bluuebunny/crossref_metadata_2025_split huggingface dataset:
A crossbar architecture based system (CAS) as hydrogen gas sensing platform
Authors: Jana Abir , Kanrar Sharmistha Shee , De Arpan , Kumari Komal , Gupta Bhaskar , Sarkar Subir Kumar 
Published: August 2023

Abstract
The development of sensing technologies and miniaturization allows for the development of smart systems with elevated sensing performance. Silicon-based hydrogen sensors have received a lot of attention due to its electrical conductivity and the mechanical endurance. With this motivation, we have proposed a two-terminal silicon-based device in a crossbar architecture as a hydrogen gas sensing platform. In this work, we have adopted a multi-layer modeling approach to analyze the performance of the proposed system. Technology computer-aided design models have been used to capture device performance. A gas sensor model based on hydrogen adsorption on the Palladium surface and a crossbar model has been adopted to understand the Palladium work function variation with gas pressure and the performance of the proposed crossbar system respectively. We have shown the impact of parameters like interconnect resistance and array size on the whole system’s performance. Finally, a comprehensive analysis has been provided for the design rule of this architecture. A fabrication process to spur future experimental works has also been added. This work will provide computational insight into the performance of a crossbar hydrogen sensor system, optimized against some critical parameters.
High sensitivity and wide range flexible piezoresistive sensor based on petal-shaped MOF-derived NiCo-NPC
Authors: Zeng Ziran , Li Yunfan , Zhao Yilin , Yuan Jun , Yi Longju , Li Peilong , Cheng Gary J , Liu Feng 
Published: August 2023

Abstract
Due to the advantages of high porosity, excellent conductivity, and tunable morphology, carbonized metal-organic framework (C-MOF) is expected to become an ideal material for constructing high-performance flexible pressure sensor. Herein, to achieving the suitable morphology of C-MOF for piezoresistive sensors, a rapid thermal process (RTP) was used for carbonization of NiCo-MOF, and the petal-shaped NiCo alloy nanoparticles/nanoporous carbon composites (NiCo-NPCs) were obtained. Compared with NiCo-NPCs carbonized by common thermal process (CTP), NiCo-NPCs carbonized by RTP exhibit a modified morphology with smaller particle size and larger most frequent pore diameter. Due to the modified morphology, the piezoresistive sensor with RTP-carbonized NiCo-NPCs has a high sensitivity of 62.13 kPa–1 at 0–3 kPa, which is 3.46 times higher than that of the sensor with CTP-carbonized NiCo-NPCs. Meanwhile, the sensor shows an ultra-wide range of 1000 kPa, excellent cycle stability (>4000 cycles), and fast response/recovery time of 25/44 ms. Furthermore, the application of the sensor in dynamic loading test, airflow monitoring, voice recognition, and gesture detection demonstrates its great application prospects. In short, this work investigates the application of carbonized NiCo-MOFs in flexible pressure sensors, and provides a new strategy to improve the performance of piezoresistive sensors with porous carbon derived from MOFs.
MRI magnitude signal-based proton beam visualisation in water phantoms reflects composite effects of beam-induced buoyant convection and radiation chemistry
Authors: Schieferecke Juliane , Gantz Sebastian , Karsch Leonhard , Pawelke Jörg , Hoffmann Aswin 
Published: August 2023

Abstract

Objective. Local magnetic resonance (MR) signal loss was previously observed during proton beam irradiation of free-floating water phantoms at ambient temperature using a research prototype in-beam magnetic resonance imaging (MRI) scanner. The emergence of this MR signal loss was hypothesised to be dependent on beam-induced convection. The aim of this study was therefore to unravel whether physical conditions allowing the development of convection must prevail for the beam-induced MRI signatures to emerge. Approach. The convection dependence of MRI magnitude signal-based proton beam visualisation was investigated in combined irradiation and imaging experiments using a gradient echo (GE)-based time-of-flight (ToF) angiography pulse sequence, which was first tested for its suitability for proton beam visualisation in free-floating water phantoms at ambient temperature. Subsequently, buoyant convection was selectively suppressed in water phantoms using either mechanical barriers or temperature control of water expansivity. The underlying contrast mechanism was further assessed using sagittal imaging and variation of T1 relaxation time-weighting. Main results. In the absence of convection-driven water flow, weak beam-induced MR signal changes occurred, whereas strong changes did occur when convection was not mechanically or thermally inhibited. Moreover, the degree of signal loss was found to change with the variation of T1-weighting. Consequently, beam-induced MR signal loss in free-floating water phantoms at ambient temperature does not exclusively originate from buoyant convection, but is caused by local composite effects of beam-induced motion and radiation chemistry resulting in a local change in the water T1 relaxation time. Significance. The identification of ToF angiography sequence-based proton beam visualisation in water phantoms to result from composite effects of beam-induced motion and radiation chemistry represents the starting point for the future elucidation of the currently unexplained motion-based MRI contrast mechanism and the identification of the proton beam-induced material change causing T1 relaxation time lengthening.
Thermal neutron detection and track recognition method in reference and out-of-field radiotherapy FLASH electron fields using Timepix3 detectors
Authors: Oancea Cristina , Solc Jaroslav , Bourgouin Alexandra , Granja Carlos , Jakubek Jan , Pivec Jiri , Riemer Felix , Vykydal Zdenek , Worm Steven , Marek Lukas 
Published: August 2023

Abstract

Objective. This work presents a method for enhanced detection, imaging, and measurement of the thermal neutron flux. Approach. Measurements were performed in a water tank, while the detector is positioned out-of-field of a 20 MeV ultra-high pulse dose rate electron beam. A semiconductor pixel detector Timepix3 with a silicon sensor partially covered by a 6LiF neutron converter was used to measure the flux, spatial, and time characteristics of the neutron field. To provide absolute measurements of thermal neutron flux, the detection efficiency calibration of the detectors was performed in a reference thermal neutron field. Neutron signals are recognized and discriminated against other particles such as gamma rays and x-rays. This is achieved by the resolving power of the pixel detector using machine learning algorithms and high-resolution pattern recognition analysis of the high-energy tracks created by thermal neutron interactions in the converter. Main results. The resulting thermal neutrons equivalent dose was obtained using conversion factor (2.13(10) pSv·cm2) from thermal neutron fluence to thermal neutron equivalent dose obtained by Monte Carlo simulations. The calibrated detectors were used to characterize scattered radiation created by electron beams. The results at 12.0 cm depth in the beam axis inside of the water for a delivered dose per pulse of 1.85 Gy (pulse length of 2.4 μs) at the reference depth, showed a contribution of flux of 4.07(8) × 103 particles·cm−2·s−1 and equivalent dose of 1.73(3) nSv per pulse, which is lower by ∼9 orders of magnitude than the delivered dose. Significance. The presented methodology for in-water measurements and identification of characteristic thermal neutrons tracks serves for the selective quantification of equivalent dose made by thermal neutrons in out-of-field particle therapy.
In vivo dosimetry in cancer patients undergoing intraoperative radiation therapy
Authors: Petoukhova Anna , Snijder Roland , Vissers Thomas , Ceha Heleen , Struikmans Henk 
Published: August 2023

Abstract

In vivo dosimetry (IVD) is an important tool in external beam radiotherapy (EBRT) to detect major errors by assessing differences between expected and delivered dose and to record the received dose by individual patients. Also, in intraoperative radiation therapy (IORT), IVD is highly relevant to register the delivered dose. This is especially relevant in low-risk breast cancer patients since a high dose of IORT is delivered in a single fraction. In contrast to EBRT, online treatment planning based on intraoperative imaging is only under development for IORT. Up to date, two commercial treatment planning systems proposed intraoperative ultrasound or in-room cone-beam CT for real-time IORT planning. This makes IVD even more important because of the possibility for real-time treatment adaptation. Here, we summarize recent developments and applications of IVD methods for IORT in clinical practice, highlighting important contributions and identifying specific challenges such as a treatment planning system for IORT. HDR brachytherapy as a delivery technique was not considered. We add IVD for ultrahigh dose rate (FLASH) radiotherapy that promises to improve the treatment efficacy, when compared to conventional radiotherapy by limiting the rate of toxicity while maintaining similar tumour control probabilities. To date, FLASH IORT is not yet in clinical use.
Joint two-stage convolutional neural networks for intracranial aneurysms detection on 3D TOF-MRA
Authors: Zhou Yuxi , Yang Yifeng , Fang Ting , Jia Shouqiang , Nie Shengdong , Ye Xiaodan 
Published: August 2023

Abstract

Objective. This study aims to develop a three-dimensional convolutional neural network utilizing computer-aided diagnostic technology to facilitate the detection of intracranial aneurysms and automatically assess their location and extent, thereby enhancing the efficiency of radiologists, and streamlining clinical workflows. Approach. A retrospective study was conducted, proposing a joint segmentation and classification network (JSCD-Net) that employs 3D time-of-flight magnetic resonance angiography images for preliminary detection of aneurysms and the minimization of false positives. Specifically, the U-Net++ network was utilized for pre-detection of aneurysms. This was followed by the creation of a multi-path network, co-trained with U-Net++ to correct the results of the first stage to further reduce the rate of false positives. Model effectiveness and robustness were evaluated using sensitivity and false positive analyses on internal and external datasets. A cross-validated free-response receiver operating characteristic curve was also plotted. Main results. JSCD-Net demonstrated a sensitivity of 91.2% (31 of 34; 95% CI: 77.0, 97.0) with an average of 3.55 false positives per scan on the internal test set. For the external test set, it identified 97.2% (70 of 72; 95% CI: 90.4, 99.2) of aneurysms with an average of 2.7 false positives per scan. Significance. When compared with the existing studies, the proposed model shows high sensitivity in detecting intracranial aneurysms with a reasonable number of false positives per case. This result emphasizes the model’s potential as a valuable tool in aiding clinical diagnoses.
Bio-potential noise of dry printed electrodes: physiology versus the skin-electrode impedance
Authors: Arché-Núñez Ana , Krebsbach Peter , Levit Bara , Possti Daniel , Gerston Aaron , Knoll Thorsten , Velten Thomas , Bar-Haim Chen , Oz Shani , Klorfeld-Auslender Shira , Hernandez-Sosa Gerardo , Mirelman Anat , Hanein Yael 
Published: August 2023

Abstract

Objective. To explore noise characteristics and the effect physiological activity has on the link between impedance and noise. Approach. Dry-printed electrodes are emerging as a new and exciting technology for skin electro-physiology. Such electrode arrays offer many advantages including user convenience, quick placement, and high resolution. Here we analyze extensive electro-physiological data recorded from the arm and the face to study and quantify the noise of dry electrodes, and to characterize the link between noise and impedance. In particular, we studied the effect of the physiological state of the subject (e.g. rapid eye movement sleep) on noise. Main results. We show that baseline noise values extracted from dry electrodes in the arm are in agreement with the Nyquist equation. In the face, on the other hand, the measured noise values were higher than the values predicted by the Nyquist equation. In addition, we studied how different electrode properties affect performances, including electrode size, shape, and material properties. Significance. Altogether, the results presented here provide a basis for understanding dry electrode performances and substantiate their great potential in electro-physiological investigations.
Collective synchronization of dissipatively-coupled noise-activated processes
Authors: Chatzittofi M , Golestanian R , Agudo-Canalejo J 
Published: August 2023

Abstract
We study the stochastic dynamics of an arbitrary number of noise-activated cyclic processes, or oscillators, that are all coupled to each other via a dissipative coupling. The N coupled oscillators are described by N phase coordinates driven in a tilted washboard potential. At low N and strong coupling, we find synchronization as well as an enhancement in the average speed of the oscillators. In the large N regime, we show that the collective dynamics can be described through a mean-field theory, which predicts a great enhancement in the average speed. In fact, beyond a critical value of the coupling strength, noise activation becomes irrelevant and the dynamics switch to an effectively deterministic ‘running’ mode. Finally, we study the stochastic thermodynamics of the coupled oscillators, in particular their performance with regards to the thermodynamic uncertainty relation.
Semi-empirical Haken–Strobl model for molecular spin qubits
Authors: Aruachan Katy , Colón Yamil J , Aravena Daniel , Herrera Felipe 
Published: August 2023

Abstract
Understanding the physical processes that determine the relaxation T
1 and dephasing T
2 times of molecular spin qubits is critical for envisioned applications in quantum metrology and information processing. Recent spin-echo measurements of solid-state molecular spin qubits have stimulated the development of quantum mechanical models for predicting intrinsic qubit timescales using first-principles electronic structure methods. We develop an alternative semi-empirical approach to construct Redfield quantum master equations for molecular spin qubits using a stochastic Haken–Strobl theory for a central spin with fluctuating gyromagnetic tensor due to spin-lattice interaction and fluctuating local magnetic field due to interactions with lattice spins. Using two vanadium-based spin qubits as case studies, we compute qubit population and decoherence times as a function of temperature and magnetic field, using a bath spectral density parametrized with a small number of T
1 measurements. The theory quantitatively agrees with experimental data over a range of conditions beyond those used to parameterize the model, demonstrating the generalization potential of the method. The ability of the model to describe the temperature dependence of the ratio

T
2

/

T
1

is discussed and possible applications for designing novel molecule-based quantum magnetometers are suggested.
Quantum canonical correlation analysis algorithm
Authors: Song Chuan-Dong , Li Jian , Hou Yan-Yan , Liu Qing-Hui , Wang Zhuo 
Published: August 2023

Abstract
Canonical correlation analysis (CCA) is a fundamental technique used to analyze data correlation in various fields, including video and medical data analysis. In this paper, we propose a quantum canonical correlation analysis (QCCA) algorithm. First, we introduce a combined density matrix representation method that transforms CCA into generalized eigenvalue decomposition. Moreover, to address the challenge of performing generalized eigenvalue decomposition in high-dimensional scenarios, we propose a quantum method for extracting the canonical principal axes. In this method, two sets of variables are transformed into a reduced density matrix, so that the product of variable matrices can be accelerated by phase estimation and controlled rotation. Complexity analysis shows that the QCCA algorithm achieves exponential acceleration in variable dimensions n, p and variable size m compared to classical algorithms. The QCCA algorithm serves as a foundation for the subsequent development of quantum algorithms for classification, regression, and other machine learning tasks.
Percolation in random sequential adsorption of polydisperse mixtures of extended objects on a triangular lattice
Authors: Dujak D , Karač A , Jakšić Z M , Vrhovac S B , Budinski-Petković Lj 
Published: August 2023

Abstract
Percolation properties of an adsorbed polydisperse mixture of extended objects on a triangular lattice are studied by Monte Carlo simulations. The depositing objects of various shapes are formed by self-avoiding walks on the lattice. We study polydisperse mixtures in which the size

ℓ

of the shape making the mixture increases gradually with the number of components. This study examines the influence of the shape of the primary object defining a polydisperse mixture on its percolation and jamming properties. The dependence of the jamming density and percolation threshold on the number of components n making the mixture is analyzed. Determining the contribution of the individual components in the lattice covering allowed a better insight into the deposit structure of the n-component mixture at the percolation threshold. In addition, we studied mixtures of objects of various shapes but the same size.
Coexisting and cooperating light–matter interaction regimes in a polaritonic photovoltaic system
Authors: Caligiuri V , Siprova S , Patra A , Lio G E , Termine R , Cilurzo S , Golemme A , Luca A De 
Published: August 2023

Abstract
Common quantum frameworks of light–matter coupling demonstrate the interaction between an atom and a cavity occurring through a single feedback channel: an exciton relaxes by emitting a photon that is stored in the cavity for several roundtrips before being re-absorbed to create another exciton, and so on. However, the possibility for the excited system to relax through two different channels belonging to two different regimes has been, until now, neglected. Here, we investigate the case in which the strong coupling regime and the photovoltaic effect cooperate to enhance the wavelength-dependent photocurrent conversion efficiency (defined as the incident photons to converted electrons ratio, namely the external quantum efficiency—EQE) of a photovoltaic cell specifically engineered to behave as an optical cavity tuned to the excitonic transition of the embedded active material (CH3NH3PbI3 perovskite). We exploit the angular dispersion of such photovoltaic cell to show that when the cavity mode approaches the energy of the exciton, the strong coupling regime is achieved and the EQE is significantly enhanced with respect to a classic configuration serving as a benchmark. Our findings do not aim at demonstrating an immediate impact in enhancing the performance of photovoltaic systems but, rather, constitute a proof-of-principle experimental demonstration of how the photovoltaic effect can benefit from the generation of polaritons. Nonetheless, such a peculiar cooperating dual-light–matter interaction could be exploited in future polaritonic photovoltaic architectures.
Ultrasensitive photodetector based on 2D WS2/AgInGaS quantum dots heterojunction with interfacial charge transfer
Authors: Wang Xusheng , Yan Danni , Zhu Cheng , Feng Yiping , Guo Tingting , Jia Runmeng , Qu Kairui , Li Linyun , Zhao Tong , Xiong Yunhai , Farhan Ahmad , Lin Yuhai , Wu Linxiang , Dong Yuhui , Zhang Shengli , Chen Xiang , Song Xiufeng 
Published: August 2023

Abstract
Tungsten disulfide (WS2), as a typical member of transition metal chalcogenides (TMDs), has attracted extensive research interest in optoelectronics, especially photodetectors. However, the performance of photodetectors based on monolayer WS2 is restricted to weak light absorption. Here, AgInGaS quantum dots (AIGS-QDs) with a large absorption coefficient and high quantum efficiency are integrated onto WS2 atomic layers to achieve excellent photoelectric performance. Notably, the observed photoluminescence (PL) quenching and the reduction of the decay time of PL in the WS2/AIGS-QDs heterojunction confirm the interfacial charge transfer from AIGS-QDs to WS2 layer. The results show that type II energy band arrangement leads to the efficient separation of photoexcited carriers at the interface between WS2 and AIGS-QDs. This WS2/AIGS-QDs photodetector achieves an ultrahigh responsivity (R) of 3.3 × 103 A W−1, an external quantum efficiency (EQE) of 7.8 × 106% and a detectivity (D*) of 1.3 × 1013 Jones. Our work provides promising potential for future high-performance monolayer TMD-based photodetectors.
The role of digital governance on carbon emission performance: evidence from the cities in yangtze river delta, China
Authors: Wang Yunhui , Zhang Xuan , Lin Feiyang , Peng Meijuan 
Published: August 2023

Abstract
Rapid industrialization and ambitious socio-economic targets have presented significant challenges to China’s carbon neutrality process. However, digital transformation offers new opportunities for sustainable development. This research examines the influence of digital governance (Digov) on carbon emission performance (Cep) and explores its underlying mechanisms. The study utilizes data from cities in China’s Yangtze River Delta from 2011 to 2019. The results show that digital governance has significantly improved carbon emission performance, a conclusion that remains robust even after conducting a series of rigorous tests and addressing endogeneity concerns. The main impact mechanisms of digital governance on carbon emission performance include energy intensity reduction, energy consumption scale reduction, industrial structure adjustment, and energy consumption structure optimization.Furthermore, the results indicate that the variation in carbon emission reduction due to digital governance can be attributed to differences in city administrative level, city size, and government capacity. In particular, digital governance plays a pivotal role in facilitating the sustainable transformation of resource-based cities.From the perspective of digital governance, this study provides effective recommendations and valuable insights for achieving low-carbon targets and promoting sustainable development of cities.