bluuebunny/crossref_metadata_2025_split huggingface dataset:
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

Abstract
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

Abstract
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

Abstract

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

Abstract
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

Abstract
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

Abstract

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

Abstract

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

Abstract

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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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.
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.