bluuebunny/crossref_metadata_2025_split huggingface dataset:
From rhetoric to regulation: inferring lobbying influence on EU efforts to regulate $$\text {CO}_2$$ emissions of cars using network analysis
Authors: Sick Harald 
Published: September 2023

AbstractThis study shows how the automotive industry was able to influence the final legislation of the Community Strategy to reduce$${CO}_{2}$$

CO

2

emissions from passenger cars and light-commercial vehicles, even though the European Commission’s introductory comments were supported by a competing interest group coalition: environmentalist NGOs. To operationalize and analyze the influence of lobbying on the EU policy formulation process, I utilize an approach not yet widely used in interest group research: discourse network analysis and inferential network analysis with exponential random graph models. This methodological combination allows me to parse the political debate within different phases of the EU policy process to gain insights into how automotive lobbyists were able to shape the final legislation in accordance with their interests and which political decision-makers empowered them to do so. Surprisingly, the party affiliation of Members of the European Parliament played only a subordinate role, whereas the origin of the MEPs was a significant predictor of whether they supported the proposals of environmentalist NGOs or the automotive industry. The latter’s proposals were adopted in the European Commission’s proposal and subsequently in the final regulation. Since the method can be easily applied to case studies from different policy areas, it provides a promising framework for the analysis of EU lobbying in general. Moreover, the network approach is easily extendable, allowing future research to integrate different strands of EU lobbying research that go beyond influence analysis.
Fabrication and characterization of an AlGaN light emitting diode with Al-doped ZnO as a current spreading tunnel junction layer
Authors: Ukita Shun , Tajiri Takeyoshi , Uchida Kazuo 
Published: September 2023

We fabricated an AlGaN light emitting diode (LED) with a heavily Al-doped n-type ZnO layer on a p-type contact layer as a tunnel junction (TJ) to improve carrier injection into the LED. We characterized its electrical and optical properties and compared them to those of an AlGaN LED without ZnO. From the I–V characteristic of the LED with ZnO, we observed a threshold voltage of circa 2 V, which could be due to Zener breakdown of the type II heterostructure of n-ZnO/p-GaN as a TJ. From the electroluminescence measurement, we observed a similar emission peak in both AlGaN LEDs at ultraviolet (UV) wavelengths, but a broad emission band around 365 nm in the LED with ZnO. This emission could be originating from ZnO photoexcited by the UV LED emission. The dependence of these peak intensities on input currents shows that there is a monotonic increase in the light emission intensity for the UV LED emission, but a saturation behavior after the threshold voltage for the emission from the ZnO. This saturation behavior is attributed to an overflow of photoexcited electron–hole pairs into p-GaN, strongly suggesting that n-ZnO/p-GaN works as a TJ. Electroluminescence data also show that the presence of the ZnO film facilitates current spreading, which enables device operation at large input currents. Therefore, ZnO can work as a current spreading TJ layer and improve the performance of the AlGaN LED.
Catalyst-free synthesis of ammonia using dc-driven atmospheric-pressure plasma in contact with water
Authors: Ramoy Mary , Shirai Naoki , Sasaki Koichi 
Published: September 2023

Abstract
Atmospheric-pressure plasma, generated using a dc power supply, in contact with water was investigated as a green, catalyst-free method for the ammonia synthesis. Stable nitrogen plasmas were generated inside bubbles which were obtained by inserting a dielectric tube with the gas flow into water. A higher production rate was obtained at a higher discharge current, a higher flow rate of nitrogen, and a lower conductivity of water. In addition, the production rate when the water worked as the cathode of the discharge was higher than that with the inverted polarity of the dc power supply. The maximum production rate of ∼0.98 µmol min−1 was realized at the optimized discharge condition, which is higher than the literature value obtained using a dc discharge in contact with water (Hawtof et al 2019 Sci. Adv.
5 eaat5778). We also discussed the possible reaction fields for the ammonia synthesis in the experimental condition.
ScGAN: a generative adversarial network to predict hypothetical superconductors
Authors: Kim Evan , Dordevic S V 
Published: September 2023

Abstract
Despite having been discovered more than three decades ago, high temperature superconductors (HTSs) lack both an explanation for their mechanisms and a systematic way to search for them. To aid this search, this project proposes ScGAN, a generative adversarial network (GAN) to efficiently predict new superconductors. ScGAN was trained on compounds in Open Quantum Materials Database and then transfer learned onto the SuperCon database or a subset of it. Once trained, the GAN was used to predict superconducting candidates, and approximately 70% of them were determined to be superconducting by a classification model–a 23-fold increase in discovery rate compared to manual search methods. Furthermore, more than 99% of predictions were novel materials, demonstrating that ScGAN was able to potentially predict completely new superconductors, including several promising HTS candidates. This project presents a novel, efficient way to search for new superconductors, which may be used in technological applications or provide insight into the unsolved problem of high temperature superconductivity.
RS-RCNN: an indoor window detection algorithm for autonomous spraying robot
Authors: Ji Xiaofei , Li Yazhou , Cao Jiangtao 
Published: September 2023

Abstract
The limitations of deep learning detection algorithms based on convolutional neural networks stem from the local characteristics inherent in convolutional operations. To overcome this issue, an RS-RCNN (ResNet_50 + Swin Transformer RCNN) object detection algorithm is proposed as an extension of Faster-RCNN. This algorithm incorporates the fusion of ResNet_50 and Swin Transformer networks as backbone networks for feature extraction. Moreover, it integrates the AAM_HRFPN (Attention Aggregation Module High resolution network) multi-feature fusion network and incorporates a linear attention mechanism. By employing the SIoU loss calculation method, the proposed RS-RCNN network enhances the representation of global context information and local semantic information, while improving fusion efficiency and detection accuracy. Compared to typical object detection networks, the proposed network achieves an AP value of 0.877, which is 7.4% points higher than the original network. The successful application of this method presents a novel solution for robot detection of non-spraying areas.
Advances in the design and use of carbon dots for analytical and biomedical applications
Authors: Adeola Adedapo O , Clermont-Paquette Adryanne , Piekny Alisa , Naccache Rafik 
Published: September 2023

Abstract
Carbon dots (CDs) have garnered significant interest for their potential use in multiple applications due to their size, fluorescent properties, high photostability, low toxicity and biocompatibility. CDs can be tailored for specific needs, as they can be synthesized with diverse precursors and techniques for functionalization. Since the applications of CDs are rapidly expanding, this review highlights recent developments in this burgeoning field. Specifically, we describe advances in CD synthesis tailored for applications that include pH and temperature sensing, biochemical analysis, and bioimaging. We also discuss various challenges and practical solutions that will drive CD-based research forward. Challenges include the lack of standardized synthesis and purification methods for CDs, the lack of clarity regarding their mechanism of action, and procedural flaws in their applications. In conclusion, we provide recommendations for collaboration among disciplines to bridge existing knowledge gaps and address the key challenges required for CDs to be fully commercialized.
Dual-function tunable metasurface for polarization-insensitive electromagnetic induction transparency and dual-band absorption
Authors: Qi Yunping , Zhou Zihao , Shi Qiang , Wen Yujiao , Wang Li , Zhao Shiyu , Zhang Shu , Wang Xiangxian 
Published: September 2023

Abstract
In this paper, we propose a dual-operating mode metasurface based on graphene and vanadium dioxide (VO2), which can switch operating modes by changing the temperature. At room temperature (25 °C), the metasurface can generates a polarization-insensitive electromagnetically induced transparency (EIT)-like effect that can be modulated by changing the Fermi energy level (E
F) of graphene (through adding external voltage). In addition, the theoretical results derived from the two-particle model are in good agreement with the simulation results based on the finite element method. At high temperature (68 °C), the metasurface mode of operation can be changed to a dual-band absorber, providing absorption of 78.6% and 99.9% at 1.13 THz and 2.16 THz, respectively. Both absorption peaks can be dynamically tuned by changing the E
F of graphene. The metasurface is also simultaneously polarization insensitive and has a wide incidence angle. The proposed metasurface can be used as a slow light device with a maximum group delay of 0.5 ps at room temperature and as a refractive index sensor with a maximum sensitivity of 0.5 THz/RIU at high temperature. The designed metasurface offers a new way for designing multifunctional terahertz devices, slow light devices, and refractive index sensors.
A weakly supervised NMF method to decipher molecular subtype-related dynamic patterns in breast DCE-MR images
Authors: Guan Jian , Fan Ming , Li Lihua 
Published: September 2023

Abstract

Objective. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is an important imaging modality for breast cancer diagnosis. Intratumoral heterogeneity causes a major challenge in the interpretation of breast DCE-MRI. Previous studies have introduced decomposition methods on DCE-MRI to reveal intratumoral heterogeneity by analyzing distinct dynamic patterns within each tumor. However, these methods estimated the dynamic patterns and their corresponding component coefficients in an unsupervised manner, without considering any clinically relevant information. Approach. To decipher molecular subtype-related dynamic patterns, we propose a weakly supervised nonnegative matrix factorization method (WSNMF), which is able to decompose the pixel kinetics of DCE-MRI with image-level subtype labels. The WSNMF is developed based on a discriminant nonnegative matrix factorization (NMF) to utilize coarse-grained subtype information, in which between- and within-class scatters are defined on the mean vector of component coefficients over all pixels in each tumor, rather than directly on the vector of component coefficients of each pixel. Main results. Experiments demonstrated that the dynamic patterns identified by WSNMF had superior performance in distinguishing between luminal A and the other subtype tumors. The classification performance was evaluated using the area under the receiver operating characteristic curve (AUC). WSNMF yielded better classification performance (AUC = 0.822) than other heterogeneity analysis methods, including two partitioning-based methods (KPC with AUC = 0.697 and TTP with AUC = 0.760) and two unsupervised decomposition-based methods (PCA with AUC = 0.774 and NMF with AUC = 0.797). Significance. Our method adds a valuable new perspective into DCE-MRI decomposition-based heterogeneity analysis by taking advantage of intrinsic tumor characteristics to improve the diagnosis of breast cancer.
Automatic ultrasound diagnosis of thyroid nodules: a combination of deep learning and KWAK TI-RADS
Authors: Zhang Jingqi , Wang Qingsong , Zhao Jingwen , Yu Hui , Wang Fei , Zhang Jie 
Published: September 2023

Abstract

Objective. There has been a considerable amount of computer-aided diagnosis (CAD) methods highlighted in the field of ultrasonic examination (USE) of thyroid nodules. However, few researches focused on the automatic risk classification, which was the basis for determining whether fine needle aspiration (FNA) was needed. The aim of this work was to implement automatic risk level assessment of thyroid nodules. Approach. Firstly, 1862 cases of thyroid nodules with the results of USE and FNA were collected as the dataset. Then, an improved U-Net++ model was utilized for segmenting thyroid nodules in ultrasound images automatically. Finally, the segmentation result was imported into a multi-task convolutional neural network (MT-CNN), the design of which was based on the clinical guideline called KWAK TI-RADS. Apart from the category of benign and malignant, the MT-CNN also exported the classification result of four malignant features, solid component (SC), hypoechogenicity or marked hypoechogenicity (HMH), microlobulated or irregular margin (MIM), microcalcification (MC), which were used for counting the risk level in KWAK TI-RADS. Main results. The performance of the improved U-Net++ was evaluated on our test set, including 302 cases. The Dice coefficient and intersection over union reached 0.899, 0.816, respectively. The classification accuracy rates of SC, HMH, MIM, MC, were 94.5%, 92.8%, 86.1%, 88.9%, while the false positive (FP) rate was 6.0%, 5.6%, 10.6%, 12.9% respectively. As for the category of benign and malignant, the precision and recall rates were 93.7% and 94.4%. Significance. The proposed CAD method showed favourable performance in the diagnosis of thyroid nodules. Compared with other methods, it could provide reports closer to clinical practice for doctors.
Full-field deformation reconstruction for large-scale cryogenic composite tanks with limited strain monitoring data
Authors: Li Tengteng , Liu Minjing , Li Jianle , Xu Hao , Wu Zhanjun 
Published: September 2023

Abstract
Online deformation monitoring, while of paramount importance in safety evaluation for aerospace composite tanks, is highly challenging due to the complex strain distributions in the composite tank and the strict restrictions of sensor placement. In this study, full-field deformation of large-scale cryogenic composite tanks were reconstructed under thermo-mechanical coupling conditions. In essence, the inner surface strains in the junction area of the head and cylindrical shell of the tank, defined as the ‘H-C portion’, was derived theoretically based on outer-surface strain measurement. The inverse finite element method (iFEM) was then applied using the measured and derived strains to reconstruct the full-field deformed shape of the tank. A systematic and efficient parametric discussion was conducted using an orthotropic model equivalent with composite laminated models with different lay-ups. The influences of various factors relevant to the material and geometries of the tank on the accuracy of deformation reconstruction were unveiled. Finally, a numerical experiment was carried out to reconstruct the full-field deformation of a large-scale aerospace composite tank with a specific lay-up, where limited strain data analogous to those sparely measured using distributed optical fiber sensors was used. It was found that the sensor placement strategy markedly affects the accuracy of deformation reconstruction.
Interaction-motif-based classification of self-organizing metabolic cycles
Authors: Ouazan-Reboul Vincent , Golestanian Ramin , Agudo-Canalejo Jaime 
Published: September 2023

Abstract
Particles that are catalytically-active and chemotactic can interact through the concentration fields upon which they act, which in turn may lead to wide-scale spatial self-organization. When these active particles interact through several fields, these interactions gain an additional structure, which can result in new forms of collective behavior. Here, we study a mixture of active species which catalyze the conversion of a substrate chemical into a product chemical, and chemotax in concentration gradients of both substrate and product. Such species develop non-reciprocal, specific interactions that we coarse-grain into attractive and repulsive, which can lead to a potentially complex interaction network. We consider the particular case of a metabolic cycle of three species, each of which interacts with itself and both other species in the cycle. We find that the stability of a cycle of species that only chemotax in gradients of their substrate is piloted by a set of two parameter-free conditions, which we use to classify the low number of corresponding interaction networks. In the more general case of substrate- and product-chemotactic species, we can derive a set of two high-dimensional stability conditions, which can be used to classify the stability of all the possible interaction networks based on the self- and pair-interaction motifs they contain. The classification scheme that we introduce can help guide future studies on the dynamics of complex interaction networks and explorations of the corresponding large parameter spaces in such metabolically active complex systems.
Dark-probe scanning near-field microscopy
Authors: Parsamyan Henrik , Yezekyan Torgom , Nerkararyan Khachatur , Bozhevolnyi Sergey I 
Published: September 2023

Abstract
Scanning near-field optical microscopy (SNOM) is a well-known powerful optical technique for visualization of surface nanostructures and fields far beyond the diffraction limit and thus indispensable in material- and nanoscience. While the SNOM resolution is theoretically unlimited, the SNOM performance is in practice constrained by the signal-to-background ratio, simply because of light scattering scaling down as the sixth power of a nanoparticle size and useful signals rapidly drowning in the background for very small objects. In modern instruments, this problem is usually ameliorated through advanced post-processing techniques. Here, we suggest using, instead or in parallel, a ‘dark’ SNOM probe designed to suppress the background light scattering, so that the scattering occurs only when the probe is very close to a nanoscopic object. We argue and demonstrate with simulations that the dark-probe SNOM imaging is much more sensitive to the presence of tiny nanoparticles or any other nanoscale features, allowing thereby for superior resolution and sensing capabilities that are invaluable for nano-optical characterization.
∂¯ -dressing method for the complex modified KdV equation
Authors: Yang Shuxin , Li Biao 
Published: September 2023

AbstractThe dressing method based on the 2 × 2 matrix∂¯-problem is generalized to study the complex modified KdV equation (cmKdV). Through two linear constraint equations, the spatial and time spectral problems related to the cmKdV equation are derived. The gauge equivalence between the cmKdV equation and the Heisenberg chain equation is obtained. Using a recursive operator, a hierarchy of cmKdV with source is proposed. On the basis of the∂¯-equation, theN-solition solutions of the cmKdV equation are obtained by selecting the appropriate spectral transformation matrix. Furthermore, we get explicit one-soliton and two-soliton solutions.
Data-driven fusion and fission solutions in the Hirota–Satsuma–Ito equation via the physics-informed neural networks method
Authors: Sun Jianlong , Xing Kaijie , An Hongli 
Published: September 2023

Abstract
Fusion and fission are two important phenomena that have been experimentally observed in many real physical models. In this paper, we investigate the two phenomena in the (2+1)-dimensional Hirota–Satsuma–Ito equation via the physics-informed neural networks (PINN) method. By choosing suitable physically constrained initial boundary conditions, the data-driven fusion and fission solutions are obtained for the first time. Dynamical behaviors and error analysis of these solutions are investigated via illustratively numerical figures, which show that good results are achieved. It is pointed out that the PINN method adopted here can be effectively used to construct the data-driven fusion and fission solutions for other nonlinear integrable equations. Based on the powerful predictive capability of the PINN method and wide applications of fusion and fission in many physical areas, it is hoped that the data-driven solutions obtained here will be helpful for experts to predict or explain related physical phenomena.
Advances in mobility enhancement of ITZO thin-film transistors: a review
Authors: Chen Feilian , Zhang Meng , Wan Yunhao , Xu Xindi , Wong Man , Kwok Hoi-Sing 
Published: September 2023

Abstract
Indium-tin-zinc oxide (ITZO) thin-film transistor (TFT) technology holds promise for achieving high mobility and offers significant opportunities for commercialization. This paper provides a review of progress made in improving the mobility of ITZO TFTs. This paper begins by describing the development and current status of metal-oxide TFTs, and then goes on to explain the advantages of selecting ITZO as the TFT channel layer. The evaluation criteria for TFTs are subsequently introduced, and the reasons and significance of enhancing mobility are clarified. This paper then explores the development of high-mobility ITZO TFTs from five perspectives: active layer optimization, gate dielectric optimization, electrode optimization, interface optimization, and device structure optimization. Finally, a summary and outlook of the research field are presented.
Low-temperature conformal vacuum deposition of OLED devices using close-space sublimation
Authors: Tam Bryan Siu Ting , Dong Shou-Cheng , Tang Ching W. 
Published: September 2023

Abstract
Close-space sublimation (CSS) has been demonstrated as an alternative vacuum deposition technique for fabricating organic light-emitting diodes (OLEDs). CSS utilizes a planar donor plate pre-coated with organic thin films as an area source to rapidly transfer the donor film to a device substrate at temperatures below 200 °C. CSS is also conformal and capable of depositing on odd-shaped substrates using flexible donor media. The evaporation behaviors of organic donor films under CSS were fully characterized using model OLED materials and CSS-deposited films exhibited comparable device performances in an OLED stack to films deposited by conventional point sources. The low temperature and conformal nature of CSS, along with its high material utilization and short process time, make it a promising method for fabricating flexible OLED displays.
Enhancing performance of inverted quantum-dot light-emitting diodes based on a solution-processed hole transport layer via ligand treatment
Authors: Li Depeng , Ma Jingrui , Liu Wenbo , Xiang Guohong , Qu Xiangwei , Jia Siqi , Gu Mi , Wei Jiahao , Liu Pai , Wang Kai , Sun Xiaowei 
Published: September 2023

Abstract
The performance of inverted quantum-dot light-emitting diodes (QLEDs) based on solution-processed hole transport layers (HTLs) has been limited by the solvent-induced damage to the quantum dot (QD) layer during the spin-coating of the HTL. The lack of compatibility between the HTL's solvent and the QD layer results in an uneven surface, which negatively impacts the overall device performance. In this work, we develop a novel method to solve this problem by modifying the QD film with 1,8-diaminooctane to improve the resistance of the QD layer for the HTL’s solvent. The uniform QD layer leads the inverted red QLED device to achieve a low turn-on voltage of 1.8 V, a high maximum luminance of 105 500 cd/m2, and a remarkable maximum external quantum efficiency of 13.34%. This approach releases the considerable potential of HTL materials selection and offers a promising avenue for the development of high-performance inverted QLEDs.