bluuebunny/crossref_metadata_2025 huggingface dataset:
Thermal conductance of the water–gold interface: The impact of the treatment of surface polarization in non-equilibrium molecular simulations
Authors: Olarte-Plata Juan D. , Bresme Fernando
Published: May 2022

Interfacial thermal conductance (ITC) quantifies heat transport across material–fluid interfaces. It is a property of crucial importance to study heat transfer processes at both macro- and nanoscales. Therefore, it is essential to accurately model the specific interactions between solids and liquids. Here, we investigate the thermal conductance of gold–water interfaces using polarizable and non-polarizable models. Both models have been fitted to reproduce the interfacial tension of the gold–water interface, but they predict significantly different ITCs. We demonstrate that the treatment of polarization using Drude-like models, widely employed in molecular simulations, leads to a coupling of the solid and liquid vibrational modes that give rise to a significant overestimation of the ITCs. We analyze the dependence of the vibrational coupling with the mass of the Drude particle and propose a solution to the artificial enhancement of the ITC, preserving at the same time the polarization response of the solid. Based on our calculations, we estimate ITCs of 200 MW/(m2 K) for the water–gold interface. This magnitude is comparable to that reported recently for gold–water interfaces [279 ± 16 MW/(m2 K)] using atomic fluctuating charges to account for the polarization contribution.
λ-DFVB(U): A hybrid density functional valence bond method based on unpaired electron density
Authors: Zheng Peikun , Gan Zixi , Zhou Chen , Su Peifeng , Wu Wei
Published: May 2022

In this paper, a hybrid density functional valence bond method based on unpaired electron density, called λ-DFVB(U), is presented, which is a combination of the valence bond self-consistent field (VBSCF) method and Kohn–Sham density functional theory. In λ-DFVB(U), the double-counting error of electron correlation is mitigated by a linear decomposition of the electron–electron interaction using a parameter λ, which is a function of an index based on the number of effectively unpaired electrons. In addition, λ-DFVB(U) is based on the approximation that correlation functionals in KS-DFT only cover dynamic correlation and exchange functionals mimic some amount of static correlation. Furthermore, effective spin densities constructed from unpaired density are used to address the symmetry dilemma problem in λ-DFVB(U). The method is applied to test calculations of atomization energies, atomic excitation energies, and reaction barriers. It is shown that the accuracy of λ-DFVB(U) is comparable to that of CASPT2, while its computational cost is approximately the same as VBSCF.
A density functional theory based tight-binding study on the water effect on nanostructuring of choline chloride + ethylene glycol deep eutectic solvent
Authors: Rozas Sara , Atilhan Mert , Aparicio Santiago
Published: May 2022

The effect of water on the properties of an archetypical type III deep eutectic solvent [choline chloride : ethyleneglycol (1:2)] is analyzed using ab initio molecular dynamics simulations in the 0 to 60 wt. % water content range. The properties of the mixed fluids are studied considering nanostructuring, intermolecular forces (hydrogen bonding), the energy of interactions, dynamic properties, and domain analysis. The reported results confirm that the change in the properties of the studied deep eutectic solvent is largely dependent on the amount of water. The competing effect of water molecules for the available hydrogen bonding sites determines the evolution of the properties upon water sorption. The main structural features of the considered deep eutectic were maintained even for large water contents; thus, its hydrophilicity could be used for tuning fluid physicochemical properties.
Accelerating ethics, empathy, and equity in geographic information science
Authors: Nelson T. A. , Goodchild M. F. , Wright D. J.
Published: May 2022

Significance

There has been a groundswell in the support needed to center ethics, empathy, and equity in scientific thought and practice. Drawing on our experience from GIScience, our goal is to accelerate ethical, empathetic, and equitable scientific practices. Many of the opportunities and challenges we outline are broadly applicable and will stimulate the conversations needed to accelerate transformation of science practice and culture. With an emphasis on practical suggestions for reshaping science, we invite all scientists to join in a fundamentally different approach. This paper is a step toward mobilizing the scientific community toward ethics, empathy, and equity by inviting humility, broader measures of excellence and success, diversity in our networks, and the creation of pathways to inclusive education.
Cortical basis for skilled vocalization
Authors: Cerkevich Christina M. , Rathelot Jean-Alban , Strick Peter L.
Published: May 2022

Significance

We examined the cortical control of a laryngeal muscle that is essential for vocalization in two monkey species that differ in their vocal motor skill. Our results suggest that enhancements in vocal skill are coupled to enlargements in the descending output from two premotor areas, ventral area 6 (area 6V) and the supplementary motor area (SMA). This result challenges the view that improvements in motor skills are due largely to changes in the output from the primary motor cortex.
Addendum: A note on spin two fields in curved backgrounds (2007 Class. Quantum Grav.

24 1683)
Authors: Deser S , Henneaux M
Published: May 2022

Some time ago, we showed that a weak (linear) massless spin two wave could only propagate on a Ricci-flat or Ricci-constant background: it must necessarily be a perturbation of general relativity. This was just the continuation of the higher spin chain: massless s = 3/2 also required Ricci-flatness (which is the basis of supergravity) while s > 2 needs Riemann flatness. This note, entitled ‘higher spin m ≠ 0 excitations on curved backgrounds and cosmological supergravity’, re-analyzes the problem in a perhaps more physical way: by considering massless and—only apparently—massive small excitations and showing how their parameters relate to the cosmological constant. We will thus prove, in a simple physical way, the necessity, as well as the sufficiency, of the known supergravity results.
Implementation of a generalized precession parameter in the RIFT parameter estimation algorithm
Authors: Henshaw Chad , O’Shaughnessy Richard , Cadonati Laura
Published: May 2022

Since the initial discovery of gravitational waves in 2015, significant developments have been made towards waveform interpretation and estimation of compact binary source parameters. We present herein an implementation of the generalized precession parameter ⟨χ
p⟩ [Gerosa et al 2021], which averages over all angular variations on the precession timescale, within the RIFT parameter estimation framework. Relative to the precession parameter χ
p, which characterizes the single largest dynamical spin in a binary, ⟨χ
p⟩ has a unique domain 1 < ⟨χ
p⟩ < 2, which is exclusive to binaries with two precessing spins. After reviewing the physical differences between these two parameters, we describe how ⟨χ
p⟩ was implemented in RIFT and apply it to all 36 events from the second half of the Advanced LIGO and Advanced Virgo third observing run (O3b). In O3b, ten events show significant amounts of precession ⟨χ
p⟩ > 0.5. Of particular interest is GW191109_010717; we show it has a

∼
28
%

probability that the originating system necessarily contains two misaligned spins.
Relativity, scaling, and electromagnetic radiation equilibrium for circular orbits
Authors: Boyer Timothy H
Published: May 2022

The radiation emitted by a charged particle moving in a circular orbit requires that the orbital speed of the particle is less than the speed of light in vacuum. This crucial relativistic restriction is lost in any treatment which combines nonrelativistic mechanics with classical electrodynamics through the nonrelativistic Larmor radiation formula or the dipole approximation, which approximations correspond to taking only the lowest power of velocity. A notable example of the resulting failure involves the derivation of the blackbody radiation spectrum within classical physics. Nature contains a smallest electric charge e and a largest speed c. Both these fundamental constants should appear in a classical theory of nature. We connect the assumptions regarding fundamental constants to the scaling aspects of classical theories. Nonrelativistic mechanics exhibits scaling which is entirely different from that found in relativistic classical electrodynamics where only Coulomb potentials are allowed and the constants e and c both appear. The scaling aspects are reflected in the radiation spectra which different theories predict for thermal radiation equilibrium. The Rayleigh–Jeans spectrum reflects the scaling aspects of nonrelativistic classical mechanics whereas the classical electromagnetic zero-point spectrum and the Planck spectrum share the scaling aspects of relativistic classical electrodynamics which includes both e and c.
Theoretical study of topological properties of ferromagnetic pyrite CoS2
Authors: Robredo I , Schröter N B M , Reyes-Serrato A , Bergara A , de Juan F , Schoop L M , Vergniory M G
Published: May 2022

Since the discovery of the first topological material 15 years ago, the search for material realizations of novel topological phases has become the driving force of the field. While oftentimes we search for new materials, we forget that well established materials can also display very interesting topological properties. In this work, we revisit CoS

2

, a metallic ferromagnetic pyrite that has been extensively studied in the literature due to its magnetic properties. We study the topological features of its electronic band structure and identify Weyl nodes and nodal lines, as well as a symmetry-protected fourfold fermion close to the Fermi level. Looking at different surface cleavage planes, we observe both spin polarized Fermi arcs in the majority channel and drumhead states. These findings suggest that CoS2 is a promising platform to study topological phenomena, as well as a good candidate for spintronic applications.
Correlation between antisymmetric magnetoresistance and anomalous hall effect in Co1−

x
Tb
x
films
Authors: Su Yangtao , Meng Yang , Wang Zhen , Xu Tiankuo , Shi Haibin , Wang Li , Cao Xinyu , Zhang Ying , Zhao Hongwu
Published: May 2022

The antisymmetric magnetoresistance (MR) and anomalous Hall effect (AHE) of perpendicularly magnetized Co1−x
Tb
x
thin films with different Tb concentration have been investigated under gradient magnetic fields. Owing to the presence of tilting domain walls, the Co1−x
Tb
x
films exhibit anomalous antisymmetric MR, whose polarity reversal is strongly dependent on the film composition and exactly consistent with the simultaneously measured AHE. Our results suggest that the effect of disorder scattering from impurities and dispersive domain wall boundaries is more pronounced in Co1−x
Tb
x
alloy films distant from the compensation point. Moreover, the modulation of AHE coefficient Rs directly leads to the remarkable variation of antisymmetric MR, providing a new insight into design of domain wall based electronic devices.
Giant optical anisotropy of WS2 flakes in the visible region characterized by Au substrate assisted near-field optical microscopy
Authors: Deng Nan , Long Hua , Wang Kun , Han Xiaobo , Wang Bing , Wang Kai , Lu Peixiang
Published: May 2022

Transition metal dichalcogenides (TMD) have attracted considerable attention in the field of photonic integrated circuits due to their giant optical anisotropy. However, on account of their inherent loss in the visible region and the difficulty of measuring high refractive index materials, near-field characterizations of the optical anisotropy of TMD in the visible region have inherent experimental difficulties. In this work, we present a systematical characterization of the optical anisotropy in tungsten disulfide (WS2) flakes by using scattering-type scanning near-field optical microscopy (s-SNOM) excited at 671 nm. Transverse-electric and transverse-magnetic (TM) waveguide modes can be excited in WS2 flakes with suitable thickness, respectively. With the assistance of the Au substrate, the contrast of the near-field fringes is enhanced in comparison with the SiO2 substrate. By combining waveguide mode near-field imaging and theoretical calculations, the in-plane and out-of-plane refractive indexes of WS2 are determined to be 4.96 and 3.01, respectively, indicating a high birefringence value up to 1.95. This work offers experimental evidence for the potential application of WS2 in optoelectronic integrated circuits in the visible region.
Recent progress in microwave-assisted preparations of 2D materials and catalysis applications
Authors: Wang Jiayue , Wu Wei , Kondo Hiroki , Fan Tongxiang , Zhou Han
Published: May 2022

On the urgency of metal-free catalysts, two-dimensional materials (2DMs) have caused extensive researches because of distinctive optical and electronic properties. In the last decade, microwave methods have emerged in rapid and effective preparations of 2DMs for catalysis. Microwave heating offers several advantages namely direct, fast, selective heating and uniform reaction temperature compared to conventional heating methods, thus bringing about high-yield and high-purity products in minutes or even seconds. This review summarizes recent advances in microwave-assisted preparations of 2DMs-based catalysts and their state-of-the-art catalytic performances. Microwave heating mechanisms are briefly introduced mainly focusing on microwave-matter interactions, which can guide the choice of precursors, liquid media, substrates, auxiliaries and experiment parameters during microwave radiation. We especially provide a detailed insight into various microwave-assisted procedures, classified as exfoliation, synthesis, doping, modification and construction towards different 2DMs nanomaterials. We also discuss how microwave affects the synthetic composition and microstructure of 2DMs-based catalysts, thereby deeply influencing their optical and electronic properties and the catalytic performances. Finally, advantages, challenges and prospects of microwave-assisted approaches for 2DMs nanomaterials are summarized to inspire the effective and large-scale fabrication of novel 2DMs-based catalysts.
Plasmonic features of free-standing chitosan nanocomposite film with silver and graphene oxide for SERS applications
Authors: Samodelova Mariia V , Kapitanova Olesya O , Evdokimov Pavel V , Eremina Olga E , Goodilin Eugene A , Veselova Irina A
Published: May 2022

A scalable procedure of SERS substrates design was developed using a novel plasmonic structure based on a freestanding chitosan film, silver nanoparticles, and graphene oxide. Chitosan provides a uniform distribution of silver nanoparticles from a colloidal suspension and, therefore, a reproducible Raman signal from local areas of measurements of several tens of microns. The addition of graphene oxide (GO) to the colloidal solution of silver nanoparticles suppresses the tortuous background fluorescence signal from the analyte and leads to an increase in the signal-to-fluorescence background intensity ratio by up to 6 times as compared to structures without GO. The manufactured plasmonic polymer nanocomposite provides a detection limit of down to 100 pM for R6G using a laser wavelength of 532 nm through a portable ×10 objective. The high colloidal stability of GO in water and the use of an aqueous colloid of silver nanoparticles simplify the procedure for creating a substrate by applying the GO-silver composite on the surface of a chitosan film without a need to form a GO film. Therefore, our approach paves a promising avenue to provide more sensitive detection even for the fluorescent analytes with short-wavelength lasers (532, 633 nm) instead of IR (785, 1024 nm) and foster the practical application of the developed plasmonic composites on portable Raman spectrometers.
Assessment of data consistency through cascades of independently recurrent inference machines for fast and robust accelerated MRI reconstruction
Authors: Karkalousos D , Noteboom S , Hulst H E , Vos F M , Caan M W A
Published: May 2022

Objective. Machine Learning methods can learn how to reconstruct magnetic resonance images (MRI) and thereby accelerate acquisition, which is of paramount importance to the clinical workflow. Physics-informed networks incorporate the forward model of accelerated MRI reconstruction in the learning process. With increasing network complexity, robustness is not ensured when reconstructing data unseen during training. We aim to embed data consistency (DC) in deep networks while balancing the degree of network complexity. While doing so, we will assess whether either explicit or implicit enforcement of DC in varying network architectures is preferred to optimize performance. Approach. We propose a scheme called Cascades of Independently Recurrent Inference Machines (CIRIM) to assess DC through unrolled optimization. Herein we assess DC both implicitly by gradient descent and explicitly by a designed term. Extensive comparison of the CIRIM to compressed sensing as well as other Machine Learning methods is performed: the End-to-End Variational Network (E2EVN), CascadeNet, KIKINet, LPDNet, RIM, IRIM, and UNet. Models were trained and evaluated on T1-weighted and FLAIR contrast brain data, and T2-weighted knee data. Both 1D and 2D undersampling patterns were evaluated. Robustness was tested by reconstructing 7.5× prospectively undersampled 3D FLAIR MRI data of multiple sclerosis (MS) patients with white matter lesions. Main results. The CIRIM performed best when implicitly enforcing DC, while the E2EVN required an explicit DC formulation. Through its cascades, the CIRIM was able to score higher on structural similarity and PSNR compared to other methods, in particular under heterogeneous imaging conditions. In reconstructing MS patient data, prospectively acquired with a sampling pattern unseen during model training, the CIRIM maintained lesion contrast while efficiently denoising the images. Significance. The CIRIM showed highly promising generalization capabilities maintaining a very fair trade-off between reconstructed image quality and fast reconstruction times, which is crucial in the clinical workflow.
Can biophysical models of dendritic spines be used to explore synaptic changes associated with addiction?
Authors: Bonilla-Quintana Mayte , Rangamani Padmini
Published: May 2022

Effective treatments that prevent or reduce drug relapse vulnerability should be developed to relieve the high burden of drug addiction on society. This will only be possible by enhancing the understanding of the molecular mechanisms underlying the neurobiology of addiction. Recent experimental data have shown that dendritic spines, small protrusions from the dendrites that receive excitatory input, of spiny neurons in the nucleus accumbens exhibit morphological changes during drug exposure and withdrawal. Moreover, these changes relate to the characteristic drug-seeking behavior of addiction. However, due to the complexity of dendritic spines, we do not yet fully understand the processes underlying their structural changes in response to different inputs. We propose that biophysical models can enhance the current understanding of these processes by incorporating different, and sometimes, discrepant experimental data to identify the shared underlying mechanisms and generate experimentally testable hypotheses. This review aims to give an up-to-date report on biophysical models of dendritic spines, focusing on those models that describe their shape changes, which are well-known to relate to learning and memory. Moreover, it examines how these models can enhance our understanding of the effect of the drugs and the synaptic changes during withdrawal, as well as during neurodegenerative disease progression such as Alzheimer’s disease.
The second harmonic generation in GaAs/GaAlAs spherical quantum dots under Woods-Saxon plus attractive inversely quadratic potential
Authors: Li Xuechao , Duan Yiming , Ma Yongrui
Published: May 2022

We examine the profile of second harmonic generation (SHG) for GaAs/GaAlAs spherical quantum dots (QDs) of Woods-Saxon (WS) plus attractive inversely quadratic (AIQ) potential under the joint influence of additional factors (pressure and temperature) and structural parameters (strengths and radius). The energies and wave functions in GaAs/GaAlAs spherical QDs under WS-AIQ limiting potential are calculated using the parametric Nikiforov-Uvarov (NU) method. Depending on the calculated energies and corresponding wave functions, the SHG coefficient is examined by the iterative procedure in the density matrix method for this system. Finally, the calculated results display that a strong SHG coefficient response, and red shift or blue shift energy can be acquired by adjusting parameters.
Emergence of associative learning in a neuromorphic inference network
Authors: Gandolfi Daniela , Puglisi Francesco M , Boiani Giulia M , Pagnoni Giuseppe , Friston Karl J , D’Angelo Egidio , Mapelli Jonathan
Published: May 2022

Objective. In the theoretical framework of predictive coding and active inference, the brain can be viewed as instantiating a rich generative model of the world that predicts incoming sensory data while continuously updating its parameters via minimization of prediction errors. While this theory has been successfully applied to cognitive processes—by modelling the activity of functional neural networks at a mesoscopic scale—the validity of the approach when modelling neurons as an ensemble of inferring agents, in a biologically plausible architecture, remained to be explored. Approach. We modelled a simplified cerebellar circuit with individual neurons acting as Bayesian agents to simulate the classical delayed eyeblink conditioning protocol. Neurons and synapses adjusted their activity to minimize their prediction error, which was used as the network cost function. This cerebellar network was then implemented in hardware by replicating digital neuronal elements via a low-power microcontroller. Main results. Persistent changes of synaptic strength—that mirrored neurophysiological observations—emerged via local (neurocentric) prediction error minimization, leading to the expression of associative learning. The same paradigm was effectively emulated in low-power hardware showing remarkably efficient performance compared to conventional neuromorphic architectures. Significance. These findings show that: (a) an ensemble of free energy minimizing neurons—organized in a biological plausible architecture—can recapitulate functional self-organization observed in nature, such as associative plasticity, and (b) a neuromorphic network of inference units can learn unsupervised tasks without embedding predefined learning rules in the circuit, thus providing a potential avenue to a novel form of brain-inspired artificial intelligence.
From unsupervised to semi-supervised adversarial domain adaptation in electroencephalography-based sleep staging
Authors: Heremans Elisabeth R M , Phan Huy , Borzée Pascal , Buyse Bertien , Testelmans Dries , De Vos Maarten
Published: May 2022

Objective. The recent breakthrough of wearable sleep monitoring devices has resulted in large amounts of sleep data. However, as limited labels are available, interpreting these data requires automated sleep stage classification methods with a small need for labeled training data. Transfer learning and domain adaptation offer possible solutions by enabling models to learn on a source dataset and adapt to a target dataset. Approach. In this paper, we investigate adversarial domain adaptation applied to real use cases with wearable sleep datasets acquired from diseased patient populations. Different practical aspects of the adversarial domain adaptation framework are examined, including the added value of (pseudo-)labels from the target dataset and the influence of domain mismatch between the source and target data. The method is also implemented for personalization to specific patients. Main results. The results show that adversarial domain adaptation is effective in the application of sleep staging on wearable data. When compared to a model applied on a target dataset without any adaptation, the domain adaptation method in its simplest form achieves relative gains of 7%–27% in accuracy. The performance in the target domain is further boosted by adding pseudo-labels and real target domain labels when available, and by choosing an appropriate source dataset. Furthermore, unsupervised adversarial domain adaptation can also personalize a model, improving the performance by 1%–2% compared to a non-personalized model. Significance. In conclusion, adversarial domain adaptation provides a flexible framework for semi-supervised and unsupervised transfer learning. This is particularly useful in sleep staging and other wearable electroencephalography applications. (Clinical trial registration number: S64190.)
Decoding neural activity preceding balance loss during standing with a lower-limb exoskeleton using an interpretable deep learning model
Authors: Sujatha Ravindran Akshay , Malaya Christopher A , John Isaac , Francisco Gerard E , Layne Charles , Contreras-Vidal Jose L
Published: May 2022

Objective: Falls are a leading cause of death in adults 65 and older. Recent efforts to restore lower-limb function in these populations have seen an increase in the use of wearable robotic systems; however, fall prevention measures in these systems require early detection of balance loss to be effective. Prior studies have investigated whether kinematic variables contain information about an impending fall, but few have examined the potential of using electroencephalography (EEG) as a fall-predicting signal and how the brain responds to avoid a fall. Approach: To address this, we decoded neural activity in a balance perturbation task while wearing an exoskeleton. We acquired EEG, electromyography (EMG), and center of pressure (COP) data from seven healthy participants during mechanical perturbations while standing. The timing of the perturbations was randomized in all trials. Main results: We found perturbation evoked potentials (PEP) components as early as 75–134 ms after the onset of the external perturbation, which preceded both the peak in EMG (∼180 ms) and the COP (∼350 ms). A convolutional neural network trained to predict balance perturbations from single-trial EEG had a mean F-score of 75.0 ± 4.3

%

. Clustering GradCAM-based model explanations demonstrated that the model utilized components in the PEP and was not driven by artifacts. Additionally, dynamic functional connectivity results agreed with model explanations; the nodal connectivity measured using phase difference derivative was higher in the occipital-parietal region in the early stage of perturbations, before shifting to the parietal, motor, and back to the frontal-parietal channels. Continuous-time decoding of COP trajectories from EEG, using a gated recurrent unit model, achieved a mean Pearson’s correlation coefficient of 0.7 ± 0.06. Significance: Overall, our findings suggest that EEG signals contain short-latency neural information related to an impending fall, which may be useful for developing brain-machine interface systems for fall prevention in robotic exoskeletons.
Peppermint protocol: first results for gas chromatography-ion mobility spectrometry
Authors: Ruszkiewicz Dorota M , Myers Renelle , Henderson Ben , Yusof Hazim , Meister Austin , Moreno Sergi , Eddleston Michael , Darnley Kareen , Nailon William H , McLaren Duncan , Lao Yvonne E , Hovda Knut Erik , Lam Stephen , Cristescu Simona M , Thomas C L Paul
Published: May 2022

The Peppermint Initiative seeks to inform the standardisation of breath analysis methods. Five Peppermint Experiments with gas chromatography-ion mobility spectrometry (GC-IMS), operating in the positive mode with a tritium 3H 5.68 keV, 370 MBq ionisation source, were undertaken to provide benchmark Peppermint Washout data for this technique, to support its use in breath-testing, analysis, and research. Headspace analysis of a peppermint-oil capsule by GC-IMS with on-column injection (0.5 cm3) identified 12 IMS responsive compounds, of which the four most abundant were: eucalyptol; β-pinene; α-pinene; and limonene. Elevated concentrations of these four compounds were identified in exhaled-breath following ingestion of a peppermint-oil capsule. An unidentified compound attributed as a volatile catabolite of peppermint-oil was also observed. The most intense exhaled peppermint-oil component was eucalyptol, which was selected as a peppermint marker for benchmarking GC-IMS. Twenty-five washout experiments monitored levels of exhaled eucalyptol, by GC-IMS with on-column injection (0.5 cm3), at t = 0 min, and then at t + 60, t + 90, t + 165, t + 285 and t + 360 min from ingestion of a peppermint capsule resulting in 148 peppermint breath analyses. Additionally, the Peppermint Washout data was used to evaluate clinical deployments with a further five washout tests run in clinical settings generating an additional 35 breath samples. Regression analysis yielded an average extrapolated time taken for exhaled eucalyptol levels to return to baseline values to be 429 ± 62 min (±95% confidence-interval). The benchmark value was assigned to the lower 95% confidence-interval, 367 min. Further evaluation of the data indicated that the maximum number of volatile organic compounds discernible from a 0.5 cm3 breath sample was 69, while the use of an in-line biofilter appeared to reduce this to 34.
Strategies for improving the use of Jakarta City Tour Bus in post-pandemic Covid-19 using IPA Method
Authors: Shakila C D , Erli K D M
Published: May 2022

The Jakarta City Tour Bus is one of the strategies of the DKI Jakarta Provincial Government to support the tourism sector. However, in recent years there has been little interest in running the Jakarta City Tour Bus. The lack of some service attributes of the Jakarta City Tour Bus performance and the new phase of adjustment during the Covid-19 pandemic are factors causing the decrease in the number of Jakarta City Tour Bus users since 2019. This study aims to formulate strategies to improve the use of the Jakarta City Tour Bus in the post-pandemic Covid-19 based on user preferences. The analytical method used in this research study is Importance-Performance Analysis (IPA) to formulate strategies for improving the use of Jakarta City Tour Bus in post-pandemic Covid-19. The results showed that there were 11 service attributes of the Jakarta City Tour Bus which had a low level of performance but had a high level of importance. Then, there are strategies to improve the use of Jakarta City Tour Bus which is divided into 2 (two) categories, including the strategies for improving the use of Jakarta City Tour Bus in normal and new normal situations.
Formulating emergency operation posts: lesson learned from Palu 2018 and Bantul 2006 indonesian earthquake events
Authors: Pamungkas A , Elisiyah I , Sianturi R S , Kurniawati U F , Avila B E , Larasati K D , Siswanto V K , Widiyanto I
Published: May 2022

Emergency operation posts play significant roles during emergency response. Unprepared emergency operation posts are likely to lead to poor coordination, a chaotic evacuation process, and uneven aids distribution, worsening disaster impacts. Reflecting on earthquake events in Indonesia, this paper examines emergency operation posts and equipment needed in those emergency operation posts during disaster events to improve emergency management. The present paper investigates government regulations and performs Focus Group Discussions (FGDs) in Palu, Bantul, and Surabaya to achieve the research goal. This study finds that six emergency operation posts, including Command Post, Field Post, Support Post, Associate Post, Communal Tent, and Individual Tent, are required to improve evacuation processes, multi-sectoral coordination, and aid distributions. Finally, the present study suggested that each post must be facilitated with adequate equipment, categorized into basic equipment, data collection equipment, and coordination equipment to support emergency response activities.
Spatial interaction modeling for assessment and determination Covid-19 handling policy in control of the pandemic spread between regions: A case study in the Surabaya Metropolitan Area
Authors: Santoso E B , Syafitri R A W D , Desiana T A
Published: May 2022

The COVID-19 pandemic influences human health and activities in many regions in Indonesia. East Java is an area with high community interaction, and mobility is one of the provinces that implements a regional restriction policy. The people mobility among regions makes it necessary to control the people interaction during the pandemic. The regional restriction strategy is one way to reduce and avoid the spread of COVID-19 in the Surabaya Metropolitan Area (Surabaya, Gresik, and Sidoarjo). This study aims to assess the COVID-19 control policy based on the interaction of activities between regions. The analysis uses a projected model of the spread of COVID-19 and a spatial interaction model based on the spread pattern of COVID-19 in the Surabaya Metropolitan Area. The research variables consisted of the health facilities, population, case data in a certain period, positive case data, patient treatment, and road network. The data were analyzed using the SurgeCDC Model and the Gravity Model furthermore. The results of the study provide the effectiveness of the policies to be carried out, predictions of the end of the pandemic in an area, descriptions of cases when using policy interventions, and conditions of spatial interaction. There needs to be an anticipation of the demand for health facilities such as medical personnel, medicine, and care rooms.
Switchable metamaterials for broadband absorption and generation of vector beams
Authors: Zhang Chunyu , Ling Fang , Zhang Bin
Published: May 2022

Metamaterial (MM) based on double-split resonator and vanadium dioxide (VO2) in terahertz (THz) region is proposed in this paper, which integrates broadband absorption (ABS) and generation of vector beams. Simulation results show that by tuning the VO2 into metallic phase, the ABS of proposed MMs achieves over 90% in 1.35–2.18 THz. The physical mechanism of broadband ABS is clarified by introducing the impedance matching theory and distributions of electric field. Moreover, ABS characteristics of the designed MMs at different polarization angles and incident angles are also studied, respectively. While VO2 is in insulated state, simulation results indicate that the radial polarized beam and the angular polarized beam can be generated when the incident wave is x-polarization and y-polarization, respectively. The polarization conversion rate over 90% is realized between 1.37 THz and 2.20 THz within the incident angle of 10°. The bi-functional MMs proposed in this paper has promising applications in modulators, imaging and sensors.
Logarithmic Riemann–Hilbert correspondences for rigid varieties
Authors: Diao Hansheng , Lan Kai-Wen , Liu Ruochuan , Zhu Xinwen
Published: May 2022

On any smooth algebraic variety over a

p
p

-adic local field, we construct a tensor functor from the category of de Rham

p
p

-adic étale local systems to the category of filtered algebraic vector bundles with integrable connections satisfying the Griffiths transversality, which we view as a

p
p

-adic analogue of Deligne’s classical Riemann–Hilbert correspondence. A crucial step is to construct canonical extensions of the desired connections to suitable compactifications of the algebraic variety with logarithmic poles along the boundary, in a precise sense characterized by the eigenvalues of residues; hence the title of the paper. As an application, we show that this

p
p

-adic Riemann–Hilbert functor is compatible with the classical one over all Shimura varieties, for local systems attached to representations of the associated reductive algebraic groups.
Integral points on the congruent number curve
Authors: Chan Stephanie
Published: May 2022

We study integral points on the quadratic twists

E

D

:

y
2

=

x
3

−

D
2

x

\mathcal {E}_D:y^2=x^3-D^2x

of the congruent number curve. We give upper bounds on the number of integral points in each coset of

2

E

D

(

Q

)

2\mathcal {E}_D(\mathbb {Q})

in

E

D

(

Q

)

\mathcal {E}_D(\mathbb {Q})

and show that their total is

≪
(
3.8

)

rank
⁡

E

D

(

Q

)

\ll (3.8)^{\operatorname {rank} \mathcal {E}_D(\mathbb {Q})}

. We further show that the average number of non-torsion integral points in this family is bounded above by

2
2

. As an application we also deduce from our upper bounds that the system of simultaneous Pell equations

a

X
2

−
b

Y
2

=
d
,
 
b

Y
2

−
c

Z
2

=
d

aX^2-bY^2=d,\ bY^2-cZ^2=d

for pairwise coprime positive integers

a
,
b
,
c
,
d

a,b,c,d

, has at most

≪
(
3.6

)

ω
(
a
b
c
d
)

\ll (3.6)^{\omega (abcd)}

integer solutions.
Association of prediabetes with death and diabetic complications in older adults: the pros and cons of active screening for prediabetes
Authors: Choi Giwoong , Yoon Hojun , Choi Hyun Ho , Ha Kyoung Hwa , Kim Dae Jung
Published: May 2022

Background

active screening can improve health outcomes for people with prediabetes. However, its efficacy in older adults remains uncertain.

Objective

the study aimed to analyse the progression from prediabetes to diabetes in older adults, including associated complications and mortality rates, to determine the benefits of active screening.

Design

retrospective cohort study.

Setting

Korean National Health Insurance Service claims database.

Subjects

a total of 36,946 adults aged ≥65 years who underwent national health examinations from 2006 to 2008.

Methods

follow-up was until 31 December 2015. Cox’s proportional hazards models estimated hazard ratios (HRs) and 95% confidence intervals (CIs) for death and diabetic complications.

Results

over a median follow-up of 8.2 years, 19.3% of older adults with prediabetes progressed to diabetes. Compared with normoglycaemic adults, the multivariable-adjusted HRs (95% CI) for major adverse cardiovascular events were 1.01 (0.95–1.07), 1.08 (0.95–1.23), 1.14 (1.05–1.23) and 1.50 (1.35–1.68) in adults with prediabetes, newly diagnosed diabetes, diabetes not on insulin and diabetes on insulin, respectively. The corresponding HRs (95% CI) for diabetic retinopathy risk were 1.28 (1.15–1.43), 3.16 (2.71–3.69), 6.58 (6.02–7.19) and 9.17 (8.21–10.24). Diabetic nephropathy risk also significantly increased.

Conclusions

progression from prediabetes to diabetes is an important concern among older adults. Prediabetes is associated with the risk of microvascular complications, but not cardiovascular complications and mortality. Therefore, active regular screening for prediabetes is necessary to prevent microvascular complications.
Apple rootstocks affect functional leaf traits with consequential effects on carbon isotope composition and vegetative vigour
Authors: Casagrande Biasuz Erica , Kalcsits Lee A
Published: May 2022

Composite trees combine optimal traits from both the rootstock and the scion. Dwarfing rootstocks are commonly used to reduce shoot vigour and improve fruit quality and productivity. Although growth habits of different rootstocks have been clearly described, the underlying physiological traits affecting scion vigour are not well understood. Plant water status and stem water potential are strongly influenced by water supply and demand through the soil–plant–atmosphere continuum. In the scion, stomata regulate water loss and are essential to prevent hydraulic failure. Stomatal conductance influences leaf carbon isotope composition. Combined, the effects of reduced stomatal conductance and, consequently, carbon fixation may affect tree growth. These differences could also correspond to differences in scion vigour controlled by rootstock genotype. Here, vegetative growth, gas exchange, stem water potential and leaf δ13C were compared to determine how rootstocks affect scion water relations and whether these differences correspond to shoot vigour. There was a range in vigour among rootstocks by almost 2-fold. Net leaf carbon assimilation rates were lower in rootstocks with lower vigour. Rootstock vigour was closely associated with leaf gas exchange and stem water potential in the scion and was reflected in leaf δ13C signatures. Dwarfing was strongly affected by changes to plant water status induced by rootstock genotype and these changes are distinguishable when measuring leaf and stem δ13C composition. These observations indicate that scion water relations and leaf carbon isotope discrimination were affected by rootstock genotype. These results have implications for better understanding dwarfing mechanisms in apple rootstocks and the relationship with water-use traits.
Digital Methods of Delivering Education and Training in Neuropsychology
Authors: Van Patten Ryan , Bellone John A , Schmitt Taylor R , Gaynor Leslie , Block Cady
Published: May 2022

Objective

High quality and accessible education is crucial to secure the future of neuropsychology. Recent and ongoing advancements include the creation of a taxonomy of education and training, progress in delineating relevant competencies, and an update to the Houston Conference Guidelines. Meanwhile, there is also potential for growth in digital methods of delivering education such as podcasts, webinars, and social media platforms. This paper aims to review the evidence for these three methods and to provide recommendations for implementing them across the continuum of training in neuropsychology, from graduate school through postdoctoral fellowship and continuing education.

Method

We reviewed the literature on podcasts, webinars, and social media platforms as educational tools that may be applied to improve learning of brain–behavior concepts and clinical skills. Specifically, we (a) introduce and describe each method, (b) discuss benefits and drawbacks, (c) review the literature on satisfaction and learning outcomes, and (d) provide suggestions for implementation in neuropsychology training programs.

Results

Podcasts and webinars have strong evidence for satisfaction and moderate support for improving learning outcomes, with the learning outcome literature suggesting equivalency between podcasts/webinars and traditional methods of education. Social media platforms are associated with high satisfaction, but learning outcome data are scarce.

Conclusions

Evidence for podcasts and webinars is sufficient to support implementation into neuropsychology education, whereas social media platforms should be used more cautiously. We provide recommendations of select instances in which trainees and educators can use these methods to improve education in neuropsychology.
BETA: a comprehensive benchmark for computational drug–target prediction
Authors: Zong Nansu , Li Ning , Wen Andrew , Ngo Victoria , Yu Yue , Huang Ming , Chowdhury Shaika , Jiang Chao , Fu Sunyang , Weinshilboum Richard , Jiang Guoqian , Hunter Lawrence , Liu Hongfang
Published: May 2022

Internal validation is the most popular evaluation strategy used for drug–target predictive models. The simple random shuffling in the cross-validation, however, is not always ideal to handle large, diverse and copious datasets as it could potentially introduce bias. Hence, these predictive models cannot be comprehensively evaluated to provide insight into their general performance on a variety of use-cases (e.g. permutations of different levels of connectiveness and categories in drug and target space, as well as validations based on different data sources). In this work, we introduce a benchmark, BETA, that aims to address this gap by (i) providing an extensive multipartite network consisting of 0.97 million biomedical concepts and 8.5 million associations, in addition to 62 million drug–drug and protein–protein similarities and (ii) presenting evaluation strategies that reflect seven cases (i.e. general, screening with different connectivity, target and drug screening based on categories, searching for specific drugs and targets and drug repurposing for specific diseases), a total of seven Tests (consisting of 344 Tasks in total) across multiple sampling and validation strategies. Six state-of-the-art methods covering two broad input data types (chemical structure- and gene sequence-based and network-based) were tested across all the developed Tasks. The best-worst performing cases have been analyzed to demonstrate the ability of the proposed benchmark to identify limitations of the tested methods for running over the benchmark tasks. The results highlight BETA as a benchmark in the selection of computational strategies for drug repurposing and target discovery.
QuasiSeq: profiling viral quasispecies via self-tuning spectral clustering with PacBio long sequencing reads
Authors: Jiao Xiaoli , Imamichi Hiromi , Sherman Brad T , Nahar Rishub , Dewar Robin L , Lane H Clifford , Imamichi Tomozumi , Chang Weizhong
Published: May 2022

Motivation

The existence of quasispecies in the viral population causes difficulties for disease prevention and treatment. High-throughput sequencing provides opportunity to determine rare quasispecies and long sequencing reads covering full genomes reduce quasispecies determination to a clustering problem. The challenge is high similarity of quasispecies and high error rate of long sequencing reads.

Results

We developed QuasiSeq using a novel signature-based self-tuning clustering method, SigClust, to profile viral mixtures with high accuracy and sensitivity. QuasiSeq can correctly identify quasispecies even using low-quality sequencing reads (accuracy <80%) and produce quasispecies sequences with high accuracy (≥99.55%). Using high-quality circular consensus sequencing reads, QuasiSeq can produce quasispecies sequences with 100% accuracy. QuasiSeq has higher sensitivity and specificity than similar published software. Moreover, the requirement of the computational resource can be controlled by the size of the signature, which makes it possible to handle big sequencing data for rare quasispecies discovery. Furthermore, parallel computation is implemented to process the clusters and further reduce the runtime. Finally, we developed a web interface for the QuasiSeq workflow with simple parameter settings based on the quality of sequencing data, making it easy to use for users without advanced data science skills.

Availability and implementation

QuasiSeq is open source and freely available at https://github.com/LHRI-Bioinformatics/QuasiSeq. The current release (v1.0.0) is archived and available at https://zenodo.org/badge/latestdoi/340494542.

Supplementary information

Supplementary data are available at Bioinformatics online.
The Olfactory Landscape Concept: A Key Source of Past, Present, and Future Information Driving Animal Movement and Decision-making
Authors: Finnerty Patrick B , McArthur Clare , Banks Peter , Price Catherine , Shrader Adrian M
Published: May 2022

Odor is everywhere, emitted across the landscape from predators, prey, decaying carcasses, conspecifics, vegetation, surface water, and smoke. Many animals exploit odor to find food, avoid threats, and attract or judge potential mates. Here, we focus on odor in terrestrial ecosystems to introduce the concept of an olfactory landscape: real-time dynamic olfactory contours reflecting the patchy distribution of resources and risks, providing a key source of information used by many animals in their movement and decision-making. Incorporating the olfactory landscape into current frameworks of movement ecology and animal behavior will provide a mechanistic link to help answer significant questions about where, why, and when many animals move, and how they do so efficiently in both space and time. By understanding how animals use the olfactory landscape to make crucial decisions affecting their fitness, we can then manipulate the landscape to modify ecological interactions and, ultimately, ecosystem consequences of these interactions.