bluuebunny/crossref_metadata_2025_split huggingface dataset:
ADRC‐based symmetric phase‐locked loop structure for improving low‐frequency stability of grid‐connected inverters
Authors: Zhu Yuan , Ohyama Kazuhiro 
Published: July 2024

AbstractWith a small short‐circuit ratio (SCR), the grid‐connected inverter is prone to low‐frequency oscillation instability due to the dynamic interaction between the phase‐locked loop (PLL) and the weak grid. To this end, an active‐disturbance‐rejection‐controller‐based symmetric PLL (ADRC‐based SyPLL) is proposed in this article to simplify the system modelling and improve the low‐frequency stability of the inverter system. Specifically, the ADRC technique is applied to phase‐regulated feedback control of a PLL, where an extended state observer (ESO) is used to estimate the lumped disturbance of the controlled system. A control loop symmetric to the q‐axis is further designed on the d‐axis of the PLL, and the d‐axis information is then added at the reference generation side to eliminate the coupled frequency term in the small‐signal model. A simple and accurate single‐input single‐output (SISO) model is thus obtained. The impedance stability analysis for grid‐connected inverters shows that the proposed ADRC‐based SyPLL can significantly improve the low‐frequency stability margin of the system compared with the conventional PI‐regulated PLL. Finally, the experimental results validate the effectiveness and superiority of the proposed method.
Review on reliability assessment of energy storage systems
Authors: Yan Xiaohe , Li Jialiang , Zhao Pengfei , Liu Nian , Wang Liangyou , Yue Bo , Liu Yanchao 
Published: July 2024

AbstractAs renewable energy, characterised by its intermittent nature, increasingly penetrates the conventional power grid, the role of energy storage systems (ESS) in maintaining energy balance becomes paramount. This dynamic necessitates a rigorous reliability assessment of ESS to ensure consistent energy availability and system stability. The authors provide a review of the existing research on ESS reliability assessment, encompassing various methods, models, reliability indicators, and offers an analysis of future research trends in ESS reliability. Firstly, the authors summarise the different types of ESS and their characteristics, analysing the trends in ESS reliability research and the unique characteristics of ESS compared to conventional power systems. Secondly, the methods used for the assessment are reviewed, including Markov methods, generalised generating functions, Monte Carlo simulations etc. The shortcomings and characteristics of these methods are discussed. The key reliability indicators, such as Mean Time Between Failures and Mean Time to Repair are emphasised. The applied role of reliability studies is summarised. Finally, the perspective of new research trends in ESS reliability assessment are identified, especially the integration of artificial intelligence and machine learning, and emphasises their potential to further improve the robustness and effectiveness of ESS reliability.
Cubic coil system composed of three sets of cubic triple coils to produce wide area 0.66% uniform magnetic field in any direction
Authors: Daimon Hiroshi , Matsushima Satoru 
Published: July 2024

A cubic coil system, which combines three sets of cubic triple square coils orthogonal to each other, is proposed, constructed, and tested. This coil system can generate a 0.66% (=±0.33%) uniform magnetic field in any direction over a large region of ∼2 m, although there is some ripple. This system has the advantages of easy assembly, self-standing, and easy access to internal instruments. In one set of cubic triple square coils, the outer two square coils form a cube, and the middle square coil is located at the center of the cube. The number of turns in the center square coil is proposed to be 0.6 times that of the outer square coil.
Nonlinear-Optical Analogies in Nuclear-Like Soliton Reactions: Selection Rules, Nonlinear Tunneling and Sub-Barrier Fusion–Fission
Authors: Belyaeva T. L. , Serkin V. N. 
Published: July 2024

Abstract
The main goal of our study is to reveal unexpected but intriguing analogies arising between optical solitons and nuclear physics, which still remain hidden from us. We consider the main cornerstones of the concept of nonlinear optics of nuclear reactions and the well-dressed repulsive-core solitons. On the base of this model, we reveal the most intriguing properties of the nonlinear tunneling of nucleus-like solitons and the soliton self-induced sub-barrier transparency effect. We describe novel interesting and stimulating analogies between the interaction of nucleus-like solitons on the repulsive barrier and nuclear sub-barrier reactions. The main finding of this study concerns the conservation of total number of nucleons (or the baryon number) in nuclear-like soliton reactions. We show that inelastic interactions among well-dressed repulsive-core solitons arise only when a “cloud” of “dressing” spectral side-bands appears in the frequency spectra of the solitons. This property of nucleus-like solitons is directly related to the nuclear density distribution described by the dimensionless small shape-squareness parameter. Thus the Fourier spectra of nucleus-like solitons are similar to the nuclear form factors. We show that the nuclear-like reactions between well-dressed solitons are realized by “exchange” between “particle-like” side bands in their spectra.
Structural and Ferroelectric Transition in Few-Layer HfO2 Films by First Principles Calculations
Authors: Gao 高 Ruiling 瑞灵 , Liu 刘 Chao 超 , Shi 施 Bowen 博文 , Li 李 Yongchang 永昌 , Luo 罗 Bing 兵 , Chen 陈 Rui 睿 , Ouyang 欧 Wenbin 阳文彬 , Gao 高 Heng 恒 , Hu 胡 Shunbo 顺波 , Wang 王 Yin 音 , Li 李 Dongdong 东栋 , Ren 任 Wei 伟 
Published: July 2024

Abstract
The discovery of ferroelectricity in HfO2-based materials with high dielectric constant has inspired tremendous research interest for next-generation electronic devices. Importantly, films structure and strain are key factors in exploration of ferroelectricity in fluorite-type oxide HfO2 films. Here we investigate the structures and strain-induced ferroelectric transition in different phases of few-layer HfO2 films (layer number N = 1–5). It is found that HfO2 films for all phases are more stable with increasing films thickness. Among them, the Pmn21 (110)-oriented film is most stable, and the films of N = 4, 5 occur with a P21 ferroelectric transition under tensile strain, resulting in polarization about 11.8 μC/cm2 along in-plane a-axis. The ferroelectric transition is caused by the strain, which induces the displacement of Hf and O atoms on the surface to non-centrosymmetric positions away from the original paraelectric positions, accompanied by the change of surface Hf–O bond lengths. More importantly, three new stable HfO2 2D structures are discovered, together with analyses of computed electronic structures, mechanical, and dielectric properties. This work provides guidance for theoretical and experimental study of the new structures and strain-tuned ferroelectricity in freestanding HfO2 films.
A comparative study of the Au/n-Si (MS) and Au/(ZnO:CeO2:PVP)/n-Si (MPS) Schottky structures by using current/voltage characteristics in dark and under illumination
Authors: Üstün Oray , Özçelik Uğur , Azizian-Kalandaragh Yashar , Altındal Şemsettin , Özçelik Süleyman 
Published: July 2024

Abstract
In the present study, both metal/semiconductor (MS) and metal/polymer/semiconductor (MPS) Schottky Diodes (SDs) were grown onto the same n-Si wafer to compare their electrical and optical characteristics. Firstly, ZnO and CeO2 nanostructures were synthesized by ultrasonic-assisted method (UAM), and structurally characterized by utilizing x-ray diffraction (XRD), Ultraviolet-visible spectroscopy (UV–vis), and Fourier-Transform-IR (FTIR) methods. The mean submicron crystallite sizes were estimated to be below 11.39 nm for CeO2 and 54.37 nm for ZnO nanostructures through the Debye–Scherrer method. The optical bandgap was calculated as 3.84 eV for CeO2 and 3.88 eV for ZnO nanostructures via Tauc plot. Electrical parameters such as reverse-saturation current (Io), ideality-factor (n), zero-bias barrier height (ΦBo), and rectification-ratio (RR) were found as 0.596 μA, 5.45, 0.64 eV, 2.74 × 105 in dark and 5.54 μA, 5.88, 0.59 eV, 8.60 × 103 under illumination for the MS SD and 0.027 μA, 4.36, 0.72 eV, 1.85 × 107 in dark and 0.714 μA, 5.18, 0.64 eV, 7.61 × 104 under illumination for the MPS SD, respectively. The energy-dependent profile of surface-states was obtained via the Card-Rhoderick method, by considering ΦB(V) and n. RR of the MPS SD is almost sixty-seven times the RR of the MS SD in the dark. The sensitivity of the MPS SD (=710) is nineteen and five-tenths the sensitivity of the MS SD (=36.4), so the MPS SD is considerably more sensitive to illumination. These results indicate that the (ZnO:CeO2:PVP) organic interlayer significantly improves the performance of the MS SD.
Compact magneto-optical traps using planar optics
Authors: Tan 谭 Zhi 智 , Lu 鹿 Bo 博 , Han 韩 Chengyin 成银 , Lee 李 Chaohong 朝红 
Published: July 2024

Abstract
Magneto-optical traps (MOTs) composed of magnetic fields and light fields have been widely utilized to cool and confine microscopic particles. Practical technology applications require miniaturized MOTs. The advancement of planar optics has promoted the development of compact MOTs. In this article, we review the development of compact MOTs based on planar optics. First, we introduce the standard MOTs. We then introduce the grating MOTs with micron structures, which have been used to build cold atomic clocks, cold atomic interferometers, and ultra-cold sources. Further, we introduce the integrated MOTs based on nano-scale metasurfaces. These new compact MOTs greatly reduce volume and power consumption, and provide new opportunities for fundamental research and practical applications.
Interaction between the core and the edge for ion cyclotron resonance heating based on artificial absorption plasma model
Authors: LIU 刘 Zhuoqi 卓琪 , ZHANG 张 Jiahui 珈珲 , WU 吴 Kaibang 凯邦 , ZHANG 张 Xinjun 新军 , QIN 秦 Chengming 成明 , WANG 王 Feng 丰 , WANG 王 Zhengxiong 正汹 
Published: July 2024

Abstract
In numerical simulations of the ion cyclotron range of frequencies (ICRF) wave heating scheme, core solvers usually focus on wave propagation and absorption mechanisms within the core plasma region. However, the realistic scrape-off layer (SOL) plasma is usually simplified, making it difficult to have deeper understanding of wave propagation and absorption within the SOL. In this work, we employ a cold plasma assumption and an artificial absorption mechanism based on the approach of reference (Zhang et al 2022 Nucl. Fusion
62 076032), to study wave propagation and absorption in the realistic SOL plasma of the EAST. During the exponential decay of the total coupled power with respect to the toroidal mode numbers, several fluctuations are observed in the case of low collisional frequencies. The fluctuations may be caused by the cavity modes associated with specific toroidal mode numbers. Due to the presence of cut-off densities, the edge power losses and the total coupled power exhibit different behaviors before and after the cut-off layer is “open”. Furthermore, the simulation results obtained from the kinetic model in reference (Zhang et al 2022 Nucl. Fusion
62 076032) is discussed. This suggests that both the core-edge combined model and the artificial mechanism are capable of simulating wave propagation and absorption.
Approaching periodic systems in ensemble density functional theory via finite one-dimensional models
Authors: Leano Remi J , Pribram-Jones Aurora , Strubbe David A 
Published: July 2024

Abstract
Ensemble density functional theory (EDFT) is a generalization of ground-state DFT, which is based on an exact formal theory of finite collections of a system’s ground and excited states. EDFT in various forms has been shown to improve the accuracy of calculated energy level differences in isolated model systems, atoms, and molecules, but it is not yet clear how EDFT could be used to calculate band gaps for periodic systems. We extend the application of EDFT toward periodic systems by estimating the thermodynamic limit with increasingly large finite one-dimensional ‘particle in a box’ systems, which approach the uniform electron gas (UEG). Using ensemble-generalized Hartree and local spin density approximation exchange-correlation functionals, we find that corrections go to zero in the infinite limit, as expected for a metallic system. However, there is a correction to the effective mass, with results comparable to other calculations on 1D, 2D, and 3D UEGs, which indicates promise for non-trivial results from EDFT on periodic systems.
Structural health monitoring of a swivel bridge for evaluating builder comfort under train-induced vibration
Authors: Huaxi Lu , Yuting Deng , Xiang Wang 
Published: July 2024

Abstract
The swivel bridge being built overcrossing the railway service line, the builder’s comfort level under the train-induced vibration is one of the critical issues in the bridge construction process. This study compares the applicability and usage limits of existing vibration comfort evaluation indicators for reasonably selected vibration comfort evaluation criteria. A framework is therefore proposed to evaluate builder comfort under train-induced vibrations during the construction process. Taking a swivel bridge overcrossing the Shanghai-Kunming railway line as an example, the structural health monitoring system is performed in three construction stages of the swivel bridge. Then, the acceleration response is measured, the acceleration-based evaluation indicators are compared, and the builder comfort level is evaluated in the three construction stages. The results demonstrate that the on-site measured indicators are close to the standard limits, and the builder comfort on the swivel bridge under the excitation of passing trains should be given attention.
‘A movie about flowers?’ Notes on the ecological turn in adaptation studies
Authors: Elliott Tomas 
Published: July 2024

Abstract
This article takes up and responds to the recent ecological turn in adaptation studies, exploring the discipline’s widespread interest in the overlap between the notion of adaptation in evolutionary biology and the notion of adaptation in literature, film, and media studies. It argues that in order to develop a historically and ecocritically alert approach to adaptation studies, it is necessary to unpack what is at stake in using biological terms and paradigms to study adaptation in art. Firstly, it offers a survey of several studies that have explored the overlap between adaptation in nature and adaptation in culture, arguing that these have been overly influenced by the notions of neo-Darwinism that were popularized by Richard Dawkins in The Selfish Gene (1976). Secondly, it offers a rereading of the film that has become a primary case study among theorists who have reached for biological metaphors to explain cultural change: Adaptation (2002). It argues that whereas scholars have often tended to use Adaptation as a springboard from which to launch an exploration of the purported homology between adaptation in nature and adaptation in art, in fact, the film’s evolutionary themes are clearly historicizable, tied to a set of values coordinated around ideas of heteronormative reproductivity, dissemination, and growth. Examining those values helps to demonstrate how the film’s evolutionary themes are deployed as part of its representational strategies, thereby challenging the idea that they might be unproblematically used to describe the overlap between adaptation in biology and adaptation in art.
RepurposeDrugs: an interactive web-portal and predictive platform for repurposing mono- and combination therapies
Authors: Ianevski Aleksandr , Kushnir Aleksandr , Nader Kristen , Miihkinen Mitro , Xhaard Henri , Aittokallio Tero , Tanoli Ziaurrehman 
Published: July 2024

Abstract
RepurposeDrugs (https://repurposedrugs.org/) is a comprehensive web-portal that combines a unique drug indication database with a machine learning (ML) predictor to discover new drug-indication associations for approved as well as investigational mono and combination therapies. The platform provides detailed information on treatment status, disease indications and clinical trials across 25 indication categories, including neoplasms and cardiovascular conditions. The current version comprises 4314 compounds (approved, terminated or investigational) and 161 drug combinations linked to 1756 indications/conditions, totaling 28 148 drug–disease pairs. By leveraging data on both approved and failed indications, RepurposeDrugs provides ML-based predictions for the approval potential of new drug–disease indications, both for mono- and combinatorial therapies, demonstrating high predictive accuracy in cross-validation. The validity of the ML predictor is validated through a number of real-world case studies, demonstrating its predictive power to accurately identify repurposing candidates with a high likelihood of future approval. To our knowledge, RepurposeDrugs web-portal is the first integrative database and ML-based predictor for interactive exploration and prediction of both single-drug and combination approval likelihood across indications. Given its broad coverage of indication areas and therapeutic options, we expect it accelerates many future drug repurposing projects.
Comparative analysis of integrative classification methods for multi-omics data
Authors: Novoloaca Alexei , Broc Camilo , Beloeil Laurent , Yu Wen-Han , Becker Jérémie 
Published: July 2024

Abstract
Recent advances in sequencing, mass spectrometry, and cytometry technologies have enabled researchers to collect multiple ’omics data types from a single sample. These large datasets have led to a growing consensus that a holistic approach is needed to identify new candidate biomarkers and unveil mechanisms underlying disease etiology, a key to precision medicine. While many reviews and benchmarks have been conducted on unsupervised approaches, their supervised counterparts have received less attention in the literature and no gold standard has emerged yet. In this work, we present a thorough comparison of a selection of six methods, representative of the main families of intermediate integrative approaches (matrix factorization, multiple kernel methods, ensemble learning, and graph-based methods). As non-integrative control, random forest was performed on concatenated and separated data types. Methods were evaluated for classification performance on both simulated and real-world datasets, the latter being carefully selected to cover different medical applications (infectious diseases, oncology, and vaccines) and data modalities. A total of 15 simulation scenarios were designed from the real-world datasets to explore a large and realistic parameter space (e.g. sample size, dimensionality, class imbalance, effect size). On real data, the method comparison showed that integrative approaches performed better or equally well than their non-integrative counterpart. By contrast, DIABLO and the four random forest alternatives outperform the others across the majority of simulation scenarios. The strengths and limitations of these methods are discussed in detail as well as guidelines for future applications.
DeepGRNCS: deep learning-based framework for jointly inferring gene regulatory networks across cell subpopulations
Authors: Lei Yahui , Huang Xiao-Tai , Guo Xingli , Hang Katie Chan Kei , Gao Lin 
Published: July 2024

Abstract
Inferring gene regulatory networks (GRNs) allows us to obtain a deeper understanding of cellular function and disease pathogenesis. Recent advances in single-cell RNA sequencing (scRNA-seq) technology have improved the accuracy of GRN inference. However, many methods for inferring individual GRNs from scRNA-seq data are limited because they overlook intercellular heterogeneity and similarities between different cell subpopulations, which are often present in the data. Here, we propose a deep learning-based framework, DeepGRNCS, for jointly inferring GRNs across cell subpopulations. We follow the commonly accepted hypothesis that the expression of a target gene can be predicted based on the expression of transcription factors (TFs) due to underlying regulatory relationships. We initially processed scRNA-seq data by discretizing data scattering using the equal-width method. Then, we trained deep learning models to predict target gene expression from TFs. By individually removing each TF from the expression matrix, we used pre-trained deep model predictions to infer regulatory relationships between TFs and genes, thereby constructing the GRN. Our method outperforms existing GRN inference methods for various simulated and real scRNA-seq datasets. Finally, we applied DeepGRNCS to non-small cell lung cancer scRNA-seq data to identify key genes in each cell subpopulation and analyzed their biological relevance. In conclusion, DeepGRNCS effectively predicts cell subpopulation-specific GRNs. The source code is available at https://github.com/Nastume777/DeepGRNCS.
Using citizen science data for predicting the timing of ecological phenomena across regions
Authors: Capinha César , Ceia-Hasse Ana , de-Miguel Sergio , Vila-Viçosa Carlos , Porto Miguel , Jarić Ivan , Tiago Patricia , Fernández Néstor , Valdez Jose , McCallum Ian , Pereira Henrique Miguel 
Published: July 2024

Abstract
The scarcity of long-term observational data has limited the use of statistical or machine-learning techniques for predicting intraannual ecological variation. However, time-stamped citizen-science observation records, supported by media data such as photographs, are increasingly available. In the present article, we present a novel framework based on the concept of relative phenological niche, using machine-learning algorithms to model observation records as a temporal sample of environmental conditions in which the represented ecological phenomenon occurs. Our approach accurately predicts the temporal dynamics of ecological events across large geographical scales and is robust to temporal bias in recording effort. These results highlight the vast potential of citizen-science observation data to predict ecological phenomena across space, including in near real time. The framework is also easily applicable for ecologists and practitioners already using machine-learning and statistics-based predictive approaches.