bluuebunny/crossref_metadata_2025_split huggingface dataset:
Northern and Southern Hemisphere Polar Cap Indices: To What Extent Do They Agree and to What Extent Should They Agree?
Authors: Lockwood M. 
Published: June 2023

AbstractThe IAGA‐endorsed Polar Cap Indices for the northern and southern hemispheres, PCN and PCS, are compared for 1998–2018, inclusive. Potential effects of the slightly different, and changing, magnetic coordinates of the two magnetic stations employed, Thule (Qaanaaq) in Greenland and Vostok in Antarctica, are investigated. It is shown that the agreement in overall behavior of the two indices is very close indeed but that PCS consistently correlates slightly better with solar wind parameters than PCN. Optimum lags for these correlations are 19 min for 1‐min data and 37 min for hourly averages. The correlations are significantly highest for the predicted magnetopause reconnection voltage, which is a linear predictor of PCN and PCS for all 1‐hr data and for all but the largest 0.1% of 1‐min values. The indices show lower correlation and marked non‐linearity (tending to saturation) at all levels with the estimated magnetopause reconnection electric field or the estimated power input into the magnetosphere. The PCN index is shown to correlate closely with the transpolar voltage measured by the northern‐hemisphere SuperDARN radar network and both PCN and PCS clearly show the Russell‐McPherron effect of dipole tilt and the Y‐component of the interplanetary magnetic field. However the patterns in time‐of‐year and Universal Time (UT) are complicated by lobe reconnection during northward‐IMF, the effect of which on the indices is shown to be predominantly a summer hemisphere phenomenon and gives a UT dependence on the IMF Y‐component that is predicted theoretically.
Broadband picometer-scale resolution on-chip spectrometer with reconfigurable photonics
Authors: Yao Chunhui , Chen Minjia , Yan Ting , Ming Liang , Cheng Qixiang , Penty Richard 
Published: June 2023

AbstractMiniaturization of optical spectrometers is important to enable spectroscopic analysis to play a role in in situ, or even in vitro and in vivo characterization systems. However, scaled-down spectrometers generally exhibit a strong trade-off between spectral resolution and operating bandwidth, and are often engineered to identify signature spectral peaks only for specific applications. In this paper, we propose and demonstrate a novel global sampling strategy with distributed filters for generating ultra-broadband pseudo-random spectral responses. The geometry of all-pass ring filters is tailored to ensure small self- and cross-correlation for effective information acquisition across the whole spectrum, which dramatically reduces the requirement on sampling channels. We employ the power of reconfigurable photonics in spectrum shaping by embedding the engineered distributed filters. Using a moderate mesh of MZIs, we create 256 diverse spectral responses on a single chip and demonstrate a resolution of 20 pm for single spectral lines and 30 pm for dual spectral lines over a broad bandwidth of 115 nm, to the best of our knowledge achieving a new record of bandwidth-to-resolution ratio. Rigorous simulations reveal that this design will readily be able to achieve single-picometer-scale resolution. We further show that the reconfigurable photonics provides an extra degree of programmability, enabling user-defined features on resolution, computation complexity, and relative error. The use of SiN integration platform enables the spectrometer to exhibit excellent thermal stability of ±2.0 °C, effectively tackling the challenge of temperature variations at picometer-scale resolutions.
Predictors and motives for mask-wearing behavior and vaccination intention
Authors: Binter Jakub , Pešout Ondra , Pieniak Michał , Martínez-Molina Judit , Noon Edward J. , Stefanczyk Michal M. , Eder Stephanie J. 
Published: June 2023

AbstractContaining a pandemic requires that individuals adhere to measures such as wearing face-masks and getting vaccinated. Therefore, identifying predictors and motives for both behaviors is of importance. Here, we study the decisions made by a cross-national sample in randomized hypothetical scenarios during the COVID-19 pandemic. Our results show that mask-wearing was predicted by empathic tendencies, germ aversion, and higher age, whilst belief in misinformation and presentation of an interaction partner as a family member lowered the safety standards. The main motives associated with taking the mask off included: rationalization, facilitating interaction, and comfort. Vaccination intention was positively predicted by empathy, and negatively predicted by belief in misinformation and higher costs of the vaccine. We found no effect of immunization status of the surrounding social group. The most common motive for vaccination was protection of oneself and others, whereas undecided and anti-vaccine groups reported doubts about the effectiveness and fear of side effects. Together, we identify social and psychological predictors and motives of mask-wearing behavior and vaccination intention. The results highlight the importance of social context for mask-wearing, easy access to vaccines, empathy, and trust in publicly distributed information.
A novel CRISPR/Cas9-based iduronate-2-sulfatase (IDS) knockout human neuronal cell line reveals earliest pathological changes
Authors: Badenetti Lorenzo , Manzoli Rosa , Trevisan Marta , D’Avanzo Francesca , Tomanin Rosella , Moro Enrico 
Published: June 2023

AbstractMultiple complex intracellular cascades contributing to Hunter syndrome (mucopolysaccharidosis type II) pathogenesis have been recognized and documented in the past years. However, the hierarchy of early cellular abnormalities leading to irreversible neuronal damage is far from being completely understood. To tackle this issue, we have generated two novel iduronate-2-sulfatase (IDS) loss of function human neuronal cell lines by means of genome editing. We show that both neuronal cell lines exhibit no enzymatic activity and increased GAG storage despite a completely different genotype. At a cellular level, they display reduced differentiation, significantly decreased LAMP1 and RAB7 protein levels, impaired lysosomal acidification and increased lipid storage. Moreover, one of the two clones is characterized by a marked decrease of the autophagic marker p62, while none of the two mutants exhibit marked oxidative stress and mitochondrial morphological changes. Based on our preliminary findings, we hypothesize that neuronal differentiation might be significantly affected by IDS functional impairment.
Relationships between local variability in parasite communities of the black-spotted croaker (Protonibea diacanthus) (Teleostei: Sciaenidae) and host population structure and seasonality
Authors: Porter Megan , Barton Diane P. , Hearnden Mark , Randall Jo , Crook David A. , Shamsi Shokoofeh 
Published: June 2023

AbstractWe evaluated spatial and temporal variability in parasite communities from the commercially important tropical marine fish the black-spotted croaker (Protonibea diacanthus) (Teleostei: Sciaenidae) to examine its population structure off the coast of the Northern Territory, Australia. Differences in parasite assemblage between four locations, across three seasons of the year, were used to evaluate the degree of connectivity of the sciaenid across coastal study areas. Analysis of parasite prevalence and mean intensity in these fish suggested the four sampling sites are distinct host populations. Across time, parasite assemblages at the four sites were distinct during the mid-dry (April–August) and build-up (September–November) seasons. During the wet season (December–March) there was substantial overlap in the parasite assemblages at three of the four sites indicating that fish population mixing may be occurring. Parasite assemblages at one nearshore site remained distinct across spatial and temporal scales. Our findings support the utility of parasitic organisms for elucidating the population structure of host species and reiterate the need to account for both spatial and temporal variability when performing stock discrimination analyses.
Frequency-domain interferometry for the determination of time delay between two extreme-ultraviolet wave packets generated by a tandem undulator
Authors: Hikosaka Y. , Kaneyasu T. , Wada S. , Kohguchi H. , Ota H. , Nakamura E. , Iwayama H. , Fujimoto M. , Hosaka M. , Katoh M. 
Published: June 2023

AbstractSynchrotron radiation, emitted by relativistic electrons traveling in a magnetic field, has poor temporal coherence. However, recent research has proved that time-domain interferometry experiments, which were thought to be enabled by only lasers of excellent temporal coherence, can be implemented with synchrotron radiation using a tandem undulator. The radiation generated by the tandem undulator comprises pairs of light wave packets, and the longitudinal coherence within a light wave packet pair is used to achieve time-domain interferometry. The time delay between two light wave packets, formed by a chicane for the electron trajectory, can be adjusted in the femtosecond range by a standard synchrotron technology. In this study, we show that frequency-domain spectra of the tandem undulator radiation exhibit fringe structures from which the time delay between a light wave packet pair can be determined with accuracy on the order of attoseconds. The feasibility and limitations of the frequency-domain interferometric determination of the time delay are examined.
Attribute‐guided transformer for robust person re‐identification
Authors: Wang Zhe , Wang Jun , Xing Junliang 
Published: June 2023

AbstractRecent studies reveal the crucial role of local features in learning robust and discriminative representations for person re‐identification (Re‐ID). Existing approaches typically rely on external tasks, for example, semantic segmentation, or pose estimation, to locate identifiable parts of given images. However, they heuristically utilise the predictions from off‐the‐shelf models, which may be sub‐optimal in terms of both local partition and computational efficiency. They also ignore the mutual information with other inputs, which weakens the representation capabilities of local features. In this study, the authors put forward a novel Attribute‐guided Transformer (AiT), which explicitly exploits pedestrian attributes as semantic priors for discriminative representation learning. Specifically, the authors first introduce an attribute learning process, which generates a set of attention maps highlighting the informative parts of pedestrian images. Then, the authors design a Feature Diffusion Module (FDM) to iteratively inject attribute information into global feature maps, aiming at suppressing unnecessary noise and inferring attribute‐aware representations. Last, the authors propose a Feature Aggregation Module (FAM) to exploit mutual information for aggregating attribute characteristics from different images, enhancing the representation capabilities of feature embedding. Extensive experiments demonstrate the superiority of our AiT in learning robust and discriminative representations. As a result, the authors achieve competitive performance with state‐of‐the‐art methods on several challenging benchmarks without any bells and whistles.
Cross‐coupling control strategy based on intelligent adaptive control for cross‐shaped dual‐axis linear motors
Authors: Li Zheng , Wang Kang tao , Wang Jin song , Zhang Zi hao , Guo Xiao qiang , Sun He xu 
Published: June 2023

AbstractIn order to ensure the high precision and good dynamic characteristics of the dual‐axis linear motor when machining components, the tracking error and contour error should be reduced with more complex machining trajectory. In this study, the characteristic model of position control of the linear motor is established by using the modelling principle of the characteristic model, and the intelligent adaptive controller is designed. In addition, a new calculation method of the equivalent contour error is proposed innovatively, and the dual axis is compensated by proportional feedback control, to improve the contour accuracy of the permanent magnet synchronous linear motor dual‐axis control system. Finally, through the simulation and experiment of the dual‐axis motor platform, the effectiveness of the proposed contour error calculation method and the intelligent adaptive controller can make the dual‐axis motion control system have higher accuracy and strong robustness and can also make the motor run more smoothly.
Managing reserve deliverability risk of integrated electricity‐heat systems in day‐ahead market: A distributionally robust joint chance constrained approach
Authors: Chen Yang , Wang Jianxue , Wang Siyuan , Bo Rui , Gu Chenjia , Li Qingtao 
Published: June 2023

AbstractThe integrated electricity and heat system (IEHS) is an emerging demand‐side flexible resource for power systems. IEHS operators participating in electricity markets considering their capabilities in reserve provision will face the reserve deliverability risk due to the energy‐limited storage nature of heat systems. To address this challenge and increase profitability, a distributionally robust joint chance‐constrained mechanism with enhanced quantifications is adopted for the heating system and reserve deployment uncertainties. Detailed pipeline storage representation for thermal networks and integrated demand response are incorporated into this strategic participation model. A two‐stage distributionally robust joint chance constrained program is then incorporated to effectively manage the reserve deliverability risk by addressing uncertainties from local distributed energy resources and real‐time reserve requests. The L‐shaped algorithm is then customized by incorporating bi‐linear Benders’ decomposition and modified scenario filtering method to efficiently tackle solution challenges for the sophisticated model. Numerical results show the advantages of our approach in virtual thermal storage utilization, risk management, computational performance enhancement and scalability.
Finite time adaptive synchronous control for fractional‐order chaotic power systems
Authors: Ai Chunyu , He Shan , Wang Weiqing , Fan XiaoChao 
Published: June 2023

AbstractAs a complex non‐linear system, the chaotic oscillation of power system seriously threatens the safe and stable operation of power grids. In this paper, a finite time adaptive synchronization control method is proposed to mitigate the problem of chaotic oscillation in the power system. The proposed method can realize chaos control and parameter identification by completely synchronizing the fractional‐order chaotic power system with the stable fractional‐order power system to identify parameters within a finite time. The fractional Lyapunov stability theory is used for numerical simulation. Theoretical and simulation results show that this method can effectively stabilize the system in a finite time. It is proved that compared with the adaptive synchronous control method, the control method is simpler in design, shorter in action time, and more meaningful in engineering practice.
Fixed head hydrothermal scheduling considering uncertainty of load demand and renewable energy sources
Authors: Basu Mousumi , Das Saborni , Jena Chitralekha , Ogale Jyotsna , Ali Ahmed , Khan Baseem 
Published: June 2023

AbstractThis manuscript suggests quasi‐oppositional fast convergence evolutionary programming (QOFCEP) technique to determine short‐term generation scheduling of fixed head hydrothermal system comprising pumped storage hydro plants (PSHPs), solar PV plants (SPVPs) and wind turbine generators (WTGs) with demand response program (DRP). PSHPs are used with fixed head hydro power plants (FHPPs), thermal generating units (TGUs), SPVPs and WTGs to operate system conveniently. Uncertainties related to renewable generations and load demands have been considered. Self‐adaptive tent mapped scenarios of uncertain parameters are generated within higher level of confidence space to deal with the system uncertainties. DRP is applied to level the load demand curve and to enhance system flexibility and reduce operational cost. Numerical results of typical test system are matched with those attained by fast convergence evolutionary programming (FCEP) and evolutionary programming (EP).
CoGAN: Cooperatively trained conditional and unconditional GAN for person image generation
Authors: Liu Yang , Sheng Hao , Wang Shuai , Wu Yubin , Xiong Zhang 
Published: June 2023

AbstractPerson image generation aims to synthesize realistic person images that follow the same distribution as the given dataset. Previous attempts can be generally categorized into two classes: conditional GAN and unconditional GAN. The former usually uses pose information as condition to make pose transfer using GAN. The generated person have the same identity as the source person. The latter generates person images from scratch, and the real person images are only used as references for the discriminator. While conditional GAN is widely studied, unconditional GAN is also worth exploring because it can synthesize person image with new identity, which is a useful manner of data augmentation. These two types of generating methods have their different advantages and disadvantages, and sometimes they are complementary. This paper proposes a CoGAN to cooperatively train two types of GANs in an end‐to‐end framework. The two GANs serve different purposes, and can learn from each other during the cooperative learning procedure. The experimental results on public datasets show that the proposed CoGAN improves the performance of both baseline methods, and achieves competitive results compared with state‐of‐the‐art methods.
Exploiting statistical effective fault attack in a blind setting
Authors: Vafaei Navid , Soleimany Hadi , Bagheri Nasour 
Published: June 2023

AbstractIn order to obtain the secret key, the majority of physical attacks require knowledge of the plaintext or ciphertext, which may be unavailable or cannot be exploited. Blind attacks are introduced to do key recovery in circumstances where the adversary has no direct access to plaintext and ciphertext. A combination of fault and power attacks can circumvent typical countermeasures in this setting, for example, Fault Template Attack (FTA). However, FTA relies on bit fault injection, which is difficult to implement in practice. The SIFA‐blind, a framework for executing the Statistical Ineffective Fault Attack, is more flexible, but sensitivity to setup noise and missed faults is its main drawback. To address this deficiency, we suggest two ways to use Statistical Effective Fault Attack in a blind setting that are much less affected by missed faults and noise when measuring power traces, even though they do not use fault injection at the bit level. In order to demonstrate the viability and adaptability of our proposed attacks, we injected a fault via glitch frequency onto the ChipWhisperer board. While SEFA‐blind does not need a bit‐level fault, our results demonstrate that it is better than SIFA‐blind when the number of missed faults increases.
HCl, DCl and TCl diatomic molecules in their ground state: predicting Born–Oppenheimer rovibrational spectra
Authors: Olivares-Pilón Horacio , Turbiner Alexander V 
Published: June 2023

AbstractThe analytic Born–Oppenheimer (B-O) potential curve for the ground stateX1Σ+of the molecule (H,D,T)Cl is constructed for the whole range of internuclear distancesR∈[0,∞)with an accuracy of 3–5 significant figures in comparison with the Rydberg–Klein–Rees-style potential curve derived from available experimental data on vibrational energies. With an accuracy of 3–4 significant figures, energy predictions are made for HCl (DCl, TCl) for the 836 (1625, 2366) B-O rovibrational bound states with maximal vibrational numberνmax=20(29,35)and maximal angular momentumLmax=64(90,109), including 24 (46, 63) weakly-bound states (close to the dissociation limit) with energies≲10−4 Hartree. The insufficiency of existing experimental data is indicated, and predictions for the bulk of the missing rovibrational states are made for all HCl, DCl, and TCl molecules.
Stability of a two-electron system under pressure confinement: structural and quantum information theoretical analysis
Authors: Mondal Santanu , Sadhukhan Anjan , Sen Kalidas , Saha Jayanta K 
Published: June 2023

Abstract
The stability of a two-electron Zee system trapped inside an impenetrable spherical cavity is analyzed using an explicitly correlated multi-exponent Hylleraas-type basis set in the framework of the Ritz variational method. The wave function is considered to be consistent with Dirichlet’s boundary condition. Four different

Z
∈
[
1
,
(

Z

c

)
b

,
0.25
,
0.0
]

values are considered, where

(

Z

c

)
b

denotes the critical nuclear charge beyond which the system is embedded in a discrete positive energy continuum due to the impenetrable nature of the cavity. The energy contribution due to the total correlation (in the presence of both radial and angular correlation) effect, the radial correlation limit, and the angular correlation limit are also studied in detail. The thermodynamic pressure felt by the two-electron Zee system inside the cavity is estimated, and a formula replicating the behavior between the pressure and volume of the cavity is deduced by a fitting procedure. Different geometrical properties, e.g. radial moments (

⟨

r
1

p

⟩

,

⟨

r
1

r
2

⟩

,

⟨

r

12

p

⟩

,

⟨

r
<

⟩

,

⟨

r
>

⟩

; p =

1
−
3

), angular moments (

〈
cos

θ
1

〉

,

⟨
cos

θ

12

⟩

), and related important physical quantities, are determined. The variation of Kirkwood and Buckingham polarizabilities w.r.t. the pressure felt by the two-electron Zee system is analyzed. A one-electron radial density is estimated for each pair of (

Z
,
R

), which has been employed to generate the electrostatic potential as well as different information theoretical measures like Shannon entropy, Fisher entropy, Rényi entropy, Tsallis entropy, and Onicescu informational energy. The chosen information theoretical measures have been found to be sensitive tools for describing changes in the electronic structure due to spatial confinement. An interesting interplay between the electronic and nuclear contribution to the classical electrostatic potential is observed, leading to a shift in the position of its characteristic minimum due to compression. Wherever possible, a comparison is made in order to ascertain the high accuracy of our numerical results. The procedure of analytic evaluation of the integrals needed to estimate the atomic properties under consideration is discussed in detail.
Practical application of Pd-based bimetallic catalysts with enhanced selectivity supported by chelating resin for catalytic nitrate reduction in real water
Authors: Zang Ling , Shi Jialu , Shen Zhanhui 
Published: June 2023

Abstract
An innovative Pd-Me (Pd-Cu, Pd-In and Pd-Sn) bimetallic catalyst supported on porous chelating DOWEX M4195 resin (D) was established to reduce the nitrate almost entirely and achieved high selectivity to the expected harmless form of nitrogen. In this study, sodium borohydride (NaBH4) was applied in preparing bimetallic catalysts by liquid-phase reduction as the prestoring reductant. Pd-In/D and Pd-Sn/D groups performed well in N2 selectivity (all above 97%). In addition, Pd-In and Pd-Sn bimetallic catalysis yields higher selectivity towards N2 than the Pd-Cu pair in the presence of HCO3
−, Cl−, SO4
2− and humic acid. Likewise, in terms of N2 selectivity, Pd-In/D and Pd-Sn/D bimetallic catalysts were superior to that of Pd-Cu/D (72.16%) in the municipal wastewater treatment plant sewage. The current results provide insight into the superb reactivity, excellent stability, and most important—extremely high harmless N2 selectivity of Pd-In and Pd-Sn-based bimetallic catalysts in practical application and provide new ideas for enhancing the feasibility of the catalytic reduction of nitrate by minimizing environmentally harmful by-products.