bluuebunny/crossref_metadata_2025_split huggingface dataset:
Physics-informed learning of governing equations from scarce data
Authors: Chen Zhao , Liu Yang , Sun Hao 
Published: October 2021

AbstractHarnessing data to discover the underlying governing laws or equations that describe the behavior of complex physical systems can significantly advance our modeling, simulation and understanding of such systems in various science and engineering disciplines. This work introduces a novel approach called physics-informed neural network with sparse regression to discover governing partial differential equations from scarce and noisy data for nonlinear spatiotemporal systems. In particular, this discovery approach seamlessly integrates the strengths of deep neural networks for rich representation learning, physics embedding, automatic differentiation and sparse regression to approximate the solution of system variables, compute essential derivatives, as well as identify the key derivative terms and parameters that form the structure and explicit expression of the equations. The efficacy and robustness of this method are demonstrated, both numerically and experimentally, on discovering a variety of partial differential equation systems with different levels of data scarcity and noise accounting for different initial/boundary conditions. The resulting computational framework shows the potential for closed-form model discovery in practical applications where large and accurate datasets are intractable to capture.
1,1'-Carbonyldiimidazole-copper nanoflower enhanced collapsible laser scribed graphene engraved microgap capacitive aptasensor for the detection of milk allergen
Authors: Subramani Indra Gandi , Perumal Veeradasan , Gopinath Subash C. B. , Mohamed Norani Muti , Ovinis Mark , Sze Lim Li 
Published: October 2021

AbstractThe bovine milk allergenic protein, ‘β-lactoglobulin’ is one of the leading causes of milk allergic reaction. In this research, a novel label-free non-faradaic capacitive aptasensor was designed to detect β-lactoglobulin using a Laser Scribed Graphene (LSG) electrode. The graphene was directly engraved into a microgapped (~ 95 µm) capacitor-electrode pattern on a flexible polyimide (PI) film via a simple one-step CO2 laser irradiation. The novel hybrid nanoflower (NF) was synthesized using 1,1′-carbonyldiimidazole (CDI) as the organic molecule and copper (Cu) as the inorganic molecule via one-pot biomineralization by tuning the reaction time and concentration. NF was fixed on the pre-modified PI film at the triangular junction of the LSG microgap specifically for bio-capturing β-lactoglobulin. The fine-tuned CDI-Cu NF revealed the flower-like structures was viewed through field emission scanning electron microscopy. Fourier-transform infrared spectroscopy showed the interactions with PI film, CDI-Cu NF, oligoaptamer and β-lactoglobulin. The non-faradaic sensing of milk allergen β-lactoglobulin corresponds to a higher loading of oligoaptamer on 3D-structured CDI-Cu NF, with a linear range detection from 1 ag/ml to 100 fg/ml and attomolar (1 ag/ml) detection limit (S/N = 3:1). This novel CDI-Cu NF/LSG microgap aptasensor has a great potential for the detection of milk allergen with high-specificity and sensitivity.
RETRACTED ARTICLE: Exploring the potential effect of COVID-19 on an endangered great ape
Authors: Colchero Fernando , Eckardt Winnie , Stoinski Tara 
Published: October 2021

AbstractThe current COVID-19 pandemic has created unmeasurable damages to society at a global level, from the irreplaceable loss of life, to the massive economic losses. In addition, the disease threatens further biodiversity loss. Due to their shared physiology with humans, primates, and particularly great apes, are susceptible to the disease. However, it is still uncertain how their populations would respond in case of infection. Here, we combine stochastic population and epidemiological models to simulate the range of potential effects of COVID-19 on the probability of extinction of mountain gorillas. We find that extinction is sharply driven by increases in the basic reproductive number and that the probability of extinction is greatly exacerbated if the immunity lasts less than 6 months. These results stress the need to limit exposure of the mountain gorilla population, the park personnel and visitors, as well as the potential of vaccination campaigns to extend the immunity duration.
2D-to-3D image translation of complex nanoporous volumes using generative networks
Authors: Anderson Timothy I. , Vega Bolivia , McKinzie Jesse , Aryana Saman A. , Kovscek Anthony R. 
Published: October 2021

AbstractImage-based characterization offers a powerful approach to studying geological porous media at the nanoscale and images are critical to understanding reactive transport mechanisms in reservoirs relevant to energy and sustainability technologies such as carbon sequestration, subsurface hydrogen storage, and natural gas recovery. Nanoimaging presents a trade off, however, between higher-contrast sample-destructive and lower-contrast sample-preserving imaging modalities. Furthermore, high-contrast imaging modalities often acquire only 2D images, while 3D volumes are needed to characterize fully a source rock sample. In this work, we present deep learning image translation models to predict high-contrast focused ion beam-scanning electron microscopy (FIB-SEM) image volumes from transmission X-ray microscopy (TXM) images when only 2D paired training data is available. We introduce a regularization method for improving 3D volume generation from 2D-to-2D deep learning image models and apply this approach to translate 3D TXM volumes to FIB-SEM fidelity. We then segment a predicted FIB-SEM volume into a flow simulation domain and calculate the sample apparent permeability using a lattice Boltzmann method (LBM) technique. Results show that our image translation approach produces simulation domains suitable for flow visualization and allows for accurate characterization of petrophysical properties from non-destructive imaging data.
An experimental look at trust, bargaining, and public goods in fishing communities
Authors: Rojas Cristian A. , Cinner Joshua , Lau Jacqueline , Ruano-Chamorro Cristina , Contreras-Drey Francisco J. , Gelcich Stefan 
Published: October 2021

AbstractPro-social behavior is crucial to the sustainable governance of common-pool resources such as fisheries. Here, we investigate how key socioeconomic characteristics influence fishers’ pro-social and bargaining behavior in three types of experimental economic games (public goods, trust, and trade) conducted in fishing associations in Chile. Our games revealed high levels of cooperation in the public goods game, a high degree of trust, and that sellers rather than buyers had more bargaining power, yet these results were strongly influenced by participants’ socioeconomic characteristics. Specifically, gender, having a secondary income source, age, and being the main income provider for the household all had a relationship to multiple game outcomes. Our results highlight that engagement in pro-social behaviors such as trust and cooperation can be influenced by people’s socioeconomic context.
Reaction mechanisms for electrolytic manganese dioxide in rechargeable aqueous zinc-ion batteries
Authors: Tran Thuy Nguyen Thanh , Jin Susi , Cuisinier Marine , Adams Brian D. , Ivey Douglas G. 
Published: October 2021

AbstractThis study reports the phase transformation behaviour associated with electrolytic manganese dioxide (EMD) utilized as the positive electrode active material for aqueous zinc-ion batteries. Electrochemical techniques, including galvanostatic charge–discharge and rotating ring-disk electrode measurements, and microstructural techniques, using X-ray powder diffraction, scanning electron microscopy, and transmission/scanning transmission electron microscopy, were utilized to characterize the positive electrode at different stages of discharge and charge of zinc-ion cells. The results indicate that, during discharge, a fraction of EMD undergoes a transformation to ZnMn2O4 (spinel-type) and Zn2+ is intercalated into the tunnels of the γ- and ε-MnO2 phases, forming ZnxMnO2 (tunnel-type). When a critical concentration of Mn3+ in the intercalated ZnxMnO2 species is reached, a disproportionation/dissolution reaction is triggered leading to the formation of soluble Mn2+ and hydroxide (OH–) ions; the latter precipitates as zinc hydroxide sulfate (ZHS, Zn4(OH)6(SO4)·5H2O) by combination with the ZnSO4/H2O electrolyte. During charge, Zn2+ is reversibly deintercalated from the intergrown tunneled phases (γ-/ε-ZnxMnO2), Mn2+ is redeposited as layered chalcophanite (ZnMn3O7·3H2O), and ZHS is decomposed by protons (H+) formed during the electrochemical deposition of chalcophanite.
Sex-differences in COVID-19 associated excess mortality is not exceptional for the COVID-19 pandemic
Authors: Nielsen Jens , Nørgaard Sarah K. , Lanzieri Giampaolo , Vestergaard Lasse S. , Moelbak Kaare 
Published: October 2021

AbstractEurope experienced excess mortality from February through June, 2020 due to the COVID-19 pandemic, with more COVID-19-associated deaths in males compared to females. However, a difference in excess mortality among females compared to among males may be a more general phenomenon, and should be investigated in none-COVID-19 situations as well. Based on death counts from Eurostat, separate excess mortalities were estimated for each of the sexes using the EuroMOMO model. Sex-differential excess mortality were expressed as differences in excess mortality incidence rates between the sexes. A general relation between sex-differential and overall excess mortality both during the COVID-19 pandemic and in preceding seasons were investigated. Data from 27 European countries were included, covering the seasons 2016/17 to 2019/20. In periods with increased excess mortality, excess was consistently highest among males. From February through May 2020 male excess mortality was 52.7 (95% PI: 56.29; 49.05) deaths per 100,000 person years higher than for females. Increased male excess mortality compared to female was also observed in the seasons 2016/17 to 2018/19. We found a linear relation between sex-differences in excess mortality and overall excess mortality, i.e., 40 additional deaths among males per 100 excess deaths per 100,000 population. This corresponds to an overall female/male mortality incidence ratio of 0.7. In situations with overall excess mortality, excess mortality increases more for males than females. We suggest that the sex-differences observed during the COVID-19 pandemic reflects a general sex-disparity in excess mortality.
The impact of recency and adequacy of historical information on sepsis predictions using machine learning
Authors: Zargoush Manaf , Sameh Alireza , Javadi Mahdi , Shabani Siyavash , Ghazalbash Somayeh , Perri Dan 
Published: October 2021

AbstractSepsis is a major public and global health concern. Every hour of delay in detecting sepsis significantly increases the risk of death, highlighting the importance of accurately predicting sepsis in a timely manner. A growing body of literature has examined developing new or improving the existing machine learning (ML) approaches for timely and accurate predictions of sepsis. This study contributes to this literature by providing clear insights regarding the role of the recency and adequacy of historical information in predicting sepsis using ML. To this end, we implemented a deep learning model using a bidirectional long short-term memory (BiLSTM) algorithm and compared it with six other ML algorithms based on numerous combinations of the prediction horizons (to capture information recency) and observation windows (to capture information adequacy) using different measures of predictive performance. Our results indicated that the BiLSTM algorithm outperforms all other ML algorithms and provides a great separability of the predicted risk of sepsis among septic versus non-septic patients. Moreover, decreasing the prediction horizon (in favor of information recency) always boosts the predictive performance; however, the impact of expanding the observation window (in favor of information adequacy) depends on the prediction horizon and the purpose of prediction. More specifically, when the prediction is responsive to the positive label (i.e., Sepsis), increasing historical data improves the predictive performance when the prediction horizon is short-moderate.
RETRACTED ARTICLE: Synthesis of silver nanoparticles using Plantago lanceolata extract and assessing their antibacterial and antioxidant activities
Authors: Shah Muhammad Zahir , Guan Zheng-Hui , Din Ala Ud , Ali Amjad , Rehman Ata Ur , Jan Kashif , Faisal Shah , Saud Shah , Adnan Muhammad , Wahid Fazli , Alamri Saud , Siddiqui Manzer H. , Ali Shamsher , Nasim Wajid , Hammad Hafiz Mohkum , Fahad Shah 
Published: October 2021

Abstract
Silver nanoparticles (Ag. NPs) have shown a biological activity range, synthesized under different environment-friendly approaches. Ag. NPs were synthesized using aqueous crude extract (ACE) isolated from Plantago lanceolata. The ACE and Ag. NPs were characterized and assessed their biological and antioxidant activities. The existence of nanoparticles (NPs) was confirmed by color shift, atomic force microscopy (AFM), and UV–Vis’s spectroscopy. The FT-IR analysis indicated the association of biomolecules (phenolic acid and flavonoids) to reduce silver (Ag+) ions. The SEM study demonstrated a sphere-shaped and mean size in the range of 30 ± 4 nm. The EDX spectrum revealed that the Ag. NPs were composed of 54.87% Ag with 20 nm size as identified by SEM and TEM. AFM has ended up being exceptionally useful in deciding morphological elements and the distance across of Ag. NPs in the scope of 23–30 nm. The TEM image showed aggregations of NPs and physical interaction. Ag. NPs formation also confirmed by XPS, DRS and BET studies. Ag. NPs showed efficient activity as compared to ACE, and finally, the bacterial growth was impaired by biogenic NPs. The lethal dose (LD50) of Ag. NPs against Agrobacterium tumefaciens, Proteus vulgaris, Staphylococcus aureus, and Escherichia coli were 45.66%, 139.71%, 332.87%, and 45.54%, with IC50 (08.02 ± 0.68), (55.78 ± 1.01), (12.34 ± 1.35) and (11.68 ± 1.42) respectively, suppressing the growth as compared to ACE. The antioxidant capacity, i.e., 2,2-diphenyl-1-picrylhydrazyl (DPPH) of Ag. NPs were assayed. ACE and Ag. NPs achieved a peak antioxidant capacity of 62.43 ± 2.4 and 16.85 ± 0.4 μg mL−1, compared to standard (69.60 ± 1.1 at 100 μg mL−1) with IC50 (369.5 ± 13.42 and 159.5 ± 10.52 respectively). Finally, the Ag. NPs synthesized by P. lanceolata extract have an excellent source of bioactive natural products (NP). Outstanding antioxidant, antibacterial activities have been shown by NPs and can be used in various biological techniques in future research.
A learning mechanism shaping risk preferences and a preliminary test of its relationship with psychopathic traits
Authors: Oba Takeyuki , Katahira Kentaro , Ohira Hideki 
Published: October 2021

AbstractPeople tend to avoid risk in the domain of gains but take risks in the domain of losses; this is called the reflection effect. Formal theories of decision-making have provided important perspectives on risk preferences, but how individuals acquire risk preferences through experiences remains unknown. In the present study, we used reinforcement learning (RL) models to examine the learning processes that can shape attitudes toward risk in both domains. In addition, relationships between learning parameters and personality traits were investigated. Fifty-one participants performed a learning task, and we examined learning parameters and risk preference in each domain. Our results revealed that an RL model that included a nonlinear subjective utility parameter and differential learning rates for positive and negative prediction errors exhibited better fit than other models and that these parameters independently predicted risk preferences and the reflection effect. Regarding personality traits, although the sample sizes may be too small to test personality traits, increased primary psychopathy scores could be linked with decreased learning rates for positive prediction error in loss conditions among participants who had low anxiety traits. The present findings not only contribute to understanding how decision-making in risky conditions is influenced by past experiences but also provide insights into certain psychiatric problems.
Outcomes after ticagrelor versus clopidogrel treatment in end-stage renal disease patients with acute myocardial infarction: a nationwide cohort study
Authors: Tung Ying-Chang , Chang Chi-Jen , Liu Jia-Rou , Chang Shu-Hao , Chan Yi-Hsin , Kuo Chi-Tai , See Lai-Chu 
Published: October 2021

AbstractClinical outcomes are unknown after ticagrelor treatment in patients with end-stage renal disease (ESRD) who are diagnosed with acute myocardial infarction (AMI). ESRD patients who were on hemodialysis and received dual antiplatelet therapy (DAPT) for AMI between July 2013 and December 2016 were identified in Taiwan's National Health Insurance Research Database. Using stabilized inverse probability of treatment weighting, patients receiving aspirin plus ticagrelor (n = 530) were compared with those receiving aspirin plus clopidogrel (n = 2462) for the primary efficacy endpoint, a composite of all-cause death, nonfatal myocardial infarction, or nonfatal stroke, and bleeding, defined according to the Bleeding Academic Research Consortium. Study outcomes were compared between the two groups using Cox proportional hazards model or competing risk model for the hazard ratio or subdistribution hazard ratio (SHR). During 9 months of follow-up, ticagrelor was comparable to clopidogrel with respect to the risks of primary efficacy endpoint [11.69 vs. 9.28/100 patient-months; SHR, 1.16; 95% confidence interval (CI) 0.97–1.4] and bleeding (5.55 vs. 4.36/100 patient-months; SHR 1.14; 95% CI 0.88–1.47). In conclusion, among hemodialysis patients receiving DAPT for AMI, ticagrelor was comparable to clopidogrel with regard to the composite efficacy endpoint and bleeding.
EEG-derived brain graphs are reliable measures for exploring exercise-induced changes in brain networks
Authors: Büchel Daniel , Lehmann Tim , Sandbakk Øyvind , Baumeister Jochen 
Published: October 2021

AbstractThe interaction of acute exercise and the central nervous system evokes increasing interest in interdisciplinary research fields of neuroscience. Novel approaches allow to monitor large-scale brain networks from mobile electroencephalography (EEG) applying graph theory, but it is yet uncertain whether brain graphs extracted after exercise are reliable. We therefore aimed to investigate brain graph reliability extracted from resting state EEG data before and after submaximal exercise twice within one week in male participants. To obtain graph measures, we extracted global small-world-index (SWI), clustering coefficient (CC) and characteristic path length (PL) based on weighted phase leg index (wPLI) and spectral coherence (Coh) calculation. For reliability analysis, Intraclass-Correlation-Coefficient (ICC) and Coefficient of Variation (CoV) were computed for graph measures before (REST) and after POST) exercise. Overall results revealed poor to excellent measures at PRE and good to excellent ICCs at POST in the theta, alpha-1 and alpha-2, beta-1 and beta-2 frequency band. Based on bootstrap-analysis, a positive effect of exercise on reliability of wPLI based measures was observed, while exercise induced a negative effect on reliability of Coh-based graph measures. Findings indicate that brain graphs are a reliable tool to analyze brain networks in exercise contexts, which might be related to the neuroregulating effect of exercise inducing functional connections within the connectome. Relative and absolute reliability demonstrated good to excellent reliability after exercise. Chosen graph measures may not only allow analysis of acute, but also longitudinal studies in exercise-scientific contexts.
Field-induced vortex-like textures as a probe of the critical line in reentrant spin glasses
Authors: Martin N. , Bannenberg L. J. , Deutsch M. , Pappas C. , Chaboussant G. , Cubitt R. , Mirebeau I. 
Published: October 2021

AbstractWe study the evolution of the low-temperature field-induced magnetic defects observed under an applied magnetic field in a series of frustrated amorphous ferromagnets (Fe$$_{1-x}$$

1
-
x

Mn$$_{x}$$

x

)$$_{75}$$

75

P$$_{16}$$

16

B$$_{3}$$

3

Al$$_{3}$$

3

(“a-Fe$$_{1-x}$$

1
-
x

Mn$$_{x}$$

x

”). Combining small-angle neutron scattering and Monte Carlo simulations, we show that the morphology of these defects resemble that of quasi-bidimensional spin vortices. They are observed in the so-called “reentrant” spin-glass (RSG) phase, up to the critical concentration $$x_{\mathrm{C}} \approx 0.36$$

x
C

≈
0.36

which separates the RSG and “true” spin glass (SG) within the low temperature part of the magnetic phase diagram of a-Fe1−xMnx. These textures systematically decrease in size with increasing magnetic field or decreasing the average exchange interaction, and they finally disappear in the SG sample ($$x = 0.41$$

x
=
0.41

), being replaced by field-induced correlations over finite length scales. We argue that the study of these nanoscopic defects could be used to probe the critical line between the RSG and SG phases.
Determining spatio-temporal characteristics of coseismic travelling ionospheric disturbances (CTID) in near real-time
Authors: Maletckii Boris , Astafyeva Elvira 
Published: October 2021

AbstractEarthquakes are known to generate ionospheric disturbances that are commonly referred to as co-seismic travelling ionospheric disturbances (CTID). In this work, for the first time, we present a novel method that enables to automatically detect CTID in ionospheric GNSS-data, and to determine their spatio-temporal characteristics (velocity and azimuth of propagation) in near-real time (NRT), i.e., less than 15 min after an earthquake. The obtained instantaneous velocities allow us to understand the evolution of CTID and to estimate the location of the CTID source in NRT. Furthermore, also for the first time, we developed a concept of real-time travel-time diagrams that aid to verify the correlation with the source and to estimate additionally the propagation speed of the observed CTID. We apply our methods to the Mw7.4 Sanriku earthquake of 09/03/2011 and the Mw9.0 Tohoku earthquake of 11/03/2011, and we make a NRT analysis of the dynamics of CTID driven by these seismic events. We show that the best results are achieved with high-rate 1 Hz data. While the first tests are made on CTID, our method is also applicable for detection and determining of spatio-temporal characteristics of other travelling ionospheric disturbances that often occur in the ionosphere driven by many geophysical phenomena.
Experimental realization of a Fresnel hologram as a super spectral resolution optical element
Authors: Hsieh Mei-Li , Ditto Thomas D. , Lee Yi-Wen , Lin Shiuan-Huei , Newberg Heidi J. , Lin Shawn-Yu 
Published: October 2021

AbstractA highly dispersive, diffractive optical element is designed and realized for an extremely high spectral resolution spectroscopy for exoplanet telescope application. Our design uses an annular Fresnel hologram to transform incident starlight directly into a spectrogram. The recording of the hologram is accomplished using two spherical waves of different radius of curvature. The resultant hologram consists of an annular grating structure with a gradually shrinking period as a function of increasing radius. The variable period not only could bring the incoming star-light into focus, but also exhibits a large on-axis chromatic behavior. We demonstrate a dispersion of wavelength 430–700 nm over 190 mm on-axis distance, leading to a super fine spectral resolution 0.0266 nm at wavelength 515 nm for a detector size of 20 µm.