bluuebunny/crossref_metadata_2025_split huggingface dataset:
New dynamics performance for established dark solitons in polariton condensate
Authors: Zahran Emad H M , Bekir Ahmet , Ibrahim Reda A 
Published: October 2024

Abstract
New diverse enormous soliton solutions to the Gross–Pitaevskii equation, which describes the dynamics of two dark solitons in a polarization condensate under non-resonant pumping, have been constructed for the first time by using two different schemes. The two schemes utilized are the generalized Kudryashov scheme and the (G’/G)-expansion scheme. Throughout these two suggested schemes we construct new diverse forms solutions that include dark, bright-shaped soliton solutions, combined bright-shaped, dark-shaped soliton solutions, hyperbolic function soliton solutions, singular-shaped soliton solutions and other rational soliton solutions. The two 2D and 3D figure designs have been configured using the Mathematica program. In addition, the Haar wavelet numerical scheme has been applied to construct the identical numerical behavior for all soliton solutions achieved by the two suggested schemes to show the existing similarity between the soliton solutions and numerical solutions.
Probing axion-like particles in leptonic decays of heavy mesons*
Authors: Yang 杨 Gang 钢 , Wang 王 Tianhong 天鸿 , Wang 王 Guo-Li 国利 
Published: October 2024

Abstract
We study the possibility of finding axion-like particles (ALPs) through the leptonic decays of heavy mesons. The Standard Model (SM) predictions of the branching ratios of the leptonic decays of heavy mesons are less than the corresponding experimental upper limits. This provides some room for the existence of decay channels, of which the ALP is one of the products. Three scenarios are considered: First, the ALP is only coupled to one single charged fermion, namely, the quark, the antiquark, or the charged lepton; second, the ALP is only coupled to quark and antiquark with the same strength; and third, the ALP is coupled to all the charged fermions with the same strength. The constraints of the coupling strength in different scenarios are obtained by comparing the experimental data of the branching ratios of leptonic decays of

,

, and

mesons with the theoretical predictions achieved by using the Bethe-Salpeter (BS) method. These constraints are further applied to predict the upper limits of the leptonic decay processes of the

meson in which the ALP participates.
Statistical Research on Megahertz-peaked Spectra Pulsars
Authors: Cai Hong-Bing , Chen Li , Li Shi-Dang , Wang Kai , Liu Hang 
Published: October 2024

Abstract
Megahertz-peaked spectra (MPS) pulsars are referred as cases whose radio spectra turn over around 100 MHz. We identified 53 MPS pulsars based on the spectral data from the literature, and statistically analyzed the spatial location distribution, the magnetic field-period distribution, peak frequencies, spectral indices, and dispersion measures of these MPS pulsars. We found that there is a strong positive correlation between the dispersion measures and the peak frequencies of MPS pulsars, and negative correlations of the dispersion measures with spectral indices and the ages are also found. Such correlations suggest that the interstellar medium is an important factor that affects observational properties of MPS pulsars.
An ANN models cortical-subcortical interaction during post-stroke recovery of finger dexterity
Authors: Kadry Ashraf , Solomonow-Avnon Deborah , Norman Sumner L , Xu Jing , Mawase Firas 
Published: October 2024

Abstract

Objective. Finger dexterity, and finger individuation in particular, is crucial for human movement, and disruptions due to brain injury can significantly impact quality of life. Understanding the neurological mechanisms responsible for recovery is vital for effective neurorehabilitation. This study explores the role of two key pathways in finger individuation: the corticospinal (CS) tract from the primary motor cortex and premotor areas, and the subcortical reticulospinal (RS) tract from the brainstem. We aimed to investigate how the cortical-reticular network reorganizes to aid recovery of finger dexterity following lesions in these areas. Approach. To provide a potential biologically plausible answer to this question, we developed an artificial neural network (ANN) to model the interaction between a premotor planning layer, a cortical layer with excitatory and inhibitory CS outputs, and RS outputs controlling finger movements. The ANN was trained to simulate normal finger individuation and strength. A simulated stroke was then applied to the CS area, RS area, or both, and the recovery of finger dexterity was analyzed. Main results. In the intact model, the ANN demonstrated a near-linear relationship between the forces of instructed and uninstructed fingers, resembling human individuation patterns. Post-stroke simulations revealed that lesions in both CS and RS regions led to increased unintended force in uninstructed fingers, immediate weakening of instructed fingers, improved control during early recovery, and increased neural plasticity. Lesions in the CS region alone significantly impaired individuation, while RS lesions affected strength and to a lesser extent, individuation. The model also predicted the impact of stroke severity on finger individuation, highlighting the combined effects of CS and RS lesions. Significance. This model provides insights into the interactive role of cortical and subcortical regions in finger individuation. It suggests that recovery mechanisms involve reorganization of these networks, which may inform neurorehabilitation strategies.
Review of deep representation learning techniques for brain–computer interfaces
Authors: Guetschel Pierre , Ahmadi Sara , Tangermann Michael 
Published: October 2024

Abstract
In the field of brain–computer interfaces (BCIs), the potential for leveraging deep learning techniques for representing electroencephalogram (EEG) signals has gained substantial interest. Objective: This review synthesizes empirical findings from a collection of articles using deep representation learning techniques for BCI decoding, to provide a comprehensive analysis of the current state-of-the-art. Approach: Each article was scrutinized based on three criteria: (1) the deep representation learning technique employed, (2) the underlying motivation for its utilization, and (3) the approaches adopted for characterizing the learned representations. Main results: Among the 81 articles finally reviewed in depth, our analysis reveals a predominance of 31 articles using autoencoders. We identified 13 studies employing self-supervised learning (SSL) techniques, among which ten were published in 2022 or later, attesting to the relative youth of the field. However, at the time being, none of these have led to standard foundation models that are picked up by the BCI community. Likewise, only a few studies have introspected their learned representations. We observed that the motivation in most studies for using representation learning techniques is for solving transfer learning tasks, but we also found more specific motivations such as to learn robustness or invariances, as an algorithmic bridge, or finally to uncover the structure of the data. Significance: Given the potential of foundation models to effectively tackle these challenges, we advocate for a continued dedication to the advancement of foundation models specifically designed for EEG signal decoding by using SSL techniques. We also underline the imperative of establishing specialized benchmarks and datasets to facilitate the development and continuous improvement of such foundation models.
Vertical instability prediction and its direction control using a support vector machine in integrated commissioning of JT-60SA solely based on magnetics
Authors: Inoue S. , Kojima S. , Miyata Y. , Wakatsuki T. , Yokoyama T. , Takechi M. , Urano H. , Yoshida M. , Suzuki T. ,   the JT-60SA Integrated Project Team
Published: October 2024

Abstract
We developed a redundant logic for predicting Vertical Displacement Events (VDEs) using only magnetic diagnostics with a Support Vector Machine (Inoue et al 2022 Nucl. Fusion
62 086007). This logic was experimentally validated in the integrated commissioning of JT-60SA. Triggering of VDEs results in significant asymmetric heat loads on first walls and electromagnetic loads on conducting materials. These concerns are mitigated by a newly proposed direction control for VDEs. We successfully detected VDEs in experiments and controlled their direction by setting the control voltage to zero upon detection, thus guiding VDEs in an intended direction and reducing the risk to the device from unmitigated VDEs in either direction, although completely avoiding VDEs remains the ideal. In the developed predictor, VDEs are predicted using proxies to monitor the controller’s performance with an adaptive voltage allocation scheme (Inoue et al 2021 Nucl. Fusion
61 096009). For future experiments, we plan to extend our approach to monitor more detailed information from the equilibrium controller, including control values of each PID component, which has been shown to enhance the prediction accuracy of VDEs.
Deterministic equivalent and error universality of deep random features learning*
Authors: Schröder Dominik , Cui Hugo , Dmitriev Daniil , Loureiro Bruno 
Published: October 2024

Abstract
This manuscript considers the problem of learning a random Gaussian network function using a fully connected network with frozen intermediate layers and trainable readout layer. This problem can be seen as a natural generalization of the widely studied random features model to deeper architectures. First, we prove Gaussian universality of the test error in a ridge regression setting where the learner and target networks share the same intermediate layers, and provide a sharp asymptotic formula for it. Establishing this result requires proving a deterministic equivalent for traces of the deep random features sample covariance matrices which can be of independent interest. Second, we conjecture the asymptotic Gaussian universality of the test error in the more general setting of arbitrary convex losses and generic learner/target architectures. We provide extensive numerical evidence for this conjecture. In light of our results, we investigate the interplay between architecture design and implicit regularization.
AI for biofabrication
Authors: Zhou Chang , Liu Changru , Liao Zhendong , Pang Yuan , Sun Wei 
Published: October 2024

Abstract
Biofabrication is an advanced technology that holds great promise for constructing highly biomimetic in vitro three-dimensional human organs. Such technology would help address the issues of immune rejection and organ donor shortage in organ transplantation, aiding doctors in formulating personalized treatments for clinical patients and replacing animal experiments. Biofabrication typically involves the interdisciplinary application of biology, materials science, mechanical engineering, and medicine to generate large amounts of data and correlations that require processing and analysis. Artificial intelligence (AI), with its excellent capabilities in big data processing and analysis, can play a crucial role in handling and processing interdisciplinary data and relationships and in better integrating and applying them in biofabrication. In recent years, the development of the semiconductor and integrated circuit industries has propelled the rapid advancement of computer processing power. An AI program can learn and iterate multiple times within a short period, thereby gaining strong automation capabilities for a specific research content or issue. To date, numerous AI programs have been applied to various processes around biofabrication, such as extracting biological information, designing and optimizing structures, intelligent cell sorting, optimizing biomaterials and processes, real-time monitoring and evaluation of models, accelerating the transformation and development of these technologies, and even changing traditional research patterns. This article reviews and summarizes the significant changes and advancements brought about by AI in biofabrication, and discusses its future application value and direction.
Multiple conformal-contact transfer of large-area crack-free transition metal dichalcogenide stacks
Authors: Fu Yu , Niu Yilin , Xue Guodong , Guo Quanlin , Liu Chang , Chen Jiale , Hong Hao , Liu Can , Liu Kaihui 
Published: October 2024

Abstract
Atomically-thin two-dimensional (2D) transition metal dichalcogenides (TMDs) have emerged as an ideal platform for both physics investigation and device applications. By stacking different layers into homo- or hetero-structures, an extra degree of freedom is involved in further tuning their properties, thereby boosting scenarios in twistronics, moiré photonics and optoelectronics. However, interfacial imperfections such as contaminations and cracks, frequently occur during the layer stacking sequence and accumulate layer by layer, greatly degenerating the interface quality. In this study, we developed a multiple conformal-contact transfer method to construct TMD stacks with crack-free intrinsic interfaces. The design of a deformable buffer layer is crucial to guarantee the conformal contact and intact transfer of each layer, contributing to the successful construction of centimetre-scale TMD stacks up to 8 layers. Precise control over spatial location and interlayer twist angle is also feasibly achieved, evidenced by the stacking-dependent interlayer exciton (IE) effects in WS2-WSe2 heterostructures. This work provides a facile and precise approach for architecting 2D stacks with perfect interfaces, which will further accelerate the customized design for their device functionalization.
High-pressure supercritical CO2-assisted fabrication of two-dimensional LaAlO3 and its room temperature ferromagnetism
Authors: Che Guoqi , Gao Bo , Xu Qun 
Published: October 2024

Abstract
Spintronics applications in two-dimensional (2D) magnetic materials can significantly contribute to the miniaturization and energy efficiency of semiconductor devices. However, current 2D ferromagnetic materials face challenges such as ferromagnetic instability and low Curie temperature (Tc), which limit their broader application. In this study, 2D room-temperature ferromagnetic LaAlO3 was successfully prepared with supercritical carbon dioxide (SC CO2). With the enhanced stress effect of CO2 molecules, the contents of oxygen vacancies OV, aluminium vacancies AlV, and AlO4 in the 2D structure are elevated, and then the ferromagnetic properties of LaAlO3 appeared to be significantly enhanced due to defects and spatial symmetry breaking of the octahedron. Notably, the 2D intrinsic ferromagnetic LaAlO3 exhibited a Tc above 350 K. Therefore, this work supplies a new means for SC CO2 to modulate the microstructure and ferromagnetic properties of 2D LaAlO3, which is conducive to expanding the applications of LaAlO3.
Study of the mechanisms of change in hydride phases in zirconium beryllide during its linear heating
Authors: Kulsartov Timur , Zaurbekova Zhanna , Kenzhin Yergazy , Kenzhina Inesh , Bochkov Vadim , Askerbekov Saulet , Ponkratov Yuriy , Yelishenkov Alexandr , Udartsev Sergey 
Published: October 2024

Abstract
Beryllides have a number of unique properties that make them promising for use in various fields of industry and energy. Since some of the beryllides have the potential to dissolve large amounts of hydrogen (in some cases forming hydride phases), there is some interest in evaluating their possible applications in hydrogen technologies. This paper presents the results of studies of changes in the phase composition of the zirconium beryllide deuteride compound during its linear heating. The studies were carried out using a complex method including thermal gravimetrical analysis (TGA), differential scanning calorimetry (DSC) and mass spectrometry. Based on the results of the experiments, a number of parameters were obtained characterizing the transitions of deuteride from the phase of the Orthorhombic I

mma
crystal structure of deuteride ZrBe2D into the Orthorhombic C

mcm
crystal structure of deuteride ZrBe2D0.67, as well as the decomposition parameters of Orthorhombic C

mcm
crystal structure of ZrBe2D0.67. The energies of endothermic reactions of the corresponding transitions and decompositions of deuteride phases were estimated, and the activation energy of the interaction of water vapor with the surface of zirconium beryllide samples was also estimated.
The effect of complex extra-furnace treatment of metal melts on the formation of non-metallic inclusions in large-sized ingots
Authors: Issagulov Aristotel , Kovalev Pavel , Melnik Sergey , Kulikov Vitaliy , Kvon Svetlana , Arinova Saniya 
Published: October 2024

Abstract
The article presents the results of studying non-metallic inclusions (NI) after extra-furnace treatment of the 13Cr9Mo2Co1NiVNbNB steel melt to produce large-sized ingots. The objects of the study were laboratory samples (the first batch), and samples obtained after melting in industrial furnace, followed by modification and double vacuuming (the second batch). Previously, using the Fact Sage software, modeling of the NI formation processes was carried out. The data were obtained on the possible NI composition and amount in this steel. The composition, the contamination index, parameters of NI in samples have been studied. It was established that in the samples of the first batch, the main part of the NI consisted of chromium- and manganese-containing oxides, which occupy more than 94% of the total area of the NI. In the samples of the second batch, the main part of the NI was represented by aluminum containing systems. It was shown that the average size of NI in the samples after extra-furnace treatment differs slightly (5%–12%), however, the contamination index in industrial samples after vacuum treatment and especially after modification and repeated vacuum treatment was reduced by more than 10 times. The results obtained allow to recommend vacuum treatment and complex modification with calcium and boron as measures to prevent the formation of NI and to improve metallurgical quality when smelting large ingots using complex alloy steels.
Spring water discharge and metal enrichment in urban soils
Authors: Mullins Angela R , Bain Daniel J 
Published: October 2024

Abstract
Urban centers have inherited a unique mix of persistent contamination that impacts interactions among urban soil and groundwater systems. In particular, the potential for urban groundwater to transport contaminants from surface sources through the subsurface environment and ultimately to soils is not well understood. Studies have focused on specific ‘natural’ mechanisms driving distribution of metals in urban soils. However, very few studies have examined the accumulation of contamination in soils at groundwater discharge locations (springs) and the potential for groundwater to redistribute urban legacy contaminants far from the source. Soil transects straddling four groundwater springs in Pittsburgh, Pennsylvania were sampled to evaluate patterns resulting from contaminated groundwater discharge on urban soils. Metal concentrations were measured in pore water and compared with concentrations observed in total digestions and exchangeable extractions (acetic acid) of the soil. Across the springs Co, Cr, Ni, and V (metals often used in steel alloys) were elevated downslope, suggesting contaminated groundwater discharge enriches trace metals in these locations. These processes create unexpected biogeochemical patterns on the landscape and have the potential to create hotspots of soil metal contamination at predictable points across the urban landscape.
Heat impacts on an aging society: a spatio-temporal analysis of heatstroke deaths in Japan
Authors: Zeng Ming , Baptista Emerson Augusto , Kakinuma Kaoru 
Published: October 2024

Abstract
Climate change, especially through heatwaves, significantly affects human health and is a growing global concern. Concurrently, population aging is occurring worldwide, with many countries experiencing an increase in their elderly populations. As the elderly are particularly susceptible to extreme temperatures and unevenly distributed due to internal migration of younger populations, a spatio-temporal analysis integrating temperature changes and demographic data is essential. This study focuses on Japan, a super-aged society where over 25% of the total population is 65 years or older. We examined the effects of climate and the proportion of the elderly population on heatstroke deaths through spatio-temporal analysis within a Bayesian framework. We estimated the annual relative risk of heatstroke-related deaths at the prefecture level from 2008 to 2019. The results indicate a strong spatial autocorrelation in heatstroke deaths across Japan. The spatio-temporal interaction model was the best-performing, showing that regional and temporal variations significantly impact heatstroke mortality. In this model, a one-degree increase in temperature anomaly was linked to a 0.35 (95% CI 0.25 to 0.46) times higher odds of heatstroke deaths, while a 1% increase in the population aged 65 years or older was associated with 4.85 (95% CI 0.92 to 8.65) times higher odds. We found that not only metropolitan areas but also rural areas, such as the Tohoku and Shikoku regions, face a high risk of heatstroke, emphasizing the need to address the challenges in rural communities. Our study highlights the necessity of integrating temperature changes and demographic data in a spatio-temporal context for heatstroke risk assessment. It demonstrates the profound effects of temperature anomalies and the proportion of the elderly population on heatstroke mortality. This research framework could be applicable to other countries experiencing aging and heatwave issues, aiding in the development of targeted public health interventions.
6G networks for the operating room of the future
Authors: Jurosch Franziska , Kröger Nicolai , Kolb Sven , Mehmeti Fidan , Martens Eimo , Speidel Stefanie , Kellerer Wolfgang , Wilhelm Dirk , Fuchtmann Jonas 
Published: October 2024

Abstract
Technical setups in today’s operating rooms (ORs) are becoming increasingly complex, especially with the integration of applications which rely on the fusion of multiple information sources. While manufacturers have already started to make use of such approaches, the quest for fully integrated ORs becoming standard is still ongoing. We describe a variety of state-of-the-art projects that envision an OR of the future in order to identify missing building blocks. While these initial implementations of sensor fused ORs have shown to be promising, all current proposals lack a scalable networking backbone that serves the needs of future applications. We therefore discuss how the coming 6G standard’s envisioned advancements can provide a flexible and intelligent platform to enable the fully integrated OR of the future.