bluuebunny/crossref_metadata_2025_split huggingface dataset:
Parametric continuation analysis of heat and mass transfer of Ethylene Glycol nanofluid flow between two vertical plates
Authors: Ahmad Adeel , Shuaib Muhammad , Rehman Hijab ur 
Published: December 2024

Abstract
This study examines the behavior of nanofluid with three nano-particles namely ZnO
2, TiO
2 and graphene with base fluid as Ethylene glycol trapped between two vertical plates, with particular attention to the temperature-dependent electrical and magnetic conductivity are affected by the Soret and Dufour effects. The governing equations for fluid flow are transformed into dimensionless ordinary differential equations using the Adeosun and Ukaegbu method. The Parametric Continuation Method (PCM) is then employed to analyze the numerical results. The numerical results obtained using the (PCM) method are graphically validated against the solutions provided by the bvp4c package, showing excellent agreement between them. Additionally, numerical results of the (PCM) method are also compared with previously published studies and about two decimal places accuracy is reported. It is found that ZnO
2 is more effective in enhancing the nanofluids temperature. The temperature of fluid usning ZnO
2, TiO
2 and graphene is 1.2985, 0.6932 and 0.4834 respectively. While graphene has minor contribution in upsurging temperature of fluid. So ZnO
2 can be used in various mechanical and electrical appliances to upsurge its temperature according to our needs. Graphene plays major role in enhancing fluids concentration and ZnO
2 has less effect on concentration of nanofluid. Using graphene the concentration of fluid is 1.6834, while using ZnO
2 its concentration is 1.2985.
A novel asymmetric encryption framework based on a 2D hyperchaotic map and enhanced S-box for secure medical image transmission
Authors: Tang Jianeng , Lu Mingsheng , Zhang Zezong 
Published: December 2024

Abstract
Recently, the advent of Internet of Medical Things (IoMT) has effectively alleviated the problem of difficulty in accessing medical services. However, during telemedicine, various medical images containing sensitive private information are exposed in communication channels. Therefore, there is an immediate need for an effective encryption method to ensure the secure transmission of medical images. In this paper, an image encryption algorithm is proposed based on a new chaotic model and an enhanced S-box. Furthermore, the proposed encryption algorithm is applied within a novel asymmetric image encryption framework. Firstly, to address the problems of narrow chaotic intervals and uneven trajectory distribution present in some existing chaotic maps, a two-dimensional cross-sine-modular model (2D-CSMM) is constructed. Secondly, in conjunction with the DNA algorithm, an enhanced S-box is proposed. Finally, in order to effectively protect key transmission as well as to enhance the system's resistance against chosen plaintext attacks, a novel asymmetric image encryption framework is designed by integrating the Elliptic Curve Diffie-Hellman (ECDH), Elliptic Curve Cryptography (ECC) and SHA-256 functions. And the secret key is computed from the cipher key, the shared key, and the native key associated with the plain image. Comprehensive experimental results demonstrate the high efficiency and the resilience of this new algorithm against common attacks.
Coherence and imaginarity of quantum states
Authors: Xu Jianwei 
Published: December 2024

Abstract
Baumgratz, Cramer and Plenio established a rigorous framework (BCP framework) for quantifying the coherence of quantum states (2014, Phys. Rev. Lett.
113, 140401). In BCP framework, a quantum state is called incoherent if it is diagonal in the fixed orthonormal basis, and a coherence measure should satisfy some conditions. For a fixed orthonormal basis, if a quantum state ρ has nonzero imaginary part, then ρ must be coherent. How to quantitatively characterize this fact? In this work, we show that any coherence measure C in BCP framework has the property C(ρ) − C(Reρ)≥0 if C is invariant under state complex conjugation, i.e. C(ρ) = C(ρ*), here ρ*is the conjugate of ρ, Reρis the real part of ρ. If C does not satisfy C(ρ) = C(ρ*), we can define a new coherence measure

C

′

(
ρ
)
=

1

2

[
C
(
ρ
)
+
C
(

ρ

*

)
]

such that

C

′

(
ρ
)
=

C

′

(
ρ
*
)
.

We also establish some similar results for bosonic Gaussian states.
Quantum sensitivity of a photon-added molecular wave packet
Authors: Das Jayashree , Ghosh Suranjana , Roy Utpal 
Published: December 2024

Abstract
We study the temporal dynamics of a photon-added SU(2) coherent state, involving vibrational bound states of an iodine molecule. The nonclassicality of Schrödinger cat-like and compass-like photon-added states of this anharmonic potential is explored through the Mandel Q parameter and the negative region in their phase spaces. The Wigner phase space distribution is thoroughly studied for the photon-added molecular state and displayed for cat- and compass-like states. Generation and control of the sub-Planck interference structures become intriguing with photon addition. We ensure that the photon addition helps to improve the quantum sensitivity for a diatomic molecular system by comparing it between photon-added and photon-subtracted states. The detailed study of quantum sensitivity reveals that one can use a photon-added molecular wave packet as a probe to attain sensitivity to displacement that greatly exceeds the standard quantum limit. We also report that the nature of variation of the quantum sensitivity becomes qualitatively commensurate with that of the Wigner-negativity.
Characterization of nonlinear spectral linewidth and light shift in diffuse laser-cooled atoms
Authors: Yin 殷 Yiran 怡然 , Xu 许 An-Ning 安宁 , Peng 彭 Jin 瑾 , Liu 刘 Bei 贝 
Published: December 2024

Abstract
We demonstrate an integrating sphere to cool 87Rb atoms and measure the recoil-induced resonance and electromagnetically induced absorption spectrum. We measure the relationship between their linewidth and light shift with variation of the detuning and power of the cooling laser and study the performance of the diffuse laser cooling mechanism by the absorption linewidth radio Δν
E/Δν
R and light shift |Δ
R–Δ
E| using nonlinear spectroscopy. Specifically, when Δν
E/Δν
R reaches a value of 1.57, the temperature and number of cold atoms achieve the optimal cooling effect. This characterization of absorption linewidth and light shift will provide a method to estimate whether diffuse light cooling achieves the best cooling effect, contributing to the future development of isotropic laser cooling for application in quantum sensing.
Relativistic hyperpolarizabilities of atomic H, Li, and Be+ systems
Authors: Lu 卢 Shan-Shan 闪闪 , Zheng 郑 Hong-Yuan 弘远 , Yan 严 Zong-Chao 宗朝 , Babb James F. , Tang 唐 Li-Yan 丽艳 
Published: December 2024

Abstract
For atoms in external electric fields, the hyperpolarizabilities are the coefficients describing the nonlinear interactions contributing to the induced energies at the fourth power of the applied electric fields. Accurate evaluations of these coefficients for various systems are crucial for improving precision in advanced atom-based optical lattice clocks and for estimating field-induced effects in atoms for quantum information applications. However, there is a notable scarcity of research on atomic hyperpolarizabilities, especially in the relativistic realm. Our work addresses this gap by establishing a novel set of alternative formulas for the hyperpolarizability based on the fourth-order perturbation theory. These formulas offer a more reasonable regrouping of scalar and tensor components compared to previous formulas, thereby enhancing their correctness and applicability. To validate our formulas, we perform the calculations for the ground and low-lying excited pure states of few-electron atoms H, Li, and Be+. The highly accurate results obtained for the H atom could serve as benchmarks for further development of other theoretical methods.
Model-agnostic meta-learning for EEG-based inter-subject emotion recognition
Authors: Chen Cheng , Fang Hao , Yang Yuxiao , Zhou Yi 
Published: December 2024

Abstract

Objective. Developing an efficient and generalizable method for inter-subject emotion recognition from neural signals is an emerging and challenging problem in affective computing. In particular, human subjects usually have heterogeneous neural signal characteristics and variable emotional activities that challenge the existing recognition algorithms from achieving high inter-subject emotion recognition accuracy. Approach. In this work, we propose a model-agnostic meta-learning algorithm to learn an adaptable and generalizable electroencephalogram-based emotion decoder at the subject’s population level. Different from many prior end-to-end emotion recognition algorithms, our learning algorithms include a pre-training step and an adaptation step. Specifically, our meta-decoder first learns on diverse known subjects and then further adapts it to unknown subjects with one-shot adaptation. More importantly, our algorithm is compatible with a variety of mainstream machine learning decoders for emotion recognition. Main results. We evaluate the adapted decoders obtained by our proposed algorithm on three Emotion-EEG datasets: SEED, DEAP, and DREAMER. Our comprehensive experimental results show that the adapted meta-emotion decoder achieves state-of-the-art inter-subject emotion recognition accuracy and outperforms the classical supervised learning baseline across different decoder architectures. Significance. Our results hold promise to incorporate the proposed meta-learning emotion recognition algorithm to effectively improve the inter-subject generalizability in designing future affective brain–computer interfaces.
The ‘Sandwich’ meta-framework for architecture agnostic deep privacy-preserving transfer learning for non-invasive brainwave decoding
Authors: Wei Xiaoxi , Narayan Jyotindra , Faisal A Aldo 
Published: December 2024

Abstract

Objective. Machine learning has enhanced the performance of decoding signals indicating human behaviour. Electroencephalography (EEG) brainwave decoding, as an exemplar indicating neural activity and human thoughts non-invasively, has been helpful in neural activity analysis and aiding paralysed patients via brain-computer interfaces. However, training machine learning algorithms on EEG encounters two primary challenges: variability across data sets and privacy concerns using data from individuals and data centres. Our objective is to address these challenges by integrating transfer learning for data variability and federated learning for data privacy into a unified approach. Approach. We introduce the ‘Sandwich’ as a novel deep privacy-preserving meta-framework combining transfer learning and federated learning. The ‘Sandwich’ framework comprises three components: federated networks (first layers) that handle data set differences at the input level, a shared network (middle layer) learning common rules and applying transfer learning techniques, and individual classifiers (final layers) for specific brain tasks of each data set. This structure enables the central network (central server) to benefit from multiple data sets, while local branches (local servers) maintain data and label privacy. Main results. We evaluated the ‘Sandwich’ meta-architecture in various configurations using the BEETL motor imagery challenge, a benchmark for heterogeneous EEG data sets. Compared with baseline models like Shallow ConvNet and EEGInception, our ‘Sandwich’ implementations showed superior performance. The best-performing model, the Inception SanDwich with deep set alignment (Inception-SD-Deepset), exceeded baseline methods by 9%. Significance. The ‘Sandwich’ framework demonstrates advancements in federated deep transfer learning for diverse tasks and data sets. It outperforms conventional deep learning methods, showcasing the potential for effective use of larger, heterogeneous data sets with enhanced privacy. In addition, through its diverse implementations with various backbone architectures and transfer learning approaches, the ‘Sandwich’ framework shows the potential as a model-agnostic meta-framework for decoding time series data like EEG, suggesting a direction towards large-scale brainwave decoding by combining deep transfer learning with privacy-preserving federated learning.
Stimulation artefact removal: review and evaluation of applications in evoked responses
Authors: Zhang Tianshu , Lawson Kiaran , Lee Wee-Lih , Petoe Matthew , Moorhead Ashton , Bulluss Kristian , Thevathasan Wesley , McDermott Hugh , Perera Thushara 
Published: December 2024

Abstract

Objective. This study investigated software methods for removing stimulation artefacts in recordings undertaken during deep brain stimulation (DBS). We aimed to evaluate artefact attenuation using sample recordings of evoked resonant neural activity (ERNA), as well as a synthetic ground-truth waveform that emulated observed ERNA characteristics. Approach. The synthetic waveform and eight raw DBS recordings were processed by fourteen algorithms spanning the following categories: signal modification, signal decomposition, and template subtraction. For the synthetic waveform, performance was quantified by comparing each reconstructed signal against the ground-truth waveform. For DBS recordings, performance was contrasted amongst each other. The stimulation artefact was quantified by its amplitude and subsequent decay to baseline by the time to first zero-crossing. Each reconstructed ERNA signal was characterised by peak-to-peak-amplitude, root-mean-square amplitude, latency, and number of zero-crossings. Main results. None of the methods performed overall as well as the Backward Filter. Signal decomposition techniques were able to attenuate stimulation artefact albeit with unacceptable ERNA distortion. Significance. Upon evaluation of common software methods for DBS artefact attenuation, we advocate the use of the Backward Filter for reducing such artefacts while reconstructing ERNA.
EKFNet: edge-based Kalman filter network for real-time EEG signal denoising
Authors: Yan Jiaquan , He Zhuoli , Ur Rehman Junejo Naveed , Li Zuoyong , Grau Antoni , Huang Jiayan , Wang Chuansheng 
Published: December 2024

Abstract

Objective. Signal denoising methods based on deep learning have been extensively adopted on electroencephalogram devices. However, they are unable to deploy on edge-based portable or wearable (P/W) electronics due to the high computational complexity of the existed models. To overcome such issue, we propose an edge-based lightweight Kalman filter network (EKFNet) that does not require manual prior knowledge estimation. Approach. Specifically, we construct a multi-scale feature fusion module to capture multi-scale feature information and implicitly compute the prior knowledge. Meanwhile, we design an adaptive gain estimation module that incorporates long short-term memory and sequential channel attention module to dynamically predict the Kalman gain. Furthermore, we present an optimization strategy utilizing operator fusion and constant folding to reduce the model’s computational overhead and memory footprint. Main results. Experimental results show that the EKFNet reduces the sum of the square of the distances by at least 12% and improves the cosine similarity by at least 2.2% over the state-of-the-art methods. Besides, the model optimization shortens the inference time by approximately 3.3×. The code of our EKFNet is available at https://github.com/cathnat/EKFNet. Significance. By integrating Kalman filter with deep learning, the approach addresses the parameter-setting challenges in traditional algorithms while reducing computational overhead and memory consumption, which exhibits a good tradeoff between algorithm performance and computing power.
Nanoparticle targeting strategies for traumatic brain injury
Authors: Flores-Prieto David E , Stabenfeldt Sarah E 
Published: December 2024

Abstract
Nanoparticle (NP)-based drug delivery systems hold immense potential for targeted therapy and diagnosis of neurological disorders, overcoming the limitations of conventional treatment modalities. This review explores the design considerations and functionalization strategies of NPs for precise targeting of the brain and central nervous system. This review discusses the challenges associated with drug delivery to the brain, including the blood–brain barrier and the complex heterogeneity of traumatic brain injury. We also examine the physicochemical properties of NPs, emphasizing the role of size, shape, and surface characteristics in their interactions with biological barriers and cellular uptake mechanisms. The review concludes by exploring the options of targeting ligands designed to augment NP affinity and retention to specific brain regions or cell types. Various targeting ligands are discussed for their ability to mimic receptor-ligand interaction, and brain-specific extracellular matrix components. Strategies to mimic viral mechanisms to increase uptake are discussed. Finally, the emergence of antibody, antibody fragments, and antibody mimicking peptides are discussed as promising targeting strategies. By integrating insights from these scientific fields, this review provides an understanding of NP-based targeting strategies for personalized medicine approaches to neurological disorders. The design considerations discussed here pave the way for the development of NP platforms with enhanced therapeutic efficacy and minimized off-target effects, ultimately advancing the field of neural engineering.
Electroencephalographic power ratio and peak frequency difference associate with central sensitization in chronic pain
Authors: Ding Keqin , Arginteanu Toren , Anderson White Mirinda , Lovell Lyndah , Thakor Nitish V , Doshi Tina 
Published: December 2024

Abstract

Objective. Central sensitization, or increased responsiveness of the central nervous system to sensory input, is present in many chronic pain patients. Clinically, it is detected through subjective, patient-reported measures. There is a need for reliable, direct measurements of neural response to controlled stimuli to quantify neuronal dysfunction in pain. The goal of this work is to investigate cortical activity, recorded via electroencephalogram (EEG), during objective and calibrated painful stimulation in chronic pain patients. Approach. Chronic pain patients (N = 8) and healthy controls (N = 8) participated in this study. We recorded electroencephalography (EEG) at rest (baseline) and during evoked pain tasks, including thermal and mechanical stimuli. The evoked pain was applied following the quantitative sensory testing (QST) protocol, which is a research technique that applies objective, calibrated painful stimuli. Main results. Peak alpha frequency at rest was significantly lower in chronic pain patients compared to healthy controls (p < 0.0002), while EEG alpha/theta and alpha/beta power ratios at rest were higher in patients (p < 0.0002). During thermal QST, these power ratios decreased in patients and increased in controls (p < 0.0002 for both). During mechanical QST, power ratios decreased or did not change. Furthermore, the peak theta–beta frequency difference at baseline was significantly lower in patients compared to controls (p < 0.0002). During thermal QST, this difference increased in patients and decreased in controls; during mechanical QST, this difference increased in both patients and controls (p < 0.0002). Functional connectivity analysis showed that controls had greater baseline theta connectivity strength that increased during mechanical QST (p < 0.0002). Significance. This work demonstrates differential patterns of EEG activity at rest and during acute painful stimulation in chronic pain patients compared to healthy controls. These measures may quantify an individual’s tendency to experience chronic pain and central sensitization and serve as diagnostic biomarkers.
Characteristics of plasma disruption mitigation achieved by MGI and SPI on EAST
Authors: Zhao S.B. , Yuan J.S. , Zhuang H.D. , Zuo G.Z. , Li L. , Tang T. , Zeng L. , Duan Y.M. , Shi T.H. , Mao S.T. , Chen D.L. , Huang M. , Chen Y. , Hu J.S. 
Published: December 2024

Abstract
This study systematically compares the influence of shattered pellet injection (SPI) and massive gas injection (MGI) on plasma disruption mitigation within the Experimental Advanced Superconducting Tokamak. The results reveal that SPI demonstrates significant advantages over MGI in plasma disruption mitigation, as it predominantly deposits impurities within the plasma core. This leads to more rapid emission of thermal radiation and a significantly shorter total disruption duration compared to MGI. Conversely, MGI primarily deposits impurities at the plasma edge, and its impurity penetration duration is longer compared to that of SPI. During the current quench phase, MGI displays an evident radiation tail extending from the plasma core to its edge, accompanied by a second current spike. These phenomena are primarily attributed to cold vertical displacement events, which cause the plasma to directly contact the first wall, thereby generating halo currents and emitting hard x-rays. Furthermore, both SPI and MGI exhibit clear magnetohydrodynamic (MHD) mode switching, wherein the inherent n = 1 and n = 2 modes transition to a new n = 1 mode. This new mode features a reversed rotation direction and is accompanied by a burst of soft x-rays from the plasma core. This observation suggests that the observed MHD mode switching is driven by impurity‒plasma interactions rather than the impurity injection method. Future research endeavors must focus on high-resolution diagnostics and further experimentation to better understand the impacts of impurities on MHD modes. Overall, this study provides crucial data support for improving plasma disruption mitigation strategies for ITER and other future fusion reactors.
Comment on ‘Cropland abandonment in the context of drought, economic restructuring, and migration in Northeast Syria’
Authors: Ash Konstantin , Trinh Duy 
Published: December 2024

Abstract
An article recently published in Environmental Research Letters made available data on land abandonment in Syria before the onset of the Syrian civil war. The article claims that land abandonment was driven by mismanagement of irrigation through neoliberal economic policies before the 2006–10 Syrian drought and that agriculture quickly recovered after the drought. We use inferential statistics to test the authors’ claims. We find that before the drought, land abandonment occurred in areas with more rain-fed agriculture and was associated with population growth in sub-districts with more urban land cover. This finding suggests that pre-drought land abandonment was driven by increased urban development, rather than water mismanagement and irrigation policy reform. After drought onset, we find climatic stress caused first-year land abandonment, consistent with the authors’ claims. We also find third-year land abandonment is associated with population decline through 2010, going against the claim of a quick recovery. Our evidence suggests that the drought interrupted a long-term trend of decreasing agricultural use related to local urban expansion, bringing about a new trend of agricultural abandonment as a result of climate-induced out-migration. We find no evidence that land abandonment is associated with neoliberal policy reforms, or that there was a recovery when the drought ended.