can be visible above the MW foreground emission,
while for s, p and d-wave models, the signal towards Sgr is most likely dominated by foreground MW
emission. We interpret our results within the context of the observed gamma-ray emission from
Sgr. We find that, given the background emission estimated from this region, the templates from
simulations likely have spatial morphology that is too extended to explain the point-like emission
that is observed.
Heterojunction-engineered carrier transport in elevated-metal metal-oxide thin-film transistors
Authors: Li Xiao , Ma Zhikang , Li Jinxiong , Pan Wengao , Liao Congwei , Zhang Shengdong , Gao Zhuo , Fu Dong , Lu Lei 
Published: October 2024

Abstract
This study investigates the carrier transport of heterojunction channel in oxide semiconductor thin-film transistor (TFT) using the elevated-metal metal-oxide (EMMO) architecture and indium−zinc oxide (InZnO). The heterojunction band diagram of InZnO bilayer was modified by the cation composition to form the two-dimensional electron gas (2DEG) at the interface quantum well, as verified using a metal−insulator−semiconductor (MIS) device. Although the 2DEG indeed contributes to a higher mobility than the monolayer channel, the competition and cooperation between the gate field and the built-in field strongly affect such mobility-boosting effect, originating from the carrier inelastic collision at the heterojunction interface and the gate field-induced suppression of quantum well. Benefited from the proper energy-band engineering, a high mobility of 84.3 cm2·V−1·s−1, a decent threshold voltage (V
th) of −6.5 V, and a steep subthreshold swing (SS) of 0.29 V/dec were obtained in InZnO-based heterojunction TFT.
Pharmacy 3D printing
Authors: Cheng Jessica T Y , Tan Edwin C K , Kang Lifeng 
Published: October 2024

Abstract
A significant limitation of the ‘one size fits all’ medication approach is the lack of consideration for special population groups. 3D printing technology has revolutionised the landscape of pharmaceuticals and pharmacy practice, playing an integral role in enabling on-demand production of customised medication. Compared to traditional pharmaceutical processes, 3D printing has major advantages in producing tailored dosage forms with unique drug release mechanisms. Moreover, this technology has enabled the combination of multiple drugs in a single formulation addressing key issues of medication burden. Development of 3D printing in pharmacy applications and large-scale pharmaceutical manufacturing has substantially increased in recent years. This review focuses on the emergence of extrusion-based 3D printing, particularly semi solid extrusion, fused deposition modelling and direct powder extrusion, which are currently the most commonly studied for pharmacy practice. The concept of each technique is summarised, with examples of current and potential applications. Next, recent advancements in the 3D printer market and pharmacist perceptions are discussed. Finally, the benefits, challenges and prospects of pharmacy 3D printing technology are highlighted, emphasising its significance in changing the future of this field.
Transfer learning-assisted modulation classification and OSNR estimation using constellation diagrams
Authors: Jha Dhirendra Kumar , Mishra Jitendra K 
Published: October 2024

Abstract
A cost-effective deep transfer learning (TL) learned with characteristics derived from the constellation diagram is proposed for executing modulation classification and optical signal-to-noise ratio (OSNR) estimation for the future generation of elastic optical networks. The information acquired with the ImageNet dataset is transferred using pre-trained TL versions including VGG19, ResNet152V2, InceptionV3, and ResNet50 to identify various modulation formats and their corresponding OSNR. Adam optimizer is employed to ascertain the appropriate settings for the hyperparameters. Numerical results demonstrate that the proposed VGG19 method achieves the highest level of accuracy (100%) in the recognition of various modulation forms. To fulfil the requirements of actual use, OSNR monitoring is also explored, with a total precision equal to 95.2%. Furthermore, a thorough investigation of the impact of training data dimensions, on TL performance is conducted. The outcomes of the proposed method demonstrate that the suggested TL-based algorithms are more accurate and need much less training and testing time than non-TL approaches.
Hybrid algorithm for the time-dependent Hartree–Fock method using the Yang–Baxter equation on quantum computers*
Authors: Gulania Sahil , Gray Stephen K , Alexeev Yuri , Peng Bo , Govind Niranjan 
Published: October 2024

Abstract
The time-dependent Hartree–Fock (TDHF) method is an approach to simulate the mean field dynamics of electrons within the assumption that the electrons move independently in their self-consistent average field and within the space of single Slater determinants. One of the major advantages of performing time dynamics within Hartree–Fock theory is the free fermionic nature of the problem, which makes TDHF classically simulatable in polynomial time. Here, we present a hybrid TDHF implementation for quantum computers. This quantum circuit grows with time; but with our recent work on circuit compression via the Yang–Baxter equation (YBE), the resulting circuit is constant depth. This study provides a new way to simulate TDHF with the aid of a quantum device as well as provides a new direction for the application of YBE symmetry in quantum chemistry simulations.
Evaluating the robustness of the ARIO model for a local disaster: 2021 flooding in Germany
Authors: Juhel Samuel , Delahais Adrien , Viguié Vincent 
Published: October 2024

Abstract
Given the interconnectedness of modern economies and the widespread adoption of just-in-time production methods, even minor disruptions caused by natural disasters can lead to substantial indirect economic impact. A substantial body of literature has explored this phenomenon, using input-output analysis, computable general equilibrium and agent-based models. However, these models (i) heavily rely on parameters and data that often lack empirical grounding or (ii) exhibit considerable uncertainty, making it challenging to assess their reliability. The ARIO model has been widely used in the literature and has provided theoretical foundation for several related models. Using the July 2021 floods in Germany as a case study, we assess the sensitivity of the results of this model by varying key parameters, as well as the multi-regional input-output tables (MRIOTs), which constitute its primary input data. To facilitate this analysis, we introduce a new, resource-efficient Python implementation of the ARIO model, enabling the execution of a large number of simulations. Our findings highlight the substantial impact of data source and parameter selection on model outcomes, especially so when post-disaster rebuilding is costly. To ensure the robustness of their results, future studies on indirect economic impacts should be careful about recovery dynamics, consider multiple scenarios and compare results using MRIOTs from various sources.
The weak-type Carleson theorem via wave packet estimates
Authors: Di Plinio Francesco , Fragkos Anastasios 
Published: October 2024

We prove that the weak-

L

p

L^{p}

norms, and in fact the sparse

(
p
,
1
)

(p,1)

-norms, of the Carleson maximal partial Fourier sum operator are

≲
(
p
−
1

)

−
1

\lesssim (p-1)^{-1}

as

p
→

1
+

p\to 1^+

. This is an improvement on the Carleson-Hunt theorem, where the same upper bound on the growth order is obtained for the restricted weak-

L
p

L^p

type norm, and which was the strongest quantitative bound prior to our result. Furthermore, our sparse

(
p
,
1
)

(p,1)

-norms bound imply new and stronger results at the endpoint

p
=
1

p=1

. In particular, we obtain that the Fourier series of functions from the weighted Arias de Reyna space

Q
A

∞

(
w
)

\mathrm {QA}_{\infty }(w)

, which contains the weighted Antonov space

L
log
⁡
L
log
⁡
log
⁡
log
⁡
L
(

T

;
w
)

L\log L\log \log \log L(\mathbb T; w)

, converge almost everywhere whenever

w
∈

A
1

w\in A_1

. This is an extension of the results of Antonov [Proceedings of the XXWorkshop on Function Theory (Moscow, 1995), 1996, pp. 187–196] and Arias De Reyna, where

w
w

must be Lebesgue measure.

The backbone of our treatment is a new, sharply quantified near-

L
1

L^1

Carleson embedding theorem for the modulation-invariant wave packet transform. The proof of the Carleson embedding relies on a newly developed smooth multi-frequency decomposition which, near the endpoint

p
=
1

p=1

, outperforms the abstract Hilbert space approach of past works, including the seminal one by Nazarov, Oberlin and Thiele [Math. Res. Lett. 17 (2010), pp. 529–545]. As a further example of application, we obtain a quantified version of the family of sparse bounds for the bilinear Hilbert transforms due to Culiuc, Ou and the first author.
Evaluating the Impact of BoNT-A Injections on Facial Expressions: A Deep Learning Analysis
Authors: Aktar Ugurlu Gulay , Ugurlu Burak Numan , Yalcinkaya Meryem 
Published: October 2024

Abstract

Background
Botulinum toxin type A (BoNT-A) injections are widely administered for facial rejuvenation, but their effects on facial expressions remain unclear.

Objectives
In this study, we aimed to objectively measure the impact of BoNT-A injections on facial expressions with deep learning techniques.

Methods
One hundred eighty patients age 25 to 60 years who underwent BoNT-A application to the upper face were included. Patients were photographed with neutral, happy, surprised, and angry expressions before and 14 days after the procedure. A convolutional neural network (CNN)-based facial emotion recognition (FER) system analyzed 1440 photographs with a hybrid data set of clinical images and the Karolinska Directed Emotional Faces (KDEF) data set.

Results
The CNN model accurately predicted 90.15% of the test images. Significant decreases in the recognition of angry and surprised expressions were observed postinjection (P &lt; .05), with no significant changes in happy or neutral expressions (P &gt; .05). Angry expressions were often misclassified as neutral or happy (P &lt; .05), and surprised expressions were more likely to be perceived as neutral (P &lt; .05).

Conclusions
Deep learning can effectively assess the impact of BoNT-A injections on facial expressions, providing more standardized data than traditional surveys. BoNT-A may reduce the expression of anger and surprise, potentially leading to a more positive facial appearance and emotional state. Further studies are needed to understand the broader implications of these changes.

Level of Evidence: 4 (Therapeutic)
Detectability of a poison frog and its Batesian mimic depends on body posture and viewing angle
Authors: McEwen Brendan L , Yeager Justin , Kinley Isaac , Anderson Hannah M , Barnett James B 
Published: October 2024

Abstract
Aposematic signals warn predators that prey should be avoided due to dangerous secondary defences. However, as warning signals do not always produce avoidance, warning colors may evolve as a trade-off balancing detectability against signal saliency. For Batesian mimics, which display salient signals but lack secondary defenses, the costs of predator encounters are greater, potentially increasing the benefit of crypsis. This raises the question of whether imperfect mimicry may reduce detectability while retaining mimetic efficacy. We tested this hypothesis with the poisonous frog Ameerega bilinguis and undefended Batesian mimic Allobates zaparo, using computational visual modeling and screen-based detection trials with human participants. We found that both species incorporate camouflage into their warning colors, but to different degrees depending on viewing angle and behavior. Contrary to expectation, we found differences in detectability between model and mimic that do not adhere to the hypothesized cryptic mimetic phenotype. To aerial observers, we found the mimic to be more detectable than the model. To terrestrial observers, likely owing to the model’s bright ventral color, we found the model more detectable in viewing angles that highlight the ventral coloration, whereas the mimic was more detectable in viewing angles that highlight the dorsal coloration. Consequently, we suggest that in addition to being the result of perceptual or developmental constraints, imperfect mimicry may also evolve as an adaptive strategy which balances camouflage with different signaling functions. Our findings complement the emerging view that aposematic signals may evolve in response to a multitude of selection pressures beyond aversion alone.
Learning to handle flowers increases pollen collection for bees but does not affect pollination success for plants
Authors: Mayberry Maggie M , Naumer Katherine C , Novinger Annaliese N , McCart Dalton M , Wilkins Rachel V , Muse Haley , Ashman Tia-Lynn , Russell Avery L 
Published: October 2024

Abstract
Cooperation and conflict are common in plant–pollinator interactions. Flowering plants often entice pollinators to visit by offering floral food rewards, thereby facilitating pollination. However, pollinators such as bees can learn to improve their collection of floral rewards (such as pollen), changing how they interact with the flower’s reproductive organs, which together could reduce pollination success. Consequently, complex flowers that slow pollinator learning might benefit the plant. Yet how pollinator learning and flower complexity interact to affect pollination success is unknown. We therefore asked how differences in complexity of 4 flower types (Phacelia campanularia, Exacum affine, Solanum elaeagnifolium, and Erythranthe guttata) affected learning by pollen-foraging generalist bumble bees (Bombus impatiens) and how learning affected pollen collection and pollen deposition on these flowers. We found that bees generally learned how to efficiently handle more complex flower types more slowly. Bees that required more visits to become efficient foragers collected less pollen, with no effect on pollen deposition. Except for the simplest flower type, learning also involved development of motor routines unique to each flower type. Experienced bees overall collected more pollen, but individual differences in motor routines did not affect pollen collection. Conversely, individual differences in motor routines affected pollen deposition, but there was no overall effect of experience. Thus, even though learning overall benefits the bee, it does not alter female (and potentially male) fitness benefits for the plant. We discuss potential reasons for these patterns and consequences for bee behavior and flower evolution.
Sustained improvements in brain health and metabolic markers 24 months following bariatric surgery
Authors: Legault Marianne , Pelletier Mélissa , Lachance Amélie , Lachance Marie-Ève , Zeighami Yashar , Gauthier Marie-Frédérique , Iceta Sylvain , Biertho Laurent , Fulton Stephanie , Richard Denis , Dagher Alain , Tchernof André , Dadar Mahsa , Michaud Andréanne 
Published: October 2024

Abstract
Obesity and its metabolic complications are associated with lower grey matter and white matter densities, whereas weight loss after bariatric surgery leads to an increase in both measures. These increases in grey and white matter density are significantly associated with post-operative weight loss and improvement of the metabolic/inflammatory profiles. While our recent studies demonstrated widespread increases in white matter density 4 and 12 months after bariatric surgery, it is not clear if these changes persist over time. The underlying mechanisms also remain unknown. In this regard, numerous studies demonstrate that the enlargement or hypertrophy of mature adipocytes, particularly in the visceral fat compartment, is an important marker of adipose tissue dysfunction and obesity-related cardiometabolic abnormalities. We aimed (i) to assess whether the increases in grey and white matter densities previously observed at 12 months are maintained 24 months after bariatric surgery; (ii) to examine the association between these structural brain changes and adiposity and metabolic markers 24 months after bariatric surgery; and (iii) to examine the association between abdominal adipocyte diameter at the time of surgery and post-surgery grey and white matter densities changes. Thirty-three participants undergoing bariatric surgery were recruited. Grey and white matter densities were assessed from T1-weighted magnetic resonance imaging scans acquired prior to and 4, 12 and 24 months post-surgery using voxel-based morphometry. Omental and subcutaneous adipose tissue samples were collected during the surgical procedure. Omental and subcutaneous adipocyte diameters were measured by microscopy of fixed adipose tissue samples. Linear mixed-effects models were performed controlling for age, sex, surgery type, initial body mass index, and initial diabetic status. The average weight loss at 24 months was 33.6 ± 7.6%. A widespread increase in white matter density was observed 24 months post-surgery mainly in the cerebellum, brainstem and corpus callosum (P &lt; 0.05, false discovery rate) as well as some regions in grey matter density. Greater omental adipocyte diameter at the time of surgery was associated with greater changes in total white matter density at 24 months (P = 0.008). A positive trend was observed between subcutaneous adipocyte diameter at the time of surgery and changes in total white matter density at 24 months (P = 0.05). Our results show prolonged increases in grey and white matter densities up to 24 months post-bariatric surgery. Greater preoperative omental adipocyte diameter is associated with greater increases in white matter density at 24 months, suggesting that individuals with excess visceral adiposity might benefit the most from surgery.