bluuebunny/crossref_metadata_2025_split huggingface dataset:
The price of prosociality in pandemic times
Authors: Santamaría-García Hernando , Burgaleta Miguel , Legaz Agustina , Flichtentrei Daniel , Córdoba-Delgado Mateo , Molina-Paredes Juliana , Linares-Puerta Juliana , Montealegre-Gómez Juan , Castelblanco Sandra , Schulte Michael , Páramo Juan David , Mondragon Izara , Leongómez Juan David , Salamone Paula , González-Pacheco Juan , Báez Sandra , Eyre Harris , Ibanez Agustín 
Published: January 2022

AbstractThe SARS-CoV-2 pandemic has imposed widespread negative impacts (economically, psychologically, neurologically, and societally), and has changed daily behaviors on a global scale. Such impacts are more significant and pervasive in countries with higher levels of inequality and reduced Government capacity and responsiveness, such as those in the Global South (e.g., Colombia). Differences in social and moral cognitive skills may significantly impact individual attitudes and responses to the pandemic. Here, we aimed to assess the extent to which factors associated with prosociality (including empathy, theory of mind (ToM), and moral judgments) predict the perception of SARS-CoV-2 impacts and responses. Participants (N = 413) from Colombia answered factors associated with prosociality measures and judgments about SARS-CoV-2 risk, impact, and acceptance of quarantine guidelines. Results revealed that affective empathy (personal distress and empathic concern) and moral tendencies (deontological trends) predicted greater acceptance of quarantine but in turn yielded an increased perception of risks and individual impacts of SARS-CoV-2. Moreover, age (older) and gender (female) also increased the risk perception and impact estimation. These results underscore the role of prosocial-related predispositions informing individual responses to the pandemic and provide an opportunity to exploit this knowledge to inform successful interventions favoring behavioral change.
Sizes of pure and doped helium droplets from single shot x-ray imaging
Authors: Tanyag Rico Mayro P. , Bacellar Camila , Pang Weiwu , Bernando Charles , Gomez Luis F. , Jones Curtis F. , Ferguson Ken R. , Kwok Justin , Anielski Denis , Belkacem Ali , Boll Rebecca , Bozek John , Carron Sebastian , Chen Gang , Delmas Tjark , Englert Lars , Epp Sascha W. , Erk Benjamin , Foucar Lutz , Hartmann Robert , Hexemer Alexander , Huth Martin , Leone Stephen R. , Ma Jonathan H. , Marchesini Stefano , Neumark Daniel M. , Poon Billy K. , Prell James , Rolles Daniel , Rudek Benedikt , Rudenko Artem , Seifrid Martin , Swiggers Michele , Ullrich Joachim , Weise Fabian , Zwart Petrus , Bostedt Christoph , Gessner Oliver , Vilesov Andrey F. 
Published: January 2022

Advancements in x-ray free-electron lasers on producing ultrashort, ultrabright, and coherent x-ray pulses enable single-shot imaging of fragile nanostructures, such as superfluid helium droplets. This imaging technique gives unique access to the sizes and shapes of individual droplets. In the past, such droplet characteristics have only been indirectly inferred by ensemble averaging techniques. Here, we report on the size distributions of both pure and doped droplets collected from single-shot x-ray imaging and produced from the free-jet expansion of helium through a 5 μm diameter nozzle at 20 bars and nozzle temperatures ranging from 4.2 to 9 K. This work extends the measurement of large helium nanodroplets containing 109–1011 atoms, which are shown to follow an exponential size distribution. Additionally, we demonstrate that the size distributions of the doped droplets follow those of the pure droplets at the same stagnation condition but with smaller average sizes.
Invariant odor recognition with ON–OFF neural ensembles
Authors: Nizampatnam Srinath , Zhang Lijun , Chandak Rishabh , Li James , Raman Baranidharan 
Published: January 2022

Significance
The smell of coffee is the same whether it is smelled in a coffee shop or grocery shop (different backgrounds), on a hot day or a cold day (different ambient conditions), after lunch or dinner (different temporal contexts), or using a deep inhalation or normal inhalation (different stimulus dynamics). This feat of pattern recognition that is still difficult to achieve in artificial chemical sensing systems is performed by most sensory systems for their survival. How is this capability achieved? We explored this issue. We found that there are two orthogonal ensembles of neurons, one activated during stimulus presence (ON neurons) and one activated after its termination (OFF neurons), and both contribute to this important computation in a complementary fashion.
Structural transitions in the GTP cap visualized by cryo-electron microscopy of catalytically inactive microtubules
Authors: LaFrance Benjamin J. , Roostalu Johanna , Henkin Gil , Greber Basil J. , Zhang Rui , Normanno Davide , McCollum Chloe O. , Surrey Thomas , Nogales Eva 
Published: January 2022

Significance
Microtubules (MTs) are nonequilibrium polymers that switch between states of growth and shrinkage. This property is critical for their function and is a consequence of GTP hydrolysis in the MT. The structure of the stable GTP part of the MT (the GTP cap) has previously been inferred from MTs polymerized with nonhydrolyzable GTP analogs. Here, we report high-resolution structures of MTs truly containing GTP, polymerized from mutated, hydrolysis-deficient tubulins. We find that GTP-MTs have an “expanded lattice” and a “closed seam,” structural characteristics possibly responsible for stabilizing the GTP cap. These results provide insight into the structural transitions at growing MT ends, furthering our understanding of the bistable nature of MTs.
Recruitment of an ancient branching program to suppress carpel development in maize flowers
Authors: Klein Harry , Gallagher Joseph , Demesa-Arevalo Edgar , Abraham-Juárez María Jazmín , Heeney Michelle , Feil Regina , Lunn John E. , Xiao Yuguo , Chuck George , Whipple Clinton , Jackson David , Bartlett Madelaine 
Published: January 2022

SignificanceFloral morphology is immensely diverse. One developmental process acting to shape this diversity is growth suppression. For example, grass flowers exhibit extreme diversity in floral sexuality, arising through differential suppression of stamens or carpels. The genes regulating this growth suppression and how they have evolved remain largely unknown. We discovered that two classic developmental genes with ancient roles in controlling vegetative branching were recruited to suppress carpel development in maize. Our results highlight the power of forward genetics to reveal unpredictable genetic interactions and hidden pleiotropy of developmental genes. More broadly, our findings illustrate how ancient gene functions are recruited to new developmental contexts in the evolution of plant form.
Influence of cosmological expansion in local experiments
Authors: Spengler Felix , Belenchia Alessio , Rätzel Dennis , Braun Daniel 
Published: January 2022

Abstract
Whether the cosmological expansion can influence the local dynamics, below the galaxy clusters scale, has been the subject of intense investigations in the past three decades. In this work, we consider McVittie and Kottler spacetimes, embedding a spherical object in a FLRW spacetime. We calculate the influence of the cosmological expansion on the frequency shift of a resonator and estimate its effect on the exchange of light signals between local observers. In passing, we also clarify some of the statements made in the literature.
Deep neural-network based optimization for the design of a multi-element surface magnet for MRI applications
Authors: Tewari Sumit , Yousefi Sahar , Webb Andrew 
Published: January 2022

Abstract
We present a combination of a CNN-based encoder with an analytical forward map for solving inverse problems. We call it an encoder-analytic (EA) hybrid model. It does not require a dedicated training dataset and can train itself from the connected forward map in a direct learning fashion. A separate regularization term is not required either, since the forward map also acts as a regularizer. As it is not a generalization model it does not suffer from overfitting. We further show that the model can be customized to either find a specific target solution or one that follows a given heuristic. As an example, we apply this approach to the design of a multi-element surface magnet for low-field magnetic resonance imaging (MRI). We further show that the EA model can outperform the benchmark genetic algorithm model currently used for magnet design in MRI, obtaining almost 10 times better results.
Fundamentals, progress and perspectives on high-frequency phononic crystals
Authors: Cang Yu , Jin Yabin , Djafari-Rouhani Bahram , Fytas George 
Published: January 2022

Abstract
Phononic crystals (PnCs) are capable of manipulating the flow of elastic energy through their periodic structures and have emerged as a promising field in the last two decades. Thanks to the advances in microfabrication technologies and developments of multifunctional materials, the engineering of periodic structures moves forward to the nanometer scale. Hence, the relevant frequencies of elastic waves are pushed toward the gigahertz regime where strong photon-phonon interactions trigger the applications of PnCs towards information and communication technologies. In this review, we present the experimental achievements on hypersonic PnCs involving microfabrication technologies to realize the desired structures and characterization of their band structures for unraveling phonon propagation modulation. Some application-oriented research directions are proposed in terms of advances in fabrication and characterization technologies and the development of electro-optomechanical systems.
Methanol carbonylation to acetaldehyde on Au particles supported by single-layer MoS2 grown on silica
Authors: Almeida Kortney , Chagoya Katerina , Felix Alan , Jiang Tao , Le Duy , Rawal Takat B , Evans Prescott E , Wurch Michelle , Yamaguchi Koichi , Dowben Peter A , Bartels Ludwig , Rahman Talat S , Blair Richard G 
Published: January 2022

Abstract
Homogenous single-layer MoS2 films coated with sub-single layer amounts of gold are found to isolate the reaction of methanol with carbon monoxide, the fundamental step toward higher alcohols, from an array of possible surface reactions. Active surfaces were prepared from homogenous single-layer MoS2 films coated with sub-single layer amounts of gold. These gold atoms formed clusters on the MoS2 surface. A gas mixture of carbon monoxide (CO) and methanol (CH3OH) was partially converted to acetaldehyde (CH3CHO) under mild process conditions (308 kPa and 393 K). This carbonylation of methanol to a C2 species is a critical step toward the formation of higher alcohols. Density functional theory modeling of critical steps of the catalytic process identify a viable reaction pathway. Imaging and spectroscopic methods revealed that the single layer of MoS2 facilitated formation of nanoscale gold islands, which appear to sinter through Ostwald ripening. The formation of acetaldehyde by the catalytic carbonylation of methanol over supported gold clusters is an important step toward realizing controlled production of useful molecules from low carbon-count precursors.
Unmanned aerial vehicle rotor fault diagnosis based on interval sampling reconstruction of vibration signals and a one-dimensional convolutional neural network deep learning method
Authors: Du Canyi , Zhang Xinyu , Zhong Rui , Li Feng , Yu Feifei , Rong Ying , Gong Yongkang 
Published: January 2022

Abstract
With the aim of identifying possible mechanical faults in unmanned aerial vehicle (UAV) rotors during operation, this paper proposes a method based on interval sampling reconstruction of vibration signals and one-dimensional convolutional neural network (1D-CNN) deep learning. Firstly, experiments were designed to collect the vibration acceleration signals of a UAV working at high speed under three states (normal, rotor damage by varying degrees, and rotor crack by different degrees). Then, considering the powerful feature extraction and complex data analysis abilities of 1D-CNN, an effective deep learning model for fault identification is established utilizing 1D-CNN. During analysis, it is found that the recognition rate for minor faults is not ideal, with all weak states being identified as normal, reducing the overall identification accuracy, when using conventional sequential sampling to construct learning sample sets. To this end, in order to make the sample data cover the whole process of data collection as much as possible, a learning sample processing method based on interval sampling reconstruction of the vibration signal is proposed. And it is also verified that the reconstructed sample set can easily reflect the global information of mechanical operation. Finally, according to the comparison of analysis results, the recognition rate of the deep learning model for different degrees of faults is greatly improved, and minor faults could also be accurately identified through this method. The results show that the 1D-CNN deep learning model could diagnose and identify UAV rotor damage faults accurately, by incorporating the proposed method of interval sampling reconstruction.
Direct Z-scheme MgIn2S4/TiO2 heterojunction for enhanced photocathodic protection of metals under visible light
Authors: Li Hong , Cui Xingqiang , Song Weizhe , Yang Zhanyuan , Li Yanhui , Zhang Pengfei , Zheng Zongmin , Wang Yuqi , Li Junru , Ma Fubin 
Published: January 2022

Abstract
To improve the photocathodic protection performance of traditional TiO2 photoanodes for metals, constructing a Z-scheme heterojunction is one of the most promising and creative strategies. Herein, we fabricated a novel Z-scheme MgIn2S4 nanosheets/TiO2 nanotube nanocomposite through anodization and hydrothermal method. The optimized Z-scheme MgIn2S4/TiO2 nanocomposites exhibited stronger visible light absorption, higher separation efficiency of photoelectrons and photocathodic protection performances in comparison to pure TiO2. The theoretical analysis and experimental results show that the Z-scheme heterojunction and oxygen vacancies jointly improved the separation efficiency of photogenerated electron–hole pairs and visible light absorption capacity, thereby improving the photoelectric conversion performance of the MgIn2S4/TiO2 nanocomposites. Furthermore, the influence of the precursor solution concentration on the photocathodic protection performances of the composites was investigated. As a result, when the concentration of magnesium source in the precursor solution was 0.06 mmol, the prepared MgIn2S4/TiO2-0.06 displayed the best photocathodic protection performance. In addition, the hydroxyl radicals (·OH) generated in the electron spin resonance (ESR) experiment verified the Z-scheme heterojunction mechanism of the MgIn2S4/TiO2 composite, and also demonstrated the excellent redox performance of the composite. This work provides valuable reference for the construction of high-performance Z-scheme heterojunctions for photocathode protection of metals.
Study on the winding quality for spiral HTS cables based on AI detection model
Authors: Wang Mingyang , Ye Haosheng , Wang Xueliang , Li Zhuyong , Sheng Jie , Hong Zhiyong , Jin Zhijian 
Published: January 2022

Abstract
The development of high-temperature superconducting (HTS) conductors is leading to diverse structural designs of HTS cables. (RE)Ba2Cu3O
x
(REBCO) tapes using spiral geometry has been a popular compact HTS cable structure, which is in the critical stage of engineering production and application. However, the winding quality of REBCO tapes is unstable for spiral HTS cables, because of the different winding methods like manual winding, device-assisted winding, or automatic winding. Although automatic winding will be the first choice for the actual applications by spiral HTS cables, the related winding quality is not monitored effectively yet. In this paper, we first discuss the possible influence of the winding quality on the critical current performance of spiral HTS cables. Then, an artificial intelligence (AI) based method is implemented to realize the detection model for the winding quality. From image data preparation to AI detection and postprocessing, the detection model provides the final results to show the winding intervals as a binary image. Through the intuitive analysis and the evaluation metrics, both error and correct winding conditions obtain acceptable detection results, and the correct one has a better performance. The identification of the winding intervals will help to determine the monitoring strategy for the spiral HTS cable fabrication.
A very high energy hadron collider on the Moon
Authors: Beacham James , Zimmermann Frank 
Published: January 2022

Abstract
The long-term prospect of building a hadron collider around the circumference of a great circle of the Moon is sketched. A circular collider on the Moon (CCM) of ∼11 000 km in circumference could reach a proton–proton center-of-mass collision energy of 14 PeV—a thousand times higher than the Large Hadron Collider at CERN—optimistically assuming a dipole magnetic field of 20 T. Several aspects of such a project are presented, including siting, construction, availability of necessary materials on the Moon, and powering, as well as a discussion of future studies and further information needed to determine the more concrete feasibility of each. Machine parameters and vacuum requirements are explored, and an injection scheme is delineated. Other unknowns are set down. Due to the strong interest from multiple organizations in establishing a permanent Moon presence, a CCM could be the (next-to-) next-to-next-generation discovery machine for high-energy particle physics and a natural successor to next-generation machines, such as the proposed future circular collider at CERN or a super proton–proton collider in China, and other future machines, such as a collider in the Sea, in the Gulf of Mexico. A CCM would serve as an important stepping stone toward a Planck-scale collider sited in our Solar System.
Pressure-dependent topological superconductivity on the surface of FeTe0.5Se0.5
Authors: Yang Chih-Kai , Lee Chi-Hsuan 
Published: January 2022

Abstract
FeTe1−x
Se
x
is a family of iron-based superconductors with its critical temperature (T
c) dependent on the composition of Se. A well-known T
c is 14.5 K for x = 0.45, which exhibits an s-wave superconducting gap between the topological superconducting surfaces states. Exchange interaction between the electrons has been proposed as the mechanism behind the formation of Cooper pairs for the sample of FeTe0.5Se0.5. In this article we provide further proof that exchange interaction, and hence the associated T
c, depends on the applied pressure on FeTe0.5Se0.5. Using density functional calculations for electrons and phonons and the Bardeen–Cooper–Schrieffer (BCS) theory for superconductivity, we found that T
c and superconducting gap for FeTe0.5Se0.5 soars under increasing compression, consistent with the results of experiment.
Quantum collisional thermostats
Authors: Tabanera Jorge , Luque Inés , Jacob Samuel L , Esposito Massimiliano , Barra Felipe , Parrondo Juan M R 
Published: January 2022

Abstract
Collisional reservoirs are becoming a major tool for modelling open quantum systems. In their simplest implementation, an external agent switches on, for a given time, the interaction between the system and a specimen from the reservoir. Generically, in this operation the external agent performs work onto the system, preventing thermalization when the reservoir is at equilibrium. One can recover thermalization by considering an autonomous global setup where the reservoir particles colliding with the system possess a kinetic degree of freedom. The drawback is that the corresponding scattering problem is rather involved. Here, we present a formal solution of the problem in one dimension and for flat interaction potentials. The solution is based on the transfer matrix formalism and allows one to explore the symmetries of the resulting scattering map. One of these symmetries is micro-reversibility, which is a condition for thermalization. We then introduce two approximations of the scattering map that preserve these symmetries and, consequently, thermalize the system. These relatively simple approximate solutions constitute models of quantum thermostats and are useful tools to study quantum systems in contact with thermal baths. We illustrate their accuracy in a specific example, showing that both are good approximations of the exact scattering problem even in situations far from equilibrium. Moreover, one of the models consists of the removal of certain coherences plus a very specific randomization of the interaction time. These two features allow one to identify as heat the energy transfer due to switching on and off the interaction. Our results prompt the fundamental question of how to distinguish between heat and work from the statistical properties of the exchange of energy between a system and its surroundings.
Varying electrical and dielectric properties of Ni:SnO2 films by MWCNTs and GNPs coating
Authors: Sarf Fatma , Er Irmak Karaduman , Ajjaq Ahmad , Çağırtekin Ali Orkun , Yakar Emin , Acar Selim 
Published: January 2022

Abstract
In this research, pure SnO2 and Ni-doped SnO2 (Ni:SnO2) nanocomposite films were produced by chemical bath deposition method and the latter were coated with multi-walled carbon nanotubes (Ni:SnO2/MWCNTs) or graphene nanoplatelets (Ni:SnO2/GNPs) by spin coating. All samples have tetragonal rutile SnO2 structure with the presence of carbon (002) peak in MWCNTs- or GNPs-coated films. Crystallite size of SnO2 films decreased remarkably with Ni doping followed by a slight decrease with MWCNTs coating and slight increase with GNPs coating. Scanning electron microscope images manifested a dispersed agglomerative nature of SnO2 nanoparticles which reduced especially with MWCNTs coating due to the porous surface provided by carbon nanotubes. From the photoluminescence measurements, oxygen defects-related peaks were spotted in the SnO2-based structures with different luminescence intensities. The most significant decrease in resistance was observed with the addition of GNPs into Ni-doped SnO2 nanocomposites compared to the other produced films mainly due to the synergetic effect that promotes excellent charge transfer between surfaces of Ni:SnO2 and graphene nanosheet. The huge increase in conductivity of GNPs-coated films led to a huge increase in dielectric losses and this followed by a drop down of dielectric constant of the GNPs-coated films.