bluuebunny/crossref_metadata_2025_split huggingface dataset:
Attribution of extremes to greenhouse gas-induced changes in regional climate variability, distinct from changes in mean climate
Authors: Barkhordarian Armineh 
Published: August 2024

Abstract
Changes in regional climate variability serve as the initial indicators of positive climate feedback mechanisms, which are expected to intensify as greenhouse gas (GHG) emissions unfold. Therefore, it is crucial to examine the extent to which GHG-induced changes in regional climate variability, in isolation from changes in mean climate, contribute to the frequency of extreme weather events. In this study, I build upon the concept of the fraction of attributable risk (FAR) by introducing the fraction of preventable risk (FPR), allowing for the assessment of GHG forcing’s contribution to risk reduction in scenarios of decreasing risk extremes. Results derived from four global climate models indicate that while the predominant factor amplifying the frequency of hot extremes is the mean warming, with a 18-fold increase in probability and an attributable risk fraction of 0.96 to GHG forcing, changes in regional climate variability have already modified the probability of extremes. In South Asia, for instance, the 12-fold increase in hot extremes resulting from mean warming is compounded by an additional ∼3 times, solely attributed to the increased temperature variability. Conversely, during winter in the Arctic, the 10-fold increase in the frequency of hot extremes due to mean warming is offset by a ∼2.5-fold decrease resulting from diminished variability, with a preventable risk fraction of −0.55 to GHG forcing. Concerning heavy-precipitation events, in certain regions, GHG-induced changes in precipitation variability carry greater significance than changes in the mean. For example, in the West African summer monsoon region GHG forcing is amplifying the risk of extreme monsoon precipitation by ∼4 times. This amplified risk of potential flooding arises from increases in both mean precipitation and variability. Separating attribution metrics into mean and variability components offers valuable insights into region-specific mechanisms affecting extreme event frequency.
Health equity and synergistic abatement strategies of carbon dioxide and air pollutant emissions reduction in China’s eastern coastal area
Authors: Xu Shasha , Zhang Silu , Pan Yujie , Liu Xiaorui , Welsch Emily , Ma Xiaotian , Guo Chaoyi , Dai Hancheng 
Published: August 2024

Abstract
Quantifying regional health disparities linked to air pollution is essential for enhancing air quality and attaining carbon neutrality objectives. Nonetheless, the efficacy of proactive policies in ensuring equitable health protection in China’s Eastern Coastal Area (ECA) remains uncertain. Here, we employed an integrated assessment model to assess the combined reduction of carbon dioxide (CO2) and atmospheric pollutants and their health repercussions in the ECA of China. Our findings reveal that 273 000 premature deaths are attributable to air pollution in 2060 in the ECA in the absence of mitigation policies. Conversely, carbon reduction policies are poised to curtail 80% of CO2 emissions, alongside reductions of 76% for NOx, 79% for SO2, 80% for PM2.5, 72% for VOCs, and 66% for NH3 emissions. Air pollution control policies could mitigate premature deaths by 19 600, while carbon reduction policies could potentially lower them by 50 800. The health inequality coefficient among provinces stands at 0.19, primarily attributable to significantly higher mortality rates in Hebei and Shandong. These findings yield valuable insights for crafting synergistic abatement strategies in similarly imbalanced developmental regions grappling with comparable environmental challenges.
Monitoring the effects of climate, land cover and land use changes on multi-hazards in the Gianh River watershed, Vietnam
Authors: Nguyen Huu Duy , Dang Dinh Kha , Nguyen Quoc-Huy , Phan-Van Tan , Bui Quang-Thanh , Petrisor Alexandru-Ionut , Nghiem Son Van 
Published: August 2024

Abstract
In recent decades, global rapid urbanization has exacerbated the impacts of natural hazards due to changes in Southeast Asia’s environmental, hydrological, and socio-economic conditions. Confounding non-stationary processes of climate change and global warming and their negative impacts can make hazards more complex and severe, particularly in Vietnam. Such complexity necessitates a study that can synthesize multi-dimensional natural-human factors in disaster risk assessments. This synthesis study aims to assess and monitor climate change and land-cover/land-use change impacts on flood and landslide hazards in Vietnam’s Gianh River basin. Three Deep Neural Network (DNN) and optimization algorithms, including the Adam, Tunicate Swarm Algorithm (TSA), and Dwarf Mongoose Optimization (DMOA) were used to determine the regions with the probability of the occurrence of flood and landslide and their combination. All efficiently evaluated hazard susceptibility based on a synthesis analysis encompassing 14 natural and anthropogenic conditioning factors. Of the three, the Deep Neural Network (DNN)-DMOA model performed the best for both flood and landslide susceptibility, with area-under-curve values of 0.99 and 0.97, respectively, followed by DNN-TSA (0.97 for flood, 0.92 for landslide), and DNN-Adam (0.96 for flood, 0.89 for landslide). Although the area affected by flooding is predicted to decrease, the overall trend for total hazard-prone areas increases over 2005–2050 due to the more extensive area affected by landslides. This study develop and demonstrate a robust framework to monitor multi-hazard susceptibility, taking into account the changes in climate and land-use influence the occurrence of multiple hazards. Based on the quantitative assessment, these findings can help policymakers understand and identify confounding hazard issues to develop proactive land-management approaches in effective mitigation or adaptation strategies that are spatially and temporally appropriate.
Global pattern of soil temperature exceeding air temperature and its linkages with surface energy fluxes
Authors: Wang Ren , Lu Jiang , Gentine Pierre , Chen Haishan 
Published: August 2024

Abstract
Understanding the pattern of changes in extreme heat is crucial to developing climate change adaptation strategies. Existing studies mostly focus on changes in air temperature and tend to overlook soil temperature; however, changes in extreme heat in air and soil can be inconsistent under global change and water–carbon cycling may be more sensitive to soil condition. In this study, we examine the global pattern of long-term trends in the difference between air temperature and soil temperature (T
soil − T
2m) for the hottest month of the year during the period of 1961–2022. The results show that in certain hotspots, such as the middle and high latitudes of Eurasia, the Mediterranean, and the Western United States, the increasing trend in soil temperature has exceeded the increasing trend in 2 m air temperature during the warm season, implying that the land surface can contribute to the increase in air temperature extreme by releasing more heat than before. Our study suggest that the effect of soil temperature to air temperature is strongly related to the partitioning of surface latent heat, sensible heat (H) and soil heat flux (G). In the hot spots, T
soil − T
2m was significantly positively correlated with H and G while a significant negative correlation was found with evaporative fraction (EF) (p< 0.05), and the significant correlations with G and EF exhibit greater spatial heterogeneity. Moreover, the higher the degree of vegetation cover and soil moisture the smaller the difference between soil and air high temperatures. Therefore, changes in vegetation cover and land use management may play an important role in regulating the range of soil and air temperature differences as well as land-atmosphere coupling effects on heat extreme.
Super-resolution of spin configurations based on flow-based generative models
Authors: Shiina Kenta , Mori Hiroyuki , Okabe Yutaka , Kuan Lee Hwee 
Published: August 2024

Abstract
We present a super-resolution method for spin systems using a flow-based generative model that is a deep generative model with reversible neural network architecture. Starting from spin configurations on a two-dimensional square lattice, our model generates spin configurations of a larger lattice. As a flow-based generative model precisely estimates the distribution of the generated configurations, it can be combined with Monte Carlo simulation to generate large lattice configurations according to the Boltzmann distribution. Hence, the long-range correlation on a large configuration is reduced into the shorter one through the flow-based generative model. This alleviates the critical slowing down near the critical temperature. We demonstrated an 8 times increased lattice size in the linear dimensions using our super-resolution scheme repeatedly. We numerically show that by performing simulations for

16
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configurations, our model can sample lattice configurations at

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on which the thermal average of physical quantities has good agreement with the one evaluated by the traditional Metropolis–Hasting Monte Carlo simulation.
Fluctuating hydrodynamics of active particles interacting via taxis and quorum sensing: static and dynamics
Authors: Dinelli Alberto , O’Byrne Jérémy , Tailleur Julien 
Published: August 2024

AbstractIn this article we derive and test the fluctuating hydrodynamic description of active particles interacting viataxis and quorum sensing, both for mono-disperse systems and for mixtures of co-existing species of active particles. We compute the average steady-state density profile in the presence of spatial motility regulation, as well as the structure factor and intermediate scattering function for interacting systems. By comparing our predictions to microscopic numerical simulations, we show that our fluctuating hydrodynamics correctly predicts the large-scale static and dynamical properties of the system. We also discuss how the theory breaks down when structures emerge at scales smaller or comparable to the persistence length of the particles. When the density field is the unique hydrodynamic mode of the system, we show that active Brownian particles, run-and-tumble particles and active Ornstein–Uhlenbeck particles, interacting via quorum-sensing or chemotactic interactions, display undistinguishable large-scale properties. This form of universality implies an interesting robustness of the predicted physics but also that large-scale observations of patterns are insufficient to assess their microscopic origins. In particular, our results predict that chemotaxis-induced and motility-induced phase separation should share strong qualitative similarities at the macroscopic scale.
Volatile organic compound analysis of malignant pleural mesothelioma chorioallantoic membrane xenografts
Authors: Little Liam D , Barnett Sarah E , Issitt Theo , Bonsall Sam , Carolan Vikki A , Allen Elizabeth , Cole Laura M , Cross Neil A , Coulson Judy M , Haywood-Small Sarah L 
Published: August 2024

Abstract
Malignant pleural mesothelioma (MPM) is an aggressive cancer associated with asbestos exposure. MPM is often diagnosed late, at a point where limited treatment options are available, but early intervention could improve the chances of successful treatment for MPM patients. Biomarkers to detect MPM in at-risk individuals are needed to implement early diagnosis technologies. Volatile organic compounds (VOCs) have previously shown diagnostic potential as biomarkers when analysed in MPM patient breath. In this study, chorioallantoic membrane (CAM) xenografts of MPM cell lines were used as models of MPM tumour development for VOC biomarker discovery with the aim of generating targets for investigation in breath, biopsies or other complex matrices. VOC headspace analysis of biphasic or epithelioid MPM CAM xenografts was performed using solid-phase microextraction and gas chromatography-mass spectrometry. We successfully demonstrated the capture, analysis and separation of VOC signatures from CAM xenografts and controls. A panel of VOCs was identified that showed discrimination between MPM xenografts generated from biphasic and epithelioid cells and CAM controls. This is the first application of the CAM xenograft model for the discovery of VOC biomarkers associated with MPM histological subtypes. These findings support the potential utility of non-invasive VOC profiling from breath or headspace analysis of tissues for detection and monitoring of MPM.
Mode analysis for multiple parameter conditions of nozzle internal unsteady flow using Parametric Global Proper Orthogonal Decomposition
Authors: Kawaguchi Mikimasa , Iwasaki Masato , Nakayama Ryoutaro , Yamamoto Ryo , Nakashima Akira , Ogata Yoichi 
Published: August 2024

Abstract
Analysis methods based on mode decomposition have been proposed to describe the characteristics of flow phenomena. Among them, proper orthogonal decomposition (POD), which decomposes modes into eigenvalues and basis vectors, has long been used. Many studies have shown that POD is a useful method for capturing the characteristics of unsteady flow. In particular, Snapshot POD has attracted much recent attention and has been used to solve unsteady flow problems. However, the basis vectors of the mode obtained by conventional POD is different for each condition. Therefore, whether the basis vectors of each mode are switching in the direction of parameters (e.g. different shapes or different Reynolds numbers) or whether they develop or decay is difficult to discuss. As a result, discussions on conventional POD tend to be qualitative. To address this issue, the present study uses Parametric Global POD, a method that perfectly matches basis vectors in results with different parameters (in this study, different Reynolds numbers). Parametric Global POD method was applied to the analysis of the flow field in a curved pipe and found to capture the development or decay of modes with major basis vectors in the direction of parameters, which is difficult to achieve with conventional POD methods.
Microstructure and mechanical properties of 1 mm thin plate TC4 titanium alloy joint by ring laser welding
Authors: Liang Jianhui , Liu Zhanqi , Li Xiang , Wang Haijiang , Yin Guili 
Published: August 2024

Abstract
Ring laser welding was used to weld 1 mm thin plates TC4 titanium alloy, and the effects of different ring laser powers on the microstructure and mechanical properties of the weld were studied. The results indicate that the microstructure in the weld zone under different powers all contain basket structures, although the morphology of the martensite within the baskets differs. The microstructure in the weld zone is mainly composed of acicular α′ martensite, secondary α phase, and residual β phase. The microstructure in the heat-affected zone of the weld is mainly composed of acicular α′ martensite, initial α phase, secondary α phase, and residual β phase. The hardness of the weld is higher than that of the base metal, showing an overall trend of first increasing, then decreasing, and then increasing again before decreasing. The tensile specimens under different powers all fractured in the heat-affected zone of the weld, and the type of fracture is quasi-cleavage. At a laser power of 900 W, the weld has good formation, with minimal spatter on the surface, and good weld penetration. The microstructure of the weld is mainly composed of acicular α′ martensite, initial α phase, secondary α phase, and residual β phase. The hardness of the weld is 387.76 HV. The tensile strength and elongation are 1098.3 MPa and 12%, respectively.
From architectures to applications: a review of neural quantum states
Authors: Lange Hannah , Van de Walle Anka , Abedinnia Atiye , Bohrdt Annabelle 
Published: August 2024

Abstract
Due to the exponential growth of the Hilbert space dimension with system size, the simulation of quantum many-body systems has remained a persistent challenge until today. Here, we review a relatively new class of variational states for the simulation of such systems, namely neural quantum states (NQS), which overcome the exponential scaling by compressing the state in terms of the network parameters rather than storing all exponentially many coefficients needed for an exact parameterization of the state. We introduce the commonly used NQS architectures and their various applications for the simulation of ground and excited states, finite temperature and open system states as well as NQS approaches to simulate the dynamics of quantum states. Furthermore, we discuss NQS in the context of quantum state tomography.
Squeezing below the ground state of motion of a continuously monitored levitating nanoparticle
Authors: Wu Q , Chisholm D A , Muffato R , Georgescu T , Homans J , Ulbricht H , Carlesso M , Paternostro M 
Published: August 2024

Abstract
Squeezing is a crucial resource for quantum information processing and quantum sensing. In levitated nanomechanics, squeezed states of motion can be generated via temporal control of the trapping frequency of a massive particle. However, the amount of achievable squeezing typically suffers from detrimental environmental effects. We propose a scheme for the generation of significant levels of mechanical squeezing in the motional state of a levitated nanoparticle by leveraging on the careful temporal control of the trapping potential. We analyse the performance of such a scheme by fully accounting for the most relevant sources of noise, including measurement backaction. The feasibility of our proposal, which is close to experimental state-of-the-art, makes it a valuable tool for quantum state engineering.
Travel footprints in the nordics
Authors: Raudsepp Johanna , Czepkiewicz Michał , Heinonen Jukka , Árnadóttir Áróra 
Published: August 2024

Abstract
This study provides analyses of carbon footprint survey data from about 7500 respondents in the Nordics to present an overview of Nordic personal travel footprints. The study considers the spatial distribution of travel footprints, the influence of climate concern, and how the footprints fit within the 1.5-degree compatible threshold for 2030. Spatial variability from urban to rural areas differed from country to country. Low climate concern was linked to higher local and long-distance travel emissions. Travel footprints in all countries exceed the recommended threshold level, indicating a need for rapid action to reduce travel emissions in upcoming years. Moreover, there are indications that people who currently meet the threshold could belong to lower socio-economic groups, raising concern about meeting the travel needs of everyone. The study further highlights the context-dependence of the transport sector, even among countries with a similar background, which should be considered in mitigation policy.
Improving the precision of work-function calculations within plane-wave density functional theory
Authors: Wang Yiyuan , J Laihonen Sari , Unge Mikael , Mostofi Arash A 
Published: August 2024

Abstract
Work function is a fundamental property of metals and is related to many surface-related phenomena of metals. Theoretically, it can be calculated with a metal slab supercell in density functional theory (DFT) calculations. In this paper, we discuss how the commensurability of atomic structure with the underlying fast Fourier transform (FFT) grid affects the accuracy of work function obtained from plane-wave pseudopotential DFT calculations. We show that the macroscopic average potential, which is an important property in work function calculations under the ‘bulk reference’ method, is more numerically stable when it is calculated with commensurate FFT grids than with incommensurate FFT grids. Due to the stability of the macroscopic average potential, work function calculated with commensurate FFT grids shows better convergence with respect to basis set size, vacuum length and slab thickness of a slab supercell. After we control the FFT grid commensurability issue in our work function calculations, we obtain well-converged work functions for Al, Pd, Au and Pt of (100), (110) and (111) surface orientations. For all the metals considered, the ordering of our calculated work functions of the three surface orientations agrees with experiment. Our findings reveal the importance of the FFT grid commensurability issue, which is usually neglected in practice, in obtaining accurate metal work functions, and are also meaningful to other DFT calculations which can be affected by the FFT grid commensurability issue.
Experimental and numerical study on the performance index of mixing for low aspect ratio serpentine microchannels
Authors: Pirdavari Pooria , Pourfattah Farzad , Tran Huy , Wang Lian-Ping , He Ziwen , Pack Min Y 
Published: August 2024

Abstract
In this work, the effect of a range of Dean numbers (De) varying from 0.01–70 on low aspect ratio (AR = 0.05–0.2) serpentine microfluidic devices was studied experimentally and numerically. It was observed that the AR, the number of circular bumps, and the angular positions of bumps transverse to the flow have a significant influence on the pressure drop and flow features (i.e., the position and shape of flow separation zones). Mixing was exclusively driven by diffusive mechanisms at low De values and at high De values, it was primarily induced by Dean vortices. The lowest mixing index (MI) was observed for De = 1 in all channel types, highlighting the transition region between the diffusion and Dean vortices-dominant mixing regimes. The MI was generally increased by increasing the AR of the channels. However, at high De, Dean vortices became strong enough to induce rapid mixing that was largely independent of the AR and bump placement. A dimensional performance index (PI) was defined as a function of the MI and the pressure drop per unit length. Distinct flow patterns arising from various positioning of bumps resulted in significant variations in the MI and PI values, with different dependencies on De. This underscored the importance of bump positioning based on the operational De range to optimize the mixing performance. Despite minor deviations between the designed and fabricated channels, the use of 3D-printed molds proved effective even at scales close to the resolution of the printer, resulting in mixing patterns consistent with the designed channels. These findings provide valuable insights into optimizing serpentine microchannels for efficient mixing while considering the trade-offs between enhanced mixing and increased pressure drop.
A combined modeling method for complex multi-fidelity data fusion
Authors: Tang Lei , Liu Feng , Wu Anping , Li Yubo , Jiang Wanqiu , Wang Qingfeng , Huang Jun 
Published: August 2024

Abstract
Currently, mainstream methods for multi-fidelity data fusion have achieved great success in many fields, but they generally suffer from poor scalability. Therefore, this paper proposes a

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combination modeling method for complex multi-fidelity data fusion, devoted to solving the modeling problems with three types of multi-fidelity data fusion, and explores a general solution for any n types of multi-fidelity data fusion. Different from the traditional direct modeling method—Multi-Fidelity Deep Neural Network (MFDNN)—the

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method is an indirect modeling method. The experimental results on three representative benchmark functions and the prediction tasks of SG6043 airfoil aerodynamic performance show that

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combination modeling has the following advantages: (1) It can quickly establish the mapping relationship between high, medium, and low fidelity data. (2) It can effectively solve the data imbalance problem in multi-fidelity modeling. (3) Compared with MFDNN, it has stronger noise resistance and higher prediction accuracy. Additionally, this paper discusses the scalability problem of the

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method when n = 4 and n = 5, providing a reference for further research on the combined modeling method.