bluuebunny/crossref_metadata_2025 huggingface dataset:
The formation of an radial edge electric field due to finite ion orbit width effects is the possible root cause of the H-mode edge
Authors: Kramer G.J. , Bortolon A. , Diallo A. , Maingi R.
Published: August 2024

Full orbit-following simulations of thermal ions show that finite ion-orbit width effects create charge separation near the last closed flux surface (LCFS) which generates a localized radial electric field. Experimentally, edge electric fields are observed in H-mode plasmas and they are necessary for the edge turbulence suppression via the

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flow shear mechanism. Confined trapped (and to a lesser extent co-passing) ions near the plasma edge form a positive charge distribution outside the LCFS, while thermal electrons are tied more tightly to field lines owing to their small mass and are poorly confined outside the LCFS, hence charge neutrality is violated outside the LCFS. A large number of reported observations from spherical and conventional tokamaks support the results from the simulations although the simulations were not performed fully self consistently. The results suggest ways to lower the H-mode power threshold and optimize the H-mode plasma edge.
MBSE-based aircraft test and evaluation model construction
Authors: An Ran
Published: August 2024

This paper carries out the research of the MBSE method based on the DoDAF modeling framework, focusing on the whole process of aircraft testing and evaluation. From eight points of view, the business, resources, standards, capabilities, and data of flight tests are designed and planned, the management model with the characteristics of development and evaluation flight test is constructed, and the model-based test flight engineering is explored. This method can standardize the whole cycle of development test and evaluation, effectively improve the efficiency of testing and evaluation, reduce the cost of testing, and shorten the test cycle.
Finite element simulation study of signal characteristics of pulsed eddy current detection technology
Authors: Zhang Ruijun
Published: August 2024

The principle of pulsed eddy current detection is the phenomenon of electromagnetic induction, and it is difficult to study pulsed eddy current detection technology through theoretical derivation due to the complexity of electromagnetic induction. Finite element analysis uses a mathematical approximation to simulate the real physical system, and at the same time, its use of simple but interacting elements makes it possible to approximate the real system of infinite unknowns through a finite number of unknowns. Finite elements not only have high computational accuracy, but also can adapt to a variety of complex shapes, and can easily deal with the advantages of nonlinear media properties. In the electromagnetic field and other problem-solving, it has a very wide range of applications. Therefore, this paper uses finite element simulation technology to design a pulse eddy current simulation model, using the model to study some parameters of the pulse eddy current detection model. Some conclusions can be obtained after several sets of experiments: (1) Whether the test piece has defects or not, the peak value of the detection voltage decreases with the increase of the inner and outer diameters of the coil. (2) Under the same inner and outer diameters of the coil, the peak value of the detection voltage with defects is smaller than that without defects. (3) The specific frequency of the pulse improves the sensitivity of the detection. (4) The bigger the lift distance between the coils and the measured parts is, the smaller the detection signal is. (5) The deeper the defect is, the lower the detection voltage is.
Novel indium phosphide charged particle detector characterization with a 120 GeV proton beam
Authors: Kim Sungjoon , Jadhav Manoj B. , Berry Vikas , Metcalfe Jessica E. , Sumant Anirudha V.
Published: August 2024

Thin film detectors which incorporate semiconductor
materials other than silicon have the potential to build upon their
unique material properties and offer advantages such as faster
response times, operation at room temperature, and radiation
hardness. To explore the possibility, promising candidate materials
were selected, and particle tracking detectors were fabricated. An
indium phosphide detector with a metal-intrinsic-metal structure has
been fabricated for particle tracking. The detector was tested using
radioactive sources and a high energy proton beam at Fermi National
Accelerator Laboratory. In addition to its simplistic design and
fabrication process, the indium phosphide particle detector showed a
very fast response time of hundreds of picoseconds for the 120 GeV
protons, which are comparable to the ultra-fast silicon
detectors. This fast-timing response is attributed to the high
electron mobility of indium phosphide. Such material properties can
be leveraged to build novel detectors with superlative performance.
The status of algal decarbonization in Southeast Asian region: a review
Authors: Rehman Zia Ur , Punnaanan Kantaphan , Nootong Kasidit , In-na Pichaya
Published: August 2024

Due to their autotrophic nature, algae capture large quantities of CO2 from the atmosphere and convert it to chemical energy in the form of biomolecules and cell mass for various applications. Such bio-based CO2 capture plays an important role in global carbon neutrality, while making innovative and sustainable food and industrial products for human and animal use. This review broadly illustrates the total CO2 emissions of the Southeast Asian region, named as the Association of Southeast Asian Nations (ASEAN), while estimating CO2 capture by regional commercial algae. Among all these nations, the top CO2 emitter in the ASEAN region is Indonesia (625 Mt yr−1) followed by Vietnam (311 Mt yr−1), Thailand (289 Mt yr−1), Malaysia (249 Mt yr−1), the Philippines (154 Mt yr−1), Singapore (52 Mt yr−1), Myanmar (49 Mt yr−1), Cambodia (16 Mt yr−1), Laos and Brunei (7 Mt yr−1). Indonesia is also ranked first in commercial algae production (9918 400 tons yr−1), followed by the Philippines (1500 326 tons yr−1) and Malaysia (188 110 tons yr−1). Similarly, the highest estimated algal CO2 capture is made by Indonesia (11 327 817 tons yr−1) followed by the Philippines (1705 871 tons yr−1) and Malaysia (214 279 tons yr−1), with other ASEAN countries having negligible algal CO2 capture due to either low algae production or a lack of data. The ASEAN region may play a pivotal role in the bio-capture of CO2 with the help of the phytoplanktonic aquaculture industry for value-added products. The rapid emergence of the algal industry in the ASEAN region was due to increased global demand for carrageenophyte seaweeds (98.63% of global), such as Eucheuma sp., from Indonesia and Kappaphycus alvarezii from Malaysia and the Philippines. Due to its tropical climate, high aquatic biodiversity, sufficient water and nutrient resources, and long coastlines, the ASEAN regional governments took prompt action and implemented policies for increased seaweed industry in the region. Conclusively, it is desirable to further strengthen the algal industry in these regional countries for useful products and efficient CO2 capture.
Attribution of extremes to greenhouse gas-induced changes in regional climate variability, distinct from changes in mean climate
Authors: Barkhordarian Armineh
Published: August 2024

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

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

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

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

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

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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

In 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

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

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

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

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

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

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

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

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

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.
Identifying chaotic dynamics in noisy time series through multimodal deep neural networks
Authors: Giuseppi Alessandro , Menegatti Danilo , Pietrabissa Antonio
Published: August 2024

Chaos detection is the problem of identifying whether a series of measurements is being sampled from an underlying set of chaotic dynamics. The unavoidable presence of measurement noise significantly affects the performance of chaos detectors, as discerning chaotic dynamics from stochastic signals becomes more challenging. This paper presents a computationally efficient multimodal deep neural network tailored for chaos detection by combining information coming from the analysis of time series, recurrence plots and spectrograms. The proposed approach is the first one suitable for multi-class classification of chaotic systems while being robust with respect to measurement noise, and is validated on a dataset of 15 different chaotic and non-chaotic dynamics subject to white, pink or brown colored noise.
Neural force functional for non-equilibrium many-body colloidal systems
Authors: Zimmermann Toni , Sammüller Florian , Hermann Sophie , Schmidt Matthias , de las Heras Daniel
Published: August 2024

We combine power functional theory and machine learning to study non-equilibrium overdamped many-body systems of colloidal particles at the level of one-body fields. We first sample in steady state the one-body fields relevant for the dynamics from computer simulations of Brownian particles under the influence of randomly generated external fields. A neural network is then trained with this data to represent locally in space the formally exact functional mapping from the one-body density and velocity profiles to the one-body internal force field. The trained network is used to analyse the non-equilibrium superadiabatic force field and the transport coefficients such as shear and bulk viscosities. Due to the local learning approach, the network can be applied to systems much larger than the original simulation box in which the one-body fields are sampled. Complemented with the exact non-equilibrium one-body force balance equation and a continuity equation, the network yields viable predictions of the dynamics in time-dependent situations. Even though training is based on steady states only, the predicted dynamics is in good agreement with simulation results. A neural dynamical density functional theory can be straightforwardly implemented as a limiting case in which the internal force field is that of an equilibrium system. The framework is general and directly applicable to other many-body systems of interacting particles following Brownian dynamics.
Regulating the development of accurate data-driven physics-informed deformation models
Authors: Newman Will , Ghaboussi Jamshid , Insana Michael
Published: August 2024

The challenge posed by the inverse problem associated with ultrasonic elasticity imaging is well matched to the capabilities of data-driven solutions. This report describes how data properties and the time sequence by which the data are introduced during training influence deformation-model accuracy and training times. Our goal is to image the elastic modulus of soft linear-elastic media as accurately as possible within a limited volume. To monitor progress during training, we introduce metrics describing convergence rate and stress entropy to guide data acquisition and other timing features. For example, a regularization term in the loss function may be introduced and later removed to speed and stabilize developing deformation models as well as establishing stopping rules for neural-network convergence. Images of a 14.4 cm3 volume within 3D software phantom visually indicate the quality of modulus images resulting over a range of training variables. The results show that a data-driven method constrained by the physics of a deformed solid will lead to quantitively accurate 3D elastic modulus images with minimum artifacts.
Learning on the correctness class for domain inverse problems of gravimetry
Authors: Chen Yihang , Li Wenbin
Published: August 2024

We consider end-to-end learning approaches for inverse problems of gravimetry. Due to ill-posedness of the inverse gravimetry, the reliability of learning approaches is questionable. To deal with this problem, we propose the strategy of learning on the correctness class. The well-posedness theorems are employed when designing the neural-network architecture and constructing the training set. Given the density-contrast function as a priori information, the domain of mass can be uniquely determined under certain constrains, and the domain inverse problem is a correctness class of the inverse gravimetry. Under this correctness class, we design the neural network for learning by mimicking the level-set formulation for the inverse gravimetry. Numerical examples illustrate that the method is able to recover mass models with non-constant density contrast.
Inhibition of Mac-1 allows human macrophages to migrate against the direction of shear flow on ICAM-1
Authors: Mittal Aman , Guin Subham , Mochida Ai , Hammer Daniel A. , Buffone Alexander
Published: August 2024

All immune cells must transit from the blood to distal sites such as the lymph nodes, bone marrow, or sites of infection. Blood borne monocytes traffic to the site of inflammation by adhering to the endothelial surface and migrating along endothelial intracellular adhesion molecule 1 (ICAM-1) by their ligand's macrophage 1 antigen (Mac-1) and lymphocyte functional antigen 1 (LFA-1) to transmigrate through the endothelium. Poor patient prognoses in chronic inflammation and tumors have been attributed to the hyper recruitment of certain types of macrophages. Therefore, targeting the binding of ICAM-1 to its respective ligands provides a novel approach to targeting the recruitment of macrophages. To that end, we determined whether the loss of Mac-1 expression could induce this upstream migration behavior by using blocking antibodies against Mac-1 to examine the effects of hydrodynamic flow on the migration of the human macrophage cell line U-937 on ICAM-1 surfaces. Blocking Mac-1 on U-937 cells led to upstream migration against the direction of shear flow on ICAM-1 surfaces. In sum, the ability of macrophages to migrate upstream when Mac-1 is blocked represents a new avenue to precisely control the differentiation, migration, and trafficking of macrophages.
Engineering empathy
Authors: Gerrans Philip
Published: August 2024

Human intelligence has a built-in locus of concern, the self, to drive and structure cognition and action. The creativity and adaptability (as well as characteristic biases and defects) of human thinking are products of an architecture that embeds emotional processing and self representation at the core of cognition. These themes are raised and addressed in different ways by work in artificial intelligence that connects artificial emotions, general intelligence and self awareness. Although much of that work is conducted in ‘classical’ AI paradigms the themes are equally to forms of generative AI that define the current landscape. The aim of this chapter is to show how these issues arise for AI and to suggest that current debates about AI, reasoning, consciousness, morality, and autonomy lack a key ingredient: the attention to the nature of the self for whom these activities are existentially vital. There are as yet no AI systems that co-ordinate systemic function based on a predictive model of the consequences of action for a self on whose behalf action is performed.
Critical Time Intervention in Criminal Legal Settings
Authors: Barrenger Stacey , Kriegel Liat S , Angell Beth
Published: August 2024

Many persons with mental illnesses cycle through the criminal legal system, accumulating the structural disadvantages linked to both their illnesses and their incarceration histories. After release from prison, persons with mental illnesses are at especially high risk of recidivism, homelessness, and other adverse outcomes and typically encounter substantial barriers to accessing the limited resources that exist to meet their complex needs. This chapter discusses the use of Critical Time Intervention (CTI) with individuals with mental illness re-entering the community from correctional institutions. Evidence suggests that CTI enhances continuity of support and access to post-release mental health services, but further research is needed to examine its impact on criminal recidivism, homelessness, and other outcomes. Implementation challenges unique to this transition are identified, including limited prison access for CTI workers, unpredictable release timing, and restrictive eligibility for potential community resources.
Critical Time Intervention With Persons Experiencing First-Episode Psychosis
Authors: Lee Rufina J , Nossel Ilana R
Published: August 2024

This chapter focuses on an adaptation of the Critical Time Intervention (CTI) model for young people who have been psychiatrically hospitalized for their first time with a diagnosis of primary psychotic disorder. It first provides a brief overview of the recent literature on specialized early interventions for first-episode psychosis (FEP). It highlights the importance of early identification and linkage to such services to reduce disability in young people who have been recently diagnosed. It then describes how the CTI model is utilized with this population, locating it within the context of a public mental health framework that targets early identification and linkage. Finally, it presents initial findings regarding implementation and considers the model’s potential to further inform services and treatments for the FEP population.
Critical Time Intervention in European Countries
Authors: Thorning Helle , Sandø Martin , Vet René de , Lako Danielle , Smith Alex , Wolf Judith
Published: August 2024

Growing interest in applying Critical Time Intervention (CTI) to address service delivery challenges related to homelessness and other social problems in Europe has led to significant research and implementation activity there. This chapter describes the implementation of CTI in three western European countries: the Netherlands, England, and Denmark. For each country, the authors provide the background and impetus for the decision to use CTI, followed by a brief history of its use and research conducted on the model. They then describe how the model has been adapted to the local context and target populations, addressing both adaptation and implementation processes as well as selected outcomes from both research and non-research settings. Challenges to successful model translation and implementation include a discussion of the impact of sociopolitical and cultural differences between these countries and the United States.
Model Diffusion and Current Developments
Authors: Herman Daniel B , Conover Sarah A , Susser Ezra S
Published: August 2024

This chapter discusses factors associated with the widespread diffusion of the Critical Time Intervention (CTI) model. These include early randomized trials conducted by the model developers, followed soon after by other investigators; growing emphasis by funders of homelessness services to apply empirically supported interventions; and the development of a training collaborative that could help support training and implementation. Other factors include the creation of tools that promote model fidelity and training material—both of which were made freely available—supported by a new Center for the Advancement of Critical Time Intervention at Hunter College in New York City to serve as a virtual clearinghouse for relevant information and expertise. As a result of these efforts, CTI is currently being widely deployed by the US Veterans Health Administration throughout its large homelessness services system. Several American states are also currently developing statewide programs.
Syrene Soundes
Authors: Chan Eleanor
Published: August 2024

False relations remain one of the great enigmas of English Renaissance musical culture. Contemporary theoretical treatises explicitly discouraged their use, yet these deliberate dissonances are hallmarks of English Renaissance music. Over the centuries they have accumulated a surfeit of subsequent connotations that have obscured how they once functioned, yet they have never been fully critically explored or elucidated in an English context. Syrene Soundes excavates beneath strata of accumulated meanings to uncover the way that false relations delighted and confounded their original listeners and performers. It presents a holistic investigation of the phenomenon, examining the cultural, literary, visual, and material understanding of such dissonances in relation to the broader culture of incongruity, surprise, and error and metaphors of harmony that captured the imagination of the English in the sixteenth and seventeenth centuries. Eleanor Chan argues that interdisciplinary angles can galvanize understanding of technical musical theoretical tropes like the false relation. She demonstrates that the false relation and its graphic ephemerality can productively be explored through the lens of English Renaissance visual culture and its idiosyncratic representational strategies, and as matters of textuality and reading. By anchoring it within the milieu of the English Reformation, burgeoning aspirations towards empire, and the increasing need for a self-fashioned collective English identity, she reveals that the false relation was key to the mythology of an inherited English tradition of music-making.
Introduction
Authors: Chan Eleanor
Published: August 2024

The false relation was a trope of carefully mannered dissonance that was much loved by composers, performers, and listeners in the English Renaissance. Nevertheless, it remained nameless until the nineteenth century. This chapter introduces readers to the multilayered histories of its reception, use, and meanings. Along the way, it reveals that false relations had an impact that percolated far outside the musical. Beyond their role as musical theoretical tropes, they can productively be considered issues that shed light on early modern reading and English Renaissance visual culture. They are of use to those seeking to nuance their understanding of the English Renaissance from textual, art historical, devotional, and educational angles. Alongside an exploration of the origins of the false relation in hexachord mutation, this chapter also explores the mythologization of the false relation by early twentieth-century composers and musicologists in their search for a definitively ‘English’ musical style.
Factors influencing older adult community fall prevention exercise implementation: a scoping review
Authors: Sibley Kathryn M , Tittlemier Brenda , Olarinde Faith , Leadbetter Brianna K , Bouchard Danielle R
Published: August 2024

Background

Exercise that challenges balance is the most effective fall prevention intervention in community-dwelling older adults. Identifying factors influencing implementation of community fall prevention exercise programs is a critical step in developing strategies to support program delivery.

Objective

To identify implementation facilitators, barriers, and details reported in peer-reviewed publications on community fall prevention exercise for older adults.

Design

Scoping review.

Methods

We searched multiple databases up to July 2023 for English-language publications that reported facilitators and/or barriers to implementing an evidence-based fall prevention exercise program in adults aged 50+ years living independently. At least two reviewers independently identified publications and extracted article, implementation, and exercise program characteristics and coded barriers and facilitators using the Consolidated Framework for Implementation Research (CFIR).

Results

We included 22 publications between 2001 and July 2023 that reported factors influencing implementation of 10 exercise programs. 293 factors were reported: 183 facilitators, 91 barriers, 6 described as both a facilitator and barrier, and 13 unspecified factors. Factors represented 33 CFIR constructs across all five CFIR domains: implementation inner setting (n = 95 factors); innovation (exercise program) characteristics (n = 84); individuals involved (n = 54); implementation process (n = 40) and outer setting (n = 20). Eight publications reported implementation strategies used; 6 reported using a conceptual framework; and 13 reported implementation outcomes.

Conclusion

The high number of factors reflects the complexity of fall prevention exercise implementation. The low reporting of implementation strategies, frameworks and outcomes highlight the ongoing need for work to implement and sustain community fall prevention exercise programs.