bluuebunny/crossref_metadata_2025_split huggingface dataset:
An ultrasensitive refractive index based THz optical biosensor based on plasmon induced transparency (PIT)
Authors: Tabassum Sumaiya Jahan , Mohsin Abu S M , Bhuian Mohammed Belal H , Haque Munima , Mosaddequr Rahman Md 
Published: August 2024

Abstract
A novel, label-free optical sensor based on plasmon induced transparency (PIT) has been designed for the detection of basal cell carcinoma in the terahertz (THz) frequency range. The sensor consists of a thin Indium Antimonide (InSb) layer on top of a silica layer with patterned plasmonic antennas where the biosamples are deposited. To overcome the problem associated with the absorption modes of water molecules in THz, the sensor is designed with resonances at frequencies where light absorption of water molecules is minimal. To enhance the reported sensor’s performance features, parametric sweeps have been conducted on the geometrical attributes of the nanostructure. Due to the strong coupling between radiative and dark plasmonic modes, the sensor has a very high sensitivity of 4.50 THz RIU−1, a Q factor of 112.7, and a figure of merit (FOM) of 43.3. The suggested design is ultracompact and easy to fabricate with the potential to be used in numerous biomedical sensing applications.
Fitting of giant magnetoimpedance curves using the complex method in optimization techniques
Authors: Sun Xutao , Shen Guotu , Zhao Zhenjie 
Published: August 2024

Abstract
The giant magnetoimpedance (GMI) effect is characterized by substantial variations in both the real and imaginary components of the impedance when subjected to an alternating current in the presence of a static magnetic field. In quasistatic models, the GMI profile can be effectively delineated by the changes in transverse permeability in response to variations in the external magnetic field. Building on this, the magnetic anisotropy field and anisotropy angle for specific magnetic phases can be determined by fitting the experimental data points. The dual magnetic phase structure of the CoP/NiCoP/Cu composite wire results in a four-peak profile in its GMI curves, making it suitable as a target curve for fitting. By preprocessing the GMI curves and applying the Complex Method within optimization techniques through multiple iterations, the theoretical target curve is ultimately obtained, along with the extraction of the physical parameters embedded within it. This approach presents a novel solution to the multi-parameter curve fitting.
Direct fabrication of rough gold nanoholes and investigation of their second-harmonic generation
Authors: Pham Quang Truong , Fandino Adriana Umbria , Ngo Gia Long , Lafargue Clement , Ledoux-Rak Isabelle , Lai Ngoc Diep 
Published: August 2024

Abstract
In this study, we introduce a simple and cost-effective technique called one-photon absorption-based direct laser writing (OPA-based DLW) to directly fabricate noncentrosymmetric gold nanoholes (Au NHs). This technique relies on the optically induced local thermal effect at the focusing spot using a 532 nm excitation wavelength, which induces the evaporation of the Au thin film and thereby creating metal NHs. By controlling the exposure doses, including fabrication powers and writing velocities, we achieve Au NHs with small sizes around 300 nm and a periodicity of 500 nm. Moreover, due to the non-uniform heat transfer in an imperfectly flat Au film, the formed NHs lack perfect circularity, showing roughness and asymmetry. Thanks to this irregular shape of Au NHs, second-harmonic generation (SHG) signals are generated. This nonlinear signal can be amplified thanks to near electric field intensity enhancement at the borders of the holes. By moving the focusing spot, this DLW method allows us to fabricate any desired two-dimensional patterns, which exhibit SHG signals. This ability could be advantageous for applications in optical data storage and nonlinear imaging.
Clustering of primordial black holes from quantum diffusion during inflation
Authors: Animali Chiara , Vennin Vincent 
Published: August 2024

Abstract
We study how large fluctuations are spatially correlated in the presence of quantum diffusion during inflation. This is done by computing real-space correlation functions in the stochastic-δ N formalism. We first derive an exact description of physical distances as measured by a local observer at the end of inflation, improving on previous works. Our approach is based on recursive algorithmic methods that consistently include volume-weighting effects. We then propose a “large-volume” approximation under which calculations can be done using first-passage time analysis only, and from which a new formula for the power spectrum in stochastic inflation is derived. We then study the full two-point statistics of the curvature perturbation. Due to the presence of exponential tails, we find that the joint distribution of large fluctuations is of the form P(ζ
R

1, ζ
R

2) = F(R
1,R
2, r) P(ζ
R

1)P( ζ
R

2), where ζ
R

1 and ζ
R

2 denote the curvature perturbation coarse-grained at radii R
1 and R
2, around two spatial points distant by r. This implies that, on the tail, the reduced correlation function, defined as P(ζ
R

1 > ζc, ζ
R

2 > ζc)/[P(ζ
R

1 > ζc) P(ζ
R

2 > ζc)]-1, is independent of the threshold value ζc. This contrasts with Gaussian statistics where the same quantity strongly decays with ζc, and shows the existence of a universal clustering profile for all structures forming in the exponential tails. Structures forming in the intermediate (i.e. not yet exponential) tails may feature different, model-dependent behaviours.
GW230529_181500: a potential primordial binary black hole merger in the mass gap
Authors: Huang Qing-Guo , Yuan Chen , Chen Zu-Cheng , Liu Lang 
Published: August 2024

AbstractDuring the fourth observing run of the LIGO-Virgo-KAGRA detector network, the LIGO Livingston observatory detected a coalescing compact binary, GW230529_181500, with component masses of 2.5–4.5M⊙and 1.2–2.0M⊙at the 90% credible level. The gravitational-wave data alone is insufficient to determine whether the components are neutron stars or black holes. In this paper, we propose that GW230529_181500 originated from the merger of two primordial black holes (PBHs). We estimate a merger rate of 5.0+47.0-4.9Gpc-3yr-1for compact binary coalescences with properties similar to GW230529_181500. Assuming the source is a PBH-PBH merger, GW230529_181500-like events lead to approximately 1.7+36.2-1.5× 10-3of the dark matter in the form of PBHs. The required abundance of PBHs to explain this event is consistent with existing upper limits derived from microlensing, cosmic microwave background observations and the null detection of gravitational-wave background by LIGO-Virgo-KAGRA.
Impact of astrophysical scatter on the epoch of reionization [H i]21 bispectrum
Authors: Murmu Chandra Shekhar , Datta Kanan K. , Majumdar Suman , Greve Thomas R. 
Published: August 2024

Abstract
It is believed that the first star-forming galaxies are the main drivers of cosmic reionization. It is usually assumed that there is a one-to-one relationship between the star formation rate (SFR) inside a galaxy and the host halo mass in semi-analytical/numerical modeling of large-scale ionization maps during the epoch of reionization. However, more accurate simulations and observations suggest that the SFR and ionizing luminosity in galaxies may vary considerably even if the host halo mass is the same. This astrophysical scatter can introduce an additional non-Gaussianity in the [H i]21cm signal, which might not be captured adequately in the power spectrum. In this work, we have studied the impact of the scatter on the [H i]21cm bispectrum using semi-numerical simulations. We find that the scatter primarily affects small ionized regions, whereas the large ionized bubbles remain largely unaffected. Although, the fractional change in the [H i]21cm bispectra due to the scatter is found to be more than a factor of 10 at large scales (k
1 ≲ 1 Mpc-1) for z=7.4, it is found to be statistically insignificant. However, at small scales (k
1 ~ 2.55 Mpc-1), we have found the impact due to the scatter to be high in magnitude (|〈Δ B 〉/B
no-scatter| ~ 1) and statistically significant (|〈Δ B〉/σ
ΔB| ≳ 5) at neutral fraction, x̅HI ~ 0.8 for z=7.4. The impact due to scatter is found to be even more prominent (|〈Δ B 〉/B
no-scatter| ≳ 10) at small scales for z=10 and x̅HI ~ 0.8, but with reduced statistical significance to some extent (|〈Δ B〉/σ
ΔB| ~ 3), compared to z=7.4 at the same neutral fraction. We have also found that in the most optimistic scenario, SKA1-Low might be able to detect these signatures of astrophysical scatter, at ~ 3σ and ~ 5σ detection significance for x̅HI ~ 0.8 and 0.9 respectively, for the equilateral [H i]21cm bispectrum at z=7.4.
FLARE: field line analysis and reconstruction for 3D boundary plasma modeling
Authors: Frerichs H. 
Published: August 2024

Abstract
The FLARE code is a magnetic mesh generator that is integrated within a suite of tools for the analysis of the magnetic geometry in toroidal fusion devices. A magnetic mesh is constructed from field line segments and permits fast reconstruction of field lines in 3D boundary plasma codes such as EMC3-EIRENE. Both intrinsically non-axisymmetric configurations (stellarators) and those with symmetry breaking perturbations of an axisymmetric equilibrium (tokamaks) are supported. The code itself is written in Modern Fortran with MPI support for parallel computing, and it incorporates object-oriented programming for the definition of the magnetic field and the material surface geometry. Extended derived types for a number of different magnetohydrodynamic equilibrium and plasma response models are implemented. The core element of FLARE is a field line tracer with adaptive step-size control, and this is integrated into tools for the construction of Poincaré maps and invariant manifolds of X-points. A collection of high-level procedures that generate output files for visualization is build on top of that. The analysis modules are build with Python frontends that facilitate customization of tasks and/or scripting of parameter scans.
Mapping the landscape of carbon dioxide removal research: a bibliometric analysis
Authors: Presty Romain , Massol Olivier , Jagu Emma , da Costa Pascal 
Published: August 2024

Abstract
An intense global research effort on carbon dioxide removal (CDR) technologies is generating a rapidly expanding scientific literature. These contributions stem from various disciplines and investigate various CDR concepts and their potential implications. This study conducts an updated analysis of the international research effort on CDR from 2012 to 2023, examining 7893 publications using bibliometric techniques. We focus on the geographic distribution of technology-specific research and the funding driving this research. Significant publication growth is observed post-2015, particularly after 2018 and in 2023, driven primarily by the EU, China, and the US. Notably, biochar, afforestation/reforestation, and soil carbon sequestration are among the most researched CDR options, with direct air carbon capture and storage, bioenergy carbon capture and storage, and blue carbon also receiving substantial attention, especially in 2023. Analysis of scientific funding patterns aligns with these trends. Based on these findings, the study proposes a knowledge roadmap to elucidate emerging trends in CDR literature, offering insights for future research and policy development.
The microhardness, morphology and tribological property of TC4 subjected to machine hammer peening
Authors: Zhang Xuewen , Jing Hongrui , Yang Ying , Zhong Zexin , Li Xiang , Liu Yu 
Published: August 2024

Abstract
Ti alloy has become an indispensable material in harsh working environments due to its excellent wear resistance, corrosion resistance and impact resistance. These environments place stringent requirements on the fatigue life, friction and wear, and surface strength of materials. Therefore, this study aims to deeply explore the surface mechanical properties of Ti-6Al-4V (TC4) alloy after machine hammer peening (MHP) treatment. By conducting experiments on TC4 alloys treated with different hammering energies, the changes in surface hardness, morphology, roughness, tensile properties, and friction properties were analyzed, and the strengthening mechanism of MHP was discussed. Experimental results show that MHP treatment can effectively improve the surface properties of TC4. With the increase of hammer energy, due to the double-sided hammering of the plate, the microhardness first increases, then decreases, and then rises again. The tensile strength and yield strength of the sample increase slightly, but the elongation at break decreases. The surface roughness of the sample increases first and then decreases. Similarly, after MHP treatment, the wear resistance of the samples increased, the friction coefficient decreased, and the wear amount and surface height difference showed a trend of first increasing and then decreasing. The wear mechanism mainly included adhesive wear, abrasive wear and fatigue wear.
A review on machine learning-guided design of energy materials
Authors: Kim Seongmin , Xu Jiaxin , Shang Wenjie , Xu Zhihao , Lee Eungkyu , Luo Tengfei 
Published: August 2024

Abstract
The development and design of energy materials are essential for improving the efficiency, sustainability, and durability of energy systems to address climate change issues. However, optimizing and developing energy materials can be challenging due to large and complex search spaces. With the advancements in computational power and algorithms over the past decade, machine learning (ML) techniques are being widely applied in various industrial and research areas for different purposes. The energy material community has increasingly leveraged ML to accelerate property predictions and design processes. This article aims to provide a comprehensive review of research in different energy material fields that employ ML techniques. It begins with foundational concepts and a broad overview of ML applications in energy material research, followed by examples of successful ML applications in energy material design. We also discuss the current challenges of ML in energy material design and our perspectives. Our viewpoint is that ML will be an integral component of energy materials research, but data scarcity, lack of tailored ML algorithms, and challenges in experimentally realizing ML-predicted candidates are major barriers that still need to be overcome.
Study on mechanical properties of unsaturated silt in the Yellow River flood area
Authors: Song Da , Sun Yun , Sun Shuxuan , Zhou Pengyuan 
Published: August 2024

Abstract
The alluvial deposits of the Yellow River led to the formation of less clay content, loose soil particles, and low compactness of the unsaturated silt, which makes it complicated to determine the mechanical properties of the Yellow River flood area soil. To investigate the mechanical characteristics of unsaturated silt, the relationship between unsaturated silt saturation and matric suction is analyzed through the soil-water characteristic test, and the soil-water characteristic curve is fitted by the Van-Genuchten model. A series of consolidated undrained shear tests were conducted on silt in Kaifeng using the GDS triaxial apparatus, the influence of moisture content, matric suction (u
a-u
w), and net confining pressure (σ
3-u
a) on the strength characteristics of unsaturated silt was discussed. The results indicated that the net confining pressure, moisture content, and matric suction significantly influenced the shear characteristics of unsaturated silt. The shear strength of unsaturated silt increased with increasing net confining pressure at the same moisture content. The strain-hardening state of silt at high confining pressure was more obvious, while the silt at low confining pressure showed a weak strain-hardening state. Under a constant net confining pressure, the deviatoric stress versus deviatoric strain curves of silt with low moisture content exhibited a strain-softening state, while other moisture content showed a weak strain-hardening state. At the same matric suction, the shear strength increased with increasing normal stress. The cohesion increased and then decreased while the internal friction angle decreased and then increased with the increasing moisture content and matric suction.
Development of a system that changes the speed of the motor for a given radius of curvature of an autonomous car while taking turn
Authors: Chandramouli R , Ghosh Rajesh , T Mahitha , K Siva Gangadhar , K Uday , B Hymavathi , G Sai Rakesh 
Published: August 2024

Abstract
This work endeavors to create a system capable of autonomously regulating a car’s speed while navigating turns, based on the curvature of the road. The primary objective is to bolster the safety and stability of autonomous vehicles by curbing excessive speeds during turns, thereby mitigating the risk of accidents such as loss of control or rollovers. The approach blends theoretical analysis with practical application, involving computations to ascertain the ideal speed for a given curve radius. Factors such as vehicle mass, wheel specifications, and centrifugal and gyroscopic forces are considered in these calculations. Subsequently, an algorithm is devised and implemented using Arduino Uno and programmed within the Arduino IDE software environment. The system employs sensors to monitor the motor’s speed and adjust the motor’s speed accordingly. Both simulated scenarios and real-world experiments validate the system’s efficacy in maintaining safe speeds during turns. The study concludes by underlining the potential of this system to heighten the safety and dependability of autonomous vehicles, while also proposing future avenues for research, such as integration with broader autonomous driving frameworks and field testing across diverse environmental conditions.
Accelerating scientific discovery with generative knowledge extraction, graph-based representation, and multimodal intelligent graph reasoning
Authors: Buehler Markus J 
Published: August 2024

Abstract
Leveraging generative Artificial Intelligence (AI), we have transformed a dataset comprising 1000 scientific papers focused on biological materials into a comprehensive ontological knowledge graph. Through an in-depth structural analysis of this graph, we have calculated node degrees, identified communities along with their connectivities, and evaluated clustering coefficients and betweenness centrality of pivotal nodes, uncovering fascinating knowledge architectures. We find that the graph has an inherently scale-free nature, shows a high level of connectedness, and can be used as a rich source for downstream graph reasoning by taking advantage of transitive and isomorphic properties to reveal insights into unprecedented interdisciplinary relationships that can be used to answer queries, identify gaps in knowledge, propose never-before-seen material designs, and predict material behaviors. Using a large language embedding model we compute deep node representations and use combinatorial node similarity ranking to develop a path sampling strategy that allows us to link dissimilar concepts that have previously not been related. One comparison revealed detailed structural parallels between biological materials and Beethoven’s 9th Symphony, highlighting shared patterns of complexity through isomorphic mapping. In another example, the algorithm proposed an innovative hierarchical mycelium-based composite based on integrating path sampling with principles extracted from Kandinsky’s ‘Composition VII’ painting. The resulting material integrates an innovative set of concepts that include a balance of chaos and order, adjustable porosity, mechanical strength, and complex patterned chemical functionalization. We uncover other isomorphisms across science, technology and art, revealing a nuanced ontology of immanence that reveal a context-dependent heterarchical interplay of constituents. Because our method transcends established disciplinary boundaries through diverse data modalities (graphs, images, text, numerical data, etc), graph-based generative AI achieves a far higher degree of novelty, explorative capacity, and technical detail, than conventional approaches and establishes a widely useful framework for innovation by revealing hidden connections.
Improving model robustness to weight noise via consistency regularization
Authors: Hou Yaoqi , Zhang Qingtian , Wang Namin , Wu Huaqiang 
Published: August 2024

Abstract
As an emerging computing architecture, the computing-in-memory (CIM) exhibits significant potential for energy efficiency and computing power in artificial intelligence applications. However, the intrinsic non-idealities of CIM devices, manifesting as random interference on the weights of neural network, may significantly impact the inference accuracy. In this paper, we propose a novel training algorithm designed to mitigate the impact of weight noise. The algorithm strategically minimizes cross-entropy loss while concurrently refining the feature representations in intermediate layers to emulate those of an ideal, noise-free network. This dual-objective approach not only preserves the accuracy of the neural network but also enhances its robustness against noise-induced degradation. Empirical validation across several benchmark datasets confirms that our algorithm sets a new benchmark for accuracy in CIM-enabled neural network applications. Compared to the most commonly used forward noise training methods, our approach yields approximately a 2% accuracy boost on the ResNet32 model with the CIFAR-10 dataset and a weight noise scale of 0.2, and achieves a minimum performance gain of 1% on ResNet18 with the ImageNet dataset under the same noise quantization conditions.
From ‘follow the leader’ to autonomous swarming: physical reservoir computing in two dimensions
Authors: E Heywood Zachary , B Mallinson Joshua , J Bones Philip , A Brown Simon 
Published: August 2024

Abstract
Percolating networks of nanoparticles (PNNs) are self-assembled nanoscale systems that possess brain-like characteristics that are useful for information processing, particularly within a reservoir computing (RC) framework. Previous work has successfully demonstrated one-dimensional RC tasks, such as chaotic time-series prediction and nonlinear transformation. We focus here on the challenge of two-dimensional (2D) tasks and introduce novel ‘follow the leader’ and ‘swarming’ tasks. In the first task a ‘follower’ is required to accurately track a ‘leader’ in two dimensions. The task is performed successfully for a range of trajectories and parameters, for both position-based tracking and velocity-based tracking incorporating inertia. In both cases, the task is successful even for trajectories unseen in training. We then successfully demonstrate a 2D implementation of swarming behavior. Each agent is represented by a PNN which is trained to react to the behavior of the other members of the swarm, such that the future trajectory of all agents is generated autonomously. As well as demonstrating that the computational capabilities of PNNs can be extended into two dimensions, this work presents a first step in the emulation of complex emergent biological behaviors such as swarming, and opens a new route to the solution of complex optimization problems.