bluuebunny/crossref_metadata_2025_split huggingface dataset:
Graphene oxide based synaptic memristor device for neuromorphic computing
Authors: Sahu Dwipak Prasad , Jetty Prabana , Jammalamadaka S Narayana 
Published: January 2021

Abstract
Brain-inspired neuromorphic computing which consist neurons and synapses, with an ability to perform complex information processing has unfolded a new paradigm of computing to overcome the von Neumann bottleneck. Electronic synaptic memristor devices which can compete with the biological synapses are indeed significant for neuromorphic computing. In this work, we demonstrate our efforts to develop and realize the graphene oxide (GO) based memristor device as a synaptic device, which mimic as a biological synapse. Indeed, this device exhibits the essential synaptic learning behavior including analog memory characteristics, potentiation and depression. Furthermore, spike-timing-dependent-plasticity learning rule is mimicked by engineering the pre- and post-synaptic spikes. In addition, non-volatile properties such as endurance, retentivity, multilevel switching of the device are explored. These results suggest that Ag/GO/fluorine-doped tin oxide memristor device would indeed be a potential candidate for future neuromorphic computing applications.
Head-and-neck organs-at-risk auto-delineation using dual pyramid networks for CBCT-guided adaptive radiotherapy
Authors: Dai Xianjin , Lei Yang , Wang Tonghe , Dhabaan Anees H , McDonald Mark , Beitler Jonathan J , Curran Walter J , Zhou Jun , Liu Tian , Yang Xiaofeng 
Published: January 2021

Abstract
Organ-at-risk (OAR) delineation is a key step for cone-beam CT (CBCT) based adaptive radiotherapy planning that can be a time-consuming, labor-intensive, and subject-to-variability process. We aim to develop a fully automated approach aided by synthetic MRI for rapid and accurate CBCT multi-organ contouring in head-and-neck (HN) cancer patients. MRI has superb soft-tissue contrasts, while CBCT offers bony-structure contrasts. Using the complementary information provided by MRI and CBCT is expected to enable accurate multi-organ segmentation in HN cancer patients. In our proposed method, MR images are firstly synthesized using a pre-trained cycle-consistent generative adversarial network given CBCT. The features of CBCT and synthetic MRI (sMRI) are then extracted using dual pyramid networks for final delineation of organs. CBCT images and their corresponding manual contours were used as pairs to train and test the proposed model. Quantitative metrics including Dice similarity coefficient (DSC), Hausdorff distance 95% (HD95), mean surface distance, and residual mean square distance (RMS) were used to evaluate the proposed method. The proposed method was evaluated on a cohort of 65 HN cancer patients. CBCT images were collected from those patients who received proton therapy. Overall, DSC values of 0.87 ± 0.03, 0.79 ± 0.10/0.79 ± 0.11, 0.89 ± 0.08/0.89 ± 0.07, 0.90 ± 0.08, 0.75 ± 0.06/0.77 ± 0.06, 0.86 ± 0.13, 0.66 ± 0.14, 0.78 ± 0.05/0.77 ± 0.04, 0.96 ± 0.04, 0.89 ± 0.04/0.89 ± 0.04, 0.83 ± 0.02, and 0.84 ± 0.07 for commonly used OARs for treatment planning including brain stem, left/right cochlea, left/right eye, larynx, left/right lens, mandible, optic chiasm, left/right optic nerve, oral cavity, left/right parotid, pharynx, and spinal cord, respectively, were achieved. This study provides a rapid and accurate OAR auto-delineation approach, which can be used for adaptive radiation therapy.
Gold nanoparticle detection and quantification in therapeutic MV beams via pair production
Authors: Brivio D , Sajo E , Zygmanski P 
Published: January 2021

Abstract

Purpose. We propose a new detection method of gold nanoparticles (AuNP) in therapeutic megavoltage (MV) x-ray beams by means of coincidence counting of annihilation photons following pair production in gold. Methods. The proposed MV x-ray induced positron emission (MVIPE) imaging technique is studied by radiation transport computations using MCNP6 (3D) and CEPXS/ONEDANT (1D) codes for two water phantoms: a 35 cm slab and a similarly sized cylinder, both having a 5 cm AuNP filled region in the center. MVIPE is compared to the standard x-ray fluorescence computed tomography (XFCT). MVIPE adopts MV x-ray sources (Co-60, 2 MV, 6 MV, 6 MV with closed MLC and 15 MV) and relies on the detection of 511 keV photon-pairs. XFCT uses kilovoltage sources (100 kVp, 120 kVp and 150 kVp) and imaging is characterized by analysis of k
α1,2 Au characteristic lines. Three levels of AuNP concentration were studied: 0.1%, 1% and 10% by weight. Results. Annihilation photons in the MVIPE technique originate both in the AuNP and in water along the x-ray beam path with significantly larger production in the AuNP-loaded region. MVIPE signal from AuNP is linearly increasing with AuNP concentration up to 10%wt, while XFCT signal reaches saturation due to self-absorption within AuNP. The production of annihilation photons is proportional to the MV source energy. MVIPE technique using a 15 MV pencil beam and 10 wt% AuNP detects about 4.5 × 103 511 keV-photons cm−2 at 90° w/r to the incident beam per 109 source photons cm−2; 500 of these come from AuNP. In contrast, the XFCT technique using 150 kVp detects only about 100 k

α1-photons cm−2 per 109 source photons cm−2. Conclusions. In MVIPE, the number of annihilation photons produced for different MV-beam energies and AuNP concentrations is significantly greater than the k

α1 photons generated in XFCT. Coincidence counting in MVIPE allows to avoid collimation, which is a major limiting factor in XFCT. MVIPE challenges include the filtering of Compton scatter and annihilation photons originating in water.
Stereoscopic portable hybrid gamma imaging for source depth estimation
Authors: Bugby S L , Lees J E , McKnight W K , Dawood N S 
Published: January 2021

Abstract
Advances in gamma imaging technology mean that is now technologically feasible to conduct stereoscopic gamma imaging in a hand-held unit. This paper derives an analytical model for stereoscopic pinhole imaging which can be used to predict performance for a wide range of camera configurations. Investigation of this concept through Monte Carlo and benchtop studies, for an example configuration, shows camera-source distance measurements with a mean deviation between calculated and actual distances of <5 mm for imaging distances of 50–250 mm. By combining this technique with stereoscopic optical imaging, we are then able to calculate the depth of a radioisotope source beneath a surface without any external positional tracking. This new hybrid technique has the potential to improve surgical localisation in procedures such as sentinel lymph node biopsy.
GroupRegNet: a groupwise one-shot deep learning-based 4D image registration method
Authors: Zhang Yunlu , Wu Xue , Gach H Michael , Li Harold , Yang Deshan 
Published: January 2021

Abstract
Accurate deformable four-dimensional (4D) (three-dimensional in space and time) medical images registration is essential in a variety of medical applications. Deep learning-based methods have recently gained popularity in this area for the significantly lower inference time. However, they suffer from drawbacks of non-optimal accuracy and the requirement of a large amount of training data. A new method named GroupRegNet is proposed to address both limitations. The deformation fields to warp all images in the group into a common template is obtained through one-shot learning. The use of the implicit template reduces bias and accumulated error associated with the specified reference image. The one-shot learning strategy is similar to the conventional iterative optimization method but the motion model and parameters are replaced with a convolutional neural network and the weights of the network. GroupRegNet also features a simpler network design and a more straightforward registration process, which eliminates the need to break up the input image into patches. The proposed method was quantitatively evaluated on two public respiratory-binned 4D-computed tomography datasets. The results suggest that GroupRegNet outperforms the latest published deep learning-based methods and is comparable to the top conventional method pTVreg. To facilitate future research, the source code is available at https://github.com/vincentme/GroupRegNet.
Waveform characterisation and comparison of nystagmus eye-tracking signals
Authors: Rosengren William , Nyström Marcus , Hammar Björn , Stridh Martin 
Published: January 2021

Abstract

Objective. Pathological nystagmus is a symptom of oculomotor disease where the eyes oscillate involuntarily. The underlying cause of the nystagmus and the characteristics of the oscillatory eye movements are patient specific. An important part of clinical assessment in nystagmus patients is therefore to characterise different recorded eye-tracking signals, i.e. waveforms.
Approach. A method for characterisation of the nystagmus waveform morphology is proposed. The method extracts local morphologic characteristics based on a sinusoidal model, and clusters these into a description of the complete signal. The clusters are used to characterise and compare recordings within and between patients and tasks. New metrics are proposed that can measure waveform similarity at different scales; from short signal segments up to entire signals, both within and between patients. Main results. The results show that the proposed method robustly can find the most prominent nystagmus waveforms in a recording. The method accurately identifies different eye movement patterns within and between patients and across different tasks. Significance. In conclusion, by allowing characterisation and comparison of nystagmus waveform patterns, the proposed method opens up for investigation and identification of the underlying condition in the individual patient, and for quantifying eye movements during tasks.
Correlation-estimated conditional average method and its application on solitary oscillation in PANTA
Authors: Kobayashi T-K , Fujisawa A , Nagashima Y , Moon C , Yamasaki K , Nishimura D , Inagaki S , Yamada T , Kasuya N , Kosuga Y , Sasaki M , Kawachi Y 
Published: January 2021

Abstract
The conditional average is a technique to extract a typical waveform from bursty or quasi-periodic phenomena regarded as a combination of deterministic trend and residual fluctuations (or probabilistic part). The article proposes a new conditional average technique that is an extension from what is called the template method that allows automatic selection, using the correlation analysis, of the identical events of the phenomena. The proposed method is applied on a quasi-periodic oscillation observed in a linear magnetized plasma, and successfully divides the phenomenon into the deterministic trend and residual fluctuations. Moreover, the statistical error analysis on the power of the residual fluctuations discloses the presence of mutual interactions between the deterministic trend and the residual fluctuations.
A wind-induced negative damping method to achieve high-energy orbit of a nonlinear vibration energy harvester
Authors: Lan Chunbo , Hu Guobiao , Liao Yabin , Qin Weiyang 
Published: January 2021

Abstract
Maintaining high-energy orbit oscillation of a nonlinear vibration energy harvester (VEH) is the key to achieve high-performance, broadband energy harvesting. Conventional orbit-jump strategies, such as mechanical modulation or electrical control methods, need to consume the limited harvested energy and may unfavourably reduce the energy harvesting efficiency. To avoid the undesired energy consumption, we focus on utilizing the overlooked wind energy to assist a nonlinear VEH to attain the preferred high-energy orbit. The novel orbit-jump method proposed in this letter is based on the wind-induced negative damping mechanism and the resultant self-excited behaviour. Both numerical simulation and experimental results validate the feasibility of the proposed method to efficiently trigger the high-energy orbit oscillations of a nonlinear VEH. Moreover, the required wind energy to achieve self-excited oscillation for different excitation frequencies and acceleration levels, is quite stable and can be easily satisfied, which demonstrates good robustness for practical applications.
Size-dependent free vibration analysis of functionally graded porous piezoelectric sandwich nanobeam reinforced with graphene platelets with consideration of flexoelectric effect
Authors: Chen Qingqing , Zheng Shijie , Li Zongjun , Zeng Chunying 
Published: January 2021

Abstract
This paper investigates the effect of flexoelectricity on the vibration responses of functionally graded porous piezoelectric sandwich nanobeam reinforced by graphene platelets (GPLs). The Euler–Bernoulli beam theory and the general modified strain gradient theory are employed to formulate the constitutive equations. Different distributions of porosity and GPLs dispersion patterns are considered and the Halpin–Tsai model is used to predict Young’s modulus and density of the nanobeam. The governing equations and boundary conditions are derived based on the general strain gradient theory and solved by differential quadrature method. A parametric study is accomplished to investigate the effects of flexoelectricity, size-dependence, porosity coefficient, GPLs weight fraction, porosity distributions as well as GPLs dispersion patterns on the fundamental frequency of composite nanobeam. Numerical results indicate that the porosity, GPLs and flexoelectricity can effectively influence the vibration behavior of nanobeam.
Piezoelectric-silicone structure for vibration energy harvesting: experimental testing and modelling
Authors: Zabek Daniel , Pullins Rhys , Pearson Matthew , Grzebielec Andrzej , Skoczkowski Tadeusz 
Published: January 2021

Abstract
Mechanical vibrations from heavy machines, building structures, or the human body can be harvested and directly converted into electrical energy. In this paper, the potential to effectively harvest mechanical vibrations and locally generate electrical energy using a novel piezoelectric-rubber composite structure is explored. Piezoelectric lead zirconate titanate is bonded to silicone rubber to form a cylindrical composite-like energy harvesting device which has the potential to structurally dampen high acceleration forces and generate electrical power. The device was experimentally load tested and an advanced dynamic model was verified against experimental data. While an experimental output power of 57 μW cm−3 was obtained, the advanced model further optimises the device geometry. The proposed energy harvesting device generates sufficient electrical power for structural health monitoring and remote sensing applications, while also providing structural damping for low frequency mechanical vibrations.
Control of photorefractive space-charge field and its deflection application in Mn-doped KTa1−x
Nb
x
O3 crystal
Authors: Wang Yu , Tan Peng , Zhou Zhongxiang , Huang Xiaolin , Hu Chengpeng , Meng Xiangda , Huang Fei , Wang Jing , Tian Hao 
Published: January 2021

Abstract
The photorefractive effect is an effective way of achieving control of a beam by inducing a space-charge field and controlling the distribution of the refractive index. Herein, the one-dimensional gradient distribution of a space-charge field is structured, and the deflection of the transmitted beam is achieved. The method of regulation and control of the space-charge field was studied by digital holographic microscopy based on a Mn-doped KTN single crystal. The realized deflector has a maximum deflection angle of 0.3° and maintains good frequency-response characteristics up to 50 kHz. This work provides guidance for optical functional devices based on engineering distribution of a space-charge field.
Searching for signatures of quantum gravity in quantum gases
Authors: Haine Simon A 
Published: January 2021

Abstract
We explore the possibility of testing the quantum nature of the gravitational field with an ensemble of ultra-cold atoms. The use of many microscopic particles may circumvent some of the experimental obstacles encountered in recent proposals involving a pair of particles with mesoscopic mass. We employ multi-parameter estimation techniques, including the quantum and classical Fisher information to provide a criteria for the observability of the quantum effects, and compare to other recently proposed schemes. Crucially, we find that by preparing the appropriate initial state, interactions mediated via a quantum-valued gravitational field provide a signature that is distinct from classical gravitational interactions. We find that a definitive demonstration of the quantum gravitational interaction is not possible with current experimental capabilities, due to uncertainty in the residual electromagnetic interactions between the atoms. However, it may be possible with significant improvements to current experimental techniques.
Quantum dynamics of electric-dipole coupled defect centers in solids
Authors: Tang Feng , Zhou Lei-Ming , Zhao Nan 
Published: January 2021

Abstract
We investigate the quantum dynamics of two defect centers in solids, which are coupled by vacuum-induced dipole–dipole interactions. When the interaction between defects and phonons is taken into account, the two coupled electron–phonon systems make up two equivalent multilevel atoms. By making Born–Markov and rotating wave approximations, we derive a master equation describing the dynamics of the coupled multilevel atoms. The results indicate the concepts of subradiant and superradiant states can be applied to these systems and the population transfer process presents different behaviors from those of the two dipolar-coupled two-level atoms due to the participation of phonons.
Sensitivity of energy eigenstates to perturbation in quantum integrable and chaotic systems
Authors: Xu Zao , Lyu Yin-Chenguang , Wang Jiaozi , Wang Wen-Ge 
Published: January 2021

Abstract
We study the sensitivity of energy eigenstates to small perturbation in quantum integrable and chaotic systems. It is shown that the distribution of rescaled components of perturbed states in unperturbed basis exhibits qualitative difference in these two types of systems: being close to the Gaussian form in quantum chaotic systems, while, far from the Gaussian form in integrable systems.
Average coherence with respect to complementary measurements
Authors: Chen Bin , Fei Shao-Ming 
Published: January 2021

Abstract
We investigate the average coherence with respect to a complete set of complementary measurements. By using a Wigner–Yanase skew information-based coherence measure introduced in Luo and Sun (2017 Phys. Rev. A 96, 022130), we evaluate the average coherence of a state with respect to any complete set of mutually unbiased measurements and general symmetric informationally complete measurements, respectively. We also establish analytically the relations among these average coherences.
Resolving neural states from optical neural response readout
Authors: Zheltikov Aleksei M 
Published: January 2021

Abstract
We examine two types of information loss function encountered in optical neural recording—the uncertainty of neural states encoding an external stimulus and the incompleteness of information that laser-excited fluorescence can read out from these neural states. We show that, even though these uncertainties are of distinctly different nature, they can be treated on an equal footing against the Fisher information metric, revealing the fundamental information limits inherent in optical neural recording.
Analyzing protein-protein interactions of coronavirus using markov clustering with cuckoo search and ant lion optimization
Authors: Afriyani R , Bustamam A , Sarwinda D 
Published: January 2021

Abstract
Proteins are complex organic compounds made up of smaller units called amino acids that are bonded together in long chains. Protein interacts with other proteins or molecules and becomes essential in the structure, function, and regulation of organisms’ cells. The Protein-Protein Interaction (PPI) results in a considerably large network. Consequently, there is a need to find a method to simplify the network for easy interpretation of the protein-protein interaction. One of the most common methods is Markov Clustering (MCL). MCL has been applied to solve graph clustering problems based on stochastic flow simulation. MCL has three main stages in the process, namely expansion, inflation, and pruning. Although MCL produces a fast and well-balanced non-hierarchical clustering, it has a limitation where the results depend on the inflation parameter being inputted manually. In this study, we develop a method to combine Markov Clustering (MCL) with Cuckoo Search (CS) and Ant Lion Optimization (ALO) Algorithm. CS and ALO are applied in MCL algorithm to obtain an optimized inflation parameter automatically. PPI network of SARS-CoV-2 and other related coronavirus datasets are used in this research and is presented in the form of a graph. The experiment shows that CS-MCL forms 47 clusters, while ALO-MCL yields 14 cluster on the PPI dataset.
Strengthened ocean-desert process in the North Pacific over the past two decades
Authors: Meng Siyu , Gong Xun , Yu Yang , Yao Xiaohong , Gong Xiang , Lu Keyu , Zhang Chao , Shi Jie , Yu Xiaojie , Gao Huiwang 
Published: January 2021

Abstract
North Pacific ocean desert (NPOD) refers to the subtropical North Pacific Ocean of low chlorophyll-a (Chl-a) concentrations, as the largest ocean desert globally. Studies have suggested a development of NPOD over recent decades based on limited evidences from in-field measurements and yet elusive mechanism. In this study, we characterize intensity, area and position of the NPOD from year 1998 to 2018, and investigate its control by the coherent climate processes, based on an available, longest satellite observations of Chl-a concentration. Our results suggested that NPOD oligotrophication and expansion processes were correlated with warming upper oceans in most part of the NPOD, except for the SW NPOD area where the Chl-a variations were linked with regional change in sea surface heights. Moreover, based on our analysis, insignificant shift but only NW-SE variability of the NPOD mean position was likely controlled by the Pacific decadal oscillation processes.