Diffusion Curve Research Articles

An Adaptive Fast-Multipole-Accelerated Hybrid Boundary Integral Equation Method for Accurate Diffusion Curves

In theory, diffusion curves promise complex color gradations for infinite-resolution vector graphics. In practice, existing realizations suffer from poor scaling, discretization artifacts, or insufficient support for rich boundary conditions. Previous applications of the boundary element method to diffusion curves have relied on polygonal approximations, which either forfeit the high-order smoothness of Bézier curves, or, when the polygonal approximation is extremely detailed, result in large and costly systems of equations that must be solved. In this paper, we utilize the boundary integral equation method to accurately and efficiently solve the underlying partial differential equation. Given a desired resolution and viewport, we then interpolate this solution and use the boundary element method to render it. We couple this hybrid approach with the fast multipole method on a non-uniform quadtree for efficient computation. Furthermore, we introduce an adaptive strategy to enable truly scalable infinite-resolution diffusion curves.

Discontinuity-Aware 2D Neural Fields

Neural image representations offer the possibility of high fidelity, compact storage, and resolution-independent accuracy, providing an attractive alternative to traditional pixel- and grid-based representations. However, coordinate neural networks fail to capture discontinuities present in the image and tend to blur across them; we aim to address this challenge. In many cases, such as rendered images, vector graphics, diffusion curves, or solutions to partial differential equations, the locations of the discontinuities are known. We take those locations as input, represented as linear, quadratic, or cubic Bézier curves, and construct a feature field that is discontinuous across these locations and smooth everywhere else. Finally, we use a shallow multi-layer perceptron to decode the features into the signal value. To construct the feature field, we develop a new data structure based on a curved triangular mesh, with features stored on the vertices and on a subset of the edges that are marked as discontinuous. We show that our method can be used to compress a 100, 000 2 -pixel rendered image into a 25MB file; can be used as a new diffusion-curve solver by combining with Monte-Carlo-based methods or directly supervised by the diffusion-curve energy; or can be used for compressing 2D physics simulation data.

Structure, photoluminescence, Judd–Ofelt analysis, and thermal stability studies of Ho3+/Yb3+ Co-doped YNbO4 green phosphors

A series of YNbO4:Ho3+/Yb3+ phosphors with varying concentrations of Yb3+ were synthesized using a high-temperature solid-state method. To ascertain the presence, structure, and purity of YNbO4:Ho3+/Yb3+, the crystal structure of the luminescent powder was investigated through x-ray diffraction. Trivalent rare earth ion-doped transparent luminescent materials possess distinctive f–f transition properties, which can be comprehensively studied using the Judd–Ofelt theory. However, when dealing with opaque powder materials, traditional Judd–Ofelt (J–O) theory becomes challenging to apply due to the unavailability of absorption spectra measurements. In this paper, to analyze the properties of Ho3+ in the YNbO4 phosphors, diffuse diffraction spectra and fluorescence decay curves were employed within the framework of the J–O theory. Furthermore, the J–O parameters Ωλ (λ = 2, 4, 6) of Ho3+ in YNbO4 phosphors were calculated and subsequently modified for accuracy and precision.

Hierarchical vectorization for facial images

The explosive growth of social media means portrait editing and retouching are in high demand. While portraits are commonly captured and stored as raster images, editing raster images is non-trivial and requires the user to be highly skilled. Aiming at developing intuitive and easy-to-use portrait editing tools, we propose a novel vectorization method that can automatically convert raster images into a 3-tier hierarchical representation. The base layer consists of a set of sparse diffusion curves (DCs) which characterize salient geometric features and low-frequency colors, providing a means for semantic color transfer and facial expression editing. The middle level encodes specular highlights and shadows as large, editable Poisson regions (PRs) and allows the user to directly adjust illumination by tuning the strength and changing the shapes of PRs. The top level contains two types of pixel-sized PRs for high-frequency residuals and fine details such as pimples and pigmentation. We train a deep generative model that can produce high-frequency residuals automatically. Thanks to the inherent meaning in vector primitives, editing portraits becomes easy and intuitive. In particular, our method supports color transfer, facial expression editing, highlight and shadow editing, and automatic retouching. To quantitatively evaluate the results, we extend the commonly used FLIP metric (which measures color and feature differences between two images) to consider illumination. The new metric, illumination-sensitive FLIP, can effectively capture salient changes in color transfer results, and is more consistent with human perception than FLIP and other quality measures for portrait images. We evaluate our method on the FFHQR dataset and show it to be effective for common portrait editing tasks, such as retouching, light editing, color transfer, and expression editing.

Investigating the diffusion of innovation: A comprehensive study of successive diffusion processes through analysis of search trends, patent records, and academic publications

Adapting to market advances gives organizations a competitive advantage in today's business environment. The anticipation of innovation can be crucial to the competitiveness of an industry. This study analyzes and compares different types of innovation diffusion processes. From a global perspective, it examines the diffusion of interest in an innovation as indicated by a search trend, the diffusion of innovation in patents, and the diffusion of innovation in academic publications. It applies the mathematical modeling of Bass to evaluate the diffusion curves. The findings suggest that the takeoff times in the curves are distinct and sequential, indicating the existence of a series of successive diffusion processes. The diffusion curves of innovation-related search interest reach the takeoff first, followed by the diffusion curves of innovation-related patents and academic publications. The present work contributes to the field by (i) extending the use of innovation diffusion theory to the diffusion of interest in an innovation as indicated by a search trend, the diffusion of innovation in patents, and the diffusion of innovation in academic publications; (ii) demonstrating the differences in diffusion curves and takeoff times; and (iii) indicating the existence of a series of successive diffusion processes.

A study of the UV photodetectors properties based on the effect of TiO2 on ZnO nanorods grown via the chemical bath deposition method on p-type Si (100) substrates

In this study, a heating composition process was used to construct Ti-doped ZnO (Ti/ZnO) nanocomposites to serve as an active layer in UV detection devices. Prior to synthesis, a ZnO seed layer was deposited on p-type Si (100) substrates (p-Si substrates) using radio frequency (RF) sputtering. Afterward, Ti/ZnO thin films (TFs) were successfully grown using the chemical bath deposition method (CBD) by combining different amounts of TiO2 nanofluid with ZnO precursor solutions. The volume of the TiO2 nanofluid ranged from 12.5% to 50% with respect to the total volume. FE-SEM, EDX, and AFM were employed to examine the morphological, elemental, and topographical characteristics of the formed nanocomposites. Thermo-evaporation was used to print Ag interdigitated electrodes (IDE) on the nanocomposites to fabricate UV photodetectors. The (002) Ti/ZnO peak along the z-axis on the substrate, which increases with increasing amounts of TiO2, provides evidence of the crystallinity and the hexagonal wurtzite structure of the samples. UV–Vis spectroscopy showed high diffuse reflectance curves with indirect energy bandgaps from 3.1969 eV to 3.2203 eV. The TiO2 nanofluid used in the growth impacted the synthesized Ti/ZnO, which affected the performance of the photodetectors. A responsivity, photosensitivity, gain, detectivity, rise time, and decay time of 0.107495 AW−1, 3833.67%, 39.34, 6.75 × 108 jones, 0.19 s, and 0.24 s were recorded when 50% of the TiO2 was used during the synthesis process of the ZnO nanorods. Based on the findings, the nanocomposites grown on p-Si substrates are suitable for use as thin films in optoelectronics, especially UV detection devices.

Simulation Study on the Mechanism of Intermediate Subdiffusion of Diffusive Particles in Crowded Systems.

The intermediate subdiffusion of diffusive particles in crowded systems is studied for two model systems: the continuous time random walk (CTRW) model and the obstruction-binding model. For the CTRW model with an arbitrarily given longest waiting time τmax, we find that the diffusive particle exhibits subdiffusion below τmax and recovers normal diffusion above τmax. For the obstruction-binding model with randomly distributed attractive obstacles, the diffusion of the diffusive particle is dependent on the binding energy and the density of obstacles. Interestingly, diffusion curves for different binding strengths can be overlapped by rescaling the simulation time, indicating that the diffusive particle in the obstruction-binding model can change from the intermediate subdiffusion to the normal diffusion at a long-term simulation scale. The results of the two model systems show that the diffusive particles only exhibit intermediate subdiffusion below the longest waiting time. Therefore, long timescale subdiffusion would only be observed in the CTRW model with an infinitely long waiting time and in the obstruction-binding model with an infinitely large binding strength.

Pacemod: parametric contour-based modifications for glyph generation

Metaphoric glyphs are intuitively related to the underlying problem domain and enhance the readability and learnability of the resulting visualization. Their construction, however, implies an appropriate modification of the base icon, which is a predominantly manual process. In this paper, we introduce the parametric contour-based modification (PACEMOD) approach that lays the foundations of automated, controllable icon manipulations. Technically, the PACEMOD parametric representation utilizes diffusion curves, enriched with new degrees of freedom in arc-length parameterization, which allows for manipulation of the icon contours’ geometry and the related color attributes. Moreover, we propose an implementation of our generic approach for a specific, automated design of metaphoric glyphs, based on periodic, wave-like contour modifications. Finally, the practicality of such periodic contour modifications is demonstrated by two visualization examples, which comprise uncertainty visualization of a rain forecast and gradient glyphs applied to COVID-19 data. In summary, with the PACEMOD approach we introduce an instrument that facilitates a user-centered design of metaphoric glyphs and provides a generic basis for potential further implementations according to specific applications.

SERS study of classical and newly β-lactams-metal complexation based on in situ laser-induced coral reefs-like silver photomicroclusters: In vitro study of antibacterial activity

The influence of metal complexation of two polar β-lactam antibiotics was investigated using surface enhanced Raman spectroscopy (SERS) technique. SERS method was applied to track the structural changes and the degradation behaviour of the studied compounds upon Zinc (II) ions-complexation. In situ laser-induced coral reefs-like photomicroclusters have been utilized as a SERS platform. The produced coral reefs-like photomicroclusters were characterized using scanning electron microscopy (SEM) and transmission electron microscope (TEM). The antibacterial efficiency of the antibiotics was investigated and compared before and after metal complexation using two techniques; agar well diffusion and growth curve. To provide a detailed elucidation of the complexation reaction, mass fragmentation of metal- antibiotics complexes was investigated using liquid chromatography/mass spectrometric (LC/MS) technique. It was found that metal complexation of classical β-lactam antibiotic (Ticarcillin) promoted the rate of its degradation, leading to a decrease of the antibacterial efficiency. On the other side, the antibacterial activity of the newly developed β-lactam (Faropenem) has been greatly enhanced via metal-complexation reaction.

IFRS diffusion and earnings quality: Moderating role of firm size and IFRS specialists

AbstractThe study investigates the learning curve impact of International Financial Reporting Standards (IFRS) adoption on earnings quality. In other words, the study investigates how IFRS affects the quality of earnings over time. The study also examines the moderating role of firm size and IFRS specialists/experts on the issue. We use firm‐level data of Bombay Stock Exchange listed firms and analyze it through the “Difference in Difference” technique. We find that the association between IFRS and earnings quality changes favorably as the introduction grows longer. In particular, we find that earnings quality is lower during the initial years of IFRS adoption, however, it improves in the later years of adoption, consistent with the notion of diffusion and learning curve theory. Our subsequent tests suggest that small firms are taking more time to percolate through the IFRS system, however, it does not hold when small firms hire IFRS specialists/experts to migrate their older accounts to IFRS, consistent with the notion of resource dependency theory of innovation. Overall, IFRS is found to be a continuous improvement process in emerging nations whose benefit can be derived in the initial years by providing sufficient infrastructural and institutional support, particularly to small firms. Our findings are robust to the problem of endogeneity and offer real‐world ramifications for regulatory authorities, accounting‐standard setters, and investors.

The effect of different aqueous solutions ratios of Ocimum basilicum utilized in AgNPs synthesis on the inhibition of bacterial growth

This study examined the effect of varying concentrations of Ocimum basilicum aqueous extract, which was done via the green synthesis of Silver nanoparticles (AgNPs), on the identification of the most effective concentration for bacteria inhibitory activity. Different concentrations of the aqueous Ocimum basilicum extract (0.25, 0.50, 0.75 and 1.00 mM) were used as reducing and stabilizing agent to synthesize AgNPs by means of the reduction method. The crystal structure and morphology of the NPs were characterized UV–Vis spectra, X-ray diffraction (XRD) and scanning electron microscopy (SEM). The antibacterial efficacy of AgNPs was studied against E. coli ATCC 35218 using well diffusion, MIC, MBC, and time-kill curve. The dark yellow color of the Ocimum basilicum aqueous solution indicates the successful synthesis process of the AgNPs. UV-spectra of the AgNPs display a gradual increase of absorption in sequence with concentration increase of aqueous Ocimum basilicum extract solution from 0.25 to 1.00 mM. This, in turn, led to a shift in the wavelength from 488 to 497 nm, along with a change in the nanoparticle size from 52 to 8 nm. The tests also showed a high activity of the particles against bacteria (E. coli), ranging between 15.6 and 62.5 µg/ml. Based on AgNPs, it was confirmed that an aqueous Ocimum basilicum extract can be used as an effective, reducing and stabilizing agent for the synthesis of different sizes of AgNPs based on the solvent concentration. The AgNPs also proved to be effective in inhibiting and killing bacteria.

Experimental Assessment of Combined Contribution of Coal Mass Diffusion and Seepage to Methane Migration under In Situ Stress Conditions

Gas diffusion and seepage characteristics can reflect the transport capacity of coal mass pores and fracture channels and are also important parameters to determine the mechanism of methane migration. Currently, research on the contribution of diffusion and seepage to methane migration is carried out mainly by fixing one parameter and discussing the effect of the other. However, few studies are focused on the dynamic combined effect of diffusion and seepage on methane migration under in situ stress conditions, which is mainly attributed to difficulties in obtaining the parameters of diffusion characteristics of coal mass under stress. This paper presents a study on the dynamic combined effect of coal mass diffusion and seepage on methane migration under different in situ stress conditions. First, the physical structure model of coal mass under high and low stresses was constructed based on the variations of coal mass porosity, permeability, and computed tomography under different stresses. Meanwhile, based on the methane desorption and diffusion curves of coal mass under different stresses, the diffusion parameters of coal mass were obtained according to the corresponding diffusion model. Furthermore, the coupled multifield model was used to calculate the methane migration in coal mass during the process of borehole extraction with different stresses. The results suggest that the difficulty in methane extraction is directly proportional to the stress, but a huge discrepancy exists in methane migration under high and low stresses principally due to the change of dominant factors in methane migration of coal mass under different stresses. Additionally, given the essential factors controlling the overall methane migration of coal mass under high and low stresses, a joint technical method for promoting methane flow rate under multicoal seam conditions in the same mining area was proposed, i.e., hydraulic fracturing and sand injection (increasing permeability) in coal seams under low stress and simultaneous application of hydraulic fracturing and sand injection and multistage cavitation pressure relief (increasing diffusion) in coal seams under high stress. The research results obtained in this paper provide theoretical support for understanding the methane migration mechanism in coal seams under in situ stress and stimulated gas production.

Phenomenon and mechanism of ion diffusion at Co-fired joint interface of NiZn Ferrite/MCT ceramic heterostructure

Nickel-zinc ferrite (NZF) discs and magnesium calcium titanite (MCT) dielectric ceramic discs were laminated and co-fired to prepare NZF/MCT bi-layer laminated heterostructure samples. The sintering shrinkage of NZF and MCT was matched well via adjusting the sintering temperature. Ion diffusion curves at the co-fired joint interface were derived by electron-probe microanalysis (EPMA) and fitted basing on infinite diffusion coupling model. Ion diffusion mechanism at the co-fired joint interface was investigated in detail. The results show that Zn2+ ions have the highest diffusion coefficient because of the smaller ion radius which is benefit for interstitial diffusion, while Ca2+ ions have the lowest diffusion coefficient and segregate along the interface of MCT side. Fe3+ ions have the biggest diffusion width because of the interstitial diffusion in the incompletely densified MCT. Sintered at 1260 °C, the maximum interdiffusion width is narrow to be about 102 μm. With increasing temperature, ion diffusion coefficient and diffusion distance both go up obviously.

Pistachio nut diffusion in Spain: Growth models

Aim of study: To analyse the diffusion of the crop by producing forecast models, that intend to help farmers in their decision-making. Area of study: Spain. The area dedicated to pistachio cultivation in Spain has multiplied by 36 in the period 2010 to 2020, reaching 44,244 ha. Material and methods: This study brings together data on the evolution of pistachio cultivation based on the following parameters: cultivated area, yield, and price. Methods are based on internal, external influence models and on an influence-price-crop yield pattern. Main results: The results indicate that given a constant crop yield, raising pistachio prices, will bring a production increase that generate the saturation level of the system. Similarly, with a constant pistachio price and an increment of the crop yield, the saturation level of the system increases. Regarding the pattern of influence, it is shown that in a context of suitable market prices for pistachio and an optimal synergy of the production factors that favour the crop yield not only increases the level of saturation of the system but also the duration of the diffusion process. Research highlights: The diffusion curve is sigmoidal with a well-defined inflection point and three well-defined phases. The adoption of pistachio in Spain responds to a model of internal influence (logistic) and never to models of external influence. According to the results, the process has a zero-innovation effect, while the dynamics of the process is completely determined by an imitation effect.

Characterization and Growth of TiO2/ZnO on PTFE Substrates at Different Volumetric Ratios Using Chemical Bath Deposition

Developing non-toxic, semiconductor-doped heterojunction materials for optoelectronic applications on the surface of a flexible substrate is a viable strategy for meeting the world’s energy needs without introducing any environmental issues. In this paper, Ti:TiO2/ZnO nanocomposites were prepared by heat treatment and utilized as an active layer in UV photodetectors. First, a ZnO seed layer was deposited by radio frequency (RF) sputtering on polytetrafluoroethylene (PTFE) substrates. Then, TiO2/ZnO thin films (TFs) were successfully grown by combining volumetric mixtures of TiO2 and ZnO at the ratios of 1:7, 1:3, 3:5, and 1:1 via the chemical bath deposition (CBD) method. The morphological, elemental, and topographical analyses of the grown TFs were investigated through SESEM, EDX, and AFM spectroscopy, respectively. XRD patterns illustrated the presence of the unified (002) peak of the Ti/ZnO hexagonal wurtzite structure in all prepared samples, with intensities indicating a very strong preferential crystallinity with increasing TiO2 ratios. Enhanced diffuse reflectance curves were obtained by UV–Vis spectroscopy, with allowed indirect energy bandgaps ranging from 3.17 eV to 3.23 eV. FTIR characterization revealed wider phonon vibration ranges indicating the presence of Ti–O and Zn–O bonds. Metal–semiconductor–metal (MSM) UV photodetectors were fabricated by thermally evaporating Ag electrodes on the grown nanocomposites. The volumetric ratio of TiO2/ZnO impacted the photodetector performance, where the responsivity, photosensitivity, gain, detectivity, rise time, and decay time of 0.495 AW−1, 247.14%, 3.47, 3.68 × 108 jones, 0.63 s, and 0.99 s, respectively, were recorded at a ratio of 1:1 (TiO2:ZnO). Based on the results, the heterostructure nanocomposites grown on PTFE substrates are believed to be highly promising TF for flexible electronics.

Pre‐Eruptive Evolution and Timescales of Silicic Volcanism in the Tarim Large Igneous Province

AbstractThe widespread (∼48,000 km2) coexisting crystal‐rich monotonous intermediates (trachydacites) and crystal‐poor high‐silica rocks (rhyolites, ignimbrites) in the Tarim Large Igneous Province (T‐LIP) offer a rare opportunity to explore pre‐eruptive timescales and evolutionary patterns of different silicic magmas in LIPs. We combined information from petrography, geochemistry, crystal kinetics, and diffusion chronometry of felsic rocks from nine boreholes to investigate their pre‐eruptive history. The complex reverse Ti zoning patterns in quartz corresponding to temperature variations over 100°C, lognormal crystal size distributions, high‐Ti rims with faster growth rates, variable pressures, and Nd isotopic modeling, indicate silicic magmas in T‐LIP might have witnessed a universal evolutionary pattern of periodic recharge and rejuvenation, coarsening, and cooling and crystallization in magma chambers at different crustal levels, which we term here as RRCCC model. The uniform composition and crystallinity of trachydacites might result from latent heat buffering, and their eruption was triggered by melt‐induced fracturing. The chemical and thermal gradients preserved in rhyolites were generated by thermally variable magma inputs and extensive fractionation at different pressures. Co‐existence of these two types of rocks was controlled by interaction between mantle plume impingement and re‐melting of hydrous crust built up by early subduction. The best‐fitting Ti‐in‐quartz diffusion curves obtained by Neural Networks indicate that magma residence time lasted for ∼106 years, and eruption was triggered within ∼105 years after the last recharge event.

Diffusion of Cement Kiln Co-Processing of Contaminated Soil in Selected Provinces of China: Engineering Practices, Modeling, and Driving Factors

Promoting the diffusion of remediation technologies is an attractive solution to environmental protection and urban sustainability challenges. To better understand technology diffusion, we reviewed the engineering practices of cement kiln co-processing (CKC) of contaminated soil and obtained diffusion parameters using the Bass model in three provinces of China. Our results show that CKC has been adopted for the disposal of multiple contaminants and that the optimal feed rate of contaminated soil is 4–5%. The obtained diffusion parameters can be used to analyze and predict CKC diffusion. Driving factors analysis suggest that CKC diffusion is regulation-driven and obeys the S-curve pattern. Policies at the national level shape the basic pattern of the diffusion curve, while local policies, market scales, and contaminant types produce variations in diffusion rates across provinces. Results also reveal that the co-processing quota management on contaminated soil has little impact on CKC adoption. This study provides insights into contaminated soil remediation technology diffusion and the effectiveness of environmental policy implementation at home and abroad.

Analyzing the co-evolutionary dynamics of consumers’ attitudes and green energy technologies based on a triple-helix model

Technology diffusion is a macroscopic manifestation of individuals' adoption behaviors. The driving force of green energy technology (GET) diffusion lies in the mindsets of individual potential adopters, which are indispensable for GET progress, consumers' attitude evolution, and the diffusion itself. To explore how the three intertwined processes interact to drive GET diffusion, this paper builds a novel agent-based triple-helix model that considers consumers' prior attitudes, peer influences via a social network, consumers' repeat purchases, technology progress, and diffusion. Based on the model calibrated with the data from the automobile industry, three numerical experiments are conducted. Our findings are trifold: (1) One unit improvement in consumers' green attitudes leads to 0.62 unit increase in GET adoption ratio on average, but the effect is subject to marginal diminishing effect; (2) Initial technology maturity and improvement rate are both positive to GET diffusion and display a complementary effect; (3) The model can not only reproduce typical S-shaped innovation diffusion curves but also output results consistent with all critical trends of the Gartner Hype Cycle. Further exploration suggests that the populations' positive initial attitudes, non-adopters interactivity, low initial technology maturity, and consumers’ repeat purchases are crucial factors driving the formation of the Gartner Hype Cycle.

Contactless medical equipment AI big data risk control and quasi thinking iterative planning

Research Background, the intelligent polymorphic system of heavy core clustering fitting iterative programming is constructed by using the edge lens of dual core heavy core. The tracking system of heavy core TANH equilibrium array is used to obtain the abnormal data range. The energy regular fluctuation of the edge lens with dual core and heavy core is used to obtain high-definition images. And build the complexity dependent parameter group from low-end equipment to high-end equipment. Heavy core clustering of hierarchical fuzzy clustering system based on differential incremental balance theory is applied to Contactless medical equipment AI big data risk control and quasi thinking iterative planning. At the same time, the mathematical model risk control is performed by fitting the TANH balance of the local nonlinear random regular micro-vibration diffusion curve. The CT/MR original data is subjected to hierarchical cross domain overlapping grid screening with the structure of fitting weakly nonlinear curve, which can capture the heavy core cluster analysis of the core layer of big data anomalies [1:10]. Successfully control the parameter group of CT/MR machine internal data, big data AI Mathematical model risk. The polar graph of high-dimensional heavy core clustering processing data is regular and scientific. The same time, it can prevent the dimension disaster caused by the construction of high-dimensional big data due to the partial loss of original data, and form a stable and predictable maintenance of CT/MR. Compared with the discrete characteristics of the polar graph of the original data. So as to correctly detect and control the dynamic change process of CT/MR in the entire life cycle. It provides help for predictive maintenance of early pre-inspection and orderly maintenance of the medical system, and developed standardized model software of automated unsupervised learning for medical big equipment big data AI Mathematical model risk control. Scientifically evaluated the exposure time and heat capacity MHU% of CT tubes, as well as the internal law of MR (nuclear magnetic resonance), and processed big data twice and three times in heavy nuclear clustering. After optimizing the algorithm, hundreds of thousands of nonlinear random vibrations are performed in the operation and maintenance database every second, and at least 30 concurrent operations are formed, which greatly improves and shortens the operation time (Yanwei et al. in J Complex 2017:1–9, 2017. https://doi.org/10.1155/2017/3437854). Finally, after adding micro-vibration quasi thinking iterative planning for the uncertain structure of AI operation, we can successfully obtain the scientific and correct results required by high-dimensional information and analyze images. This kind of AI big data risk control improves the intelligent management ability of medical institutions. Cross platform embedded web system for predictable maintenance of AI big data is established (Qi et al. in J IEEE Trans Ind Inf 99:1, 2020. https://doi.org/10.1109/tii.2020.3012157).

Technological progress observed for fixed-bottom offshore wind in the EU and UK

Offshore wind is a rapidly maturing low-carbon energy technology, for which the technology cost has increased before starting to decline. In literature, the cost development trends of offshore wind and factors responsible were poorly studied. Understanding the factors contributing to the cost developments and their individual impacts are vital for long-term energy policy actions and investment decisions. Therefore, this study combined three different but highly complementary quantitative methodologies to analyze the technological progress observed for fixed-bottom offshore wind in the EU and UK. The technology diffusion curve was first applied to identify the individual development phases of offshore wind technology. Then, the cost developments observed across the identified phases were quantified using experience curve and bottom-up cost modeling methodologies. In the formative phase of the development process, the offshore wind farm's specific capital expenditure had increased from 2 M€/MW in 2000 to 5 M€/MW in 2010, thereby resulting in negative LR. The increase in specific capital expenditure increased the Levelized Cost of Energy (LCoE) from ~110 €/MWh to above 150 €/MWh. After that, during the upscaling and growth phase, the specific capital expenditure declined from 5.4 M€/MW in 2011 to 3.3 M€/MW in 2020. LR of 8–11 % was observed for specific capital expenditure in this phase. In the same phase, the LCoE declined more rapidly than the specific capital expenditure, i.e., from roughly 150 €/MWh in 2011 to 69 €/MWh in 2020, a 54 % decline. This rapid decline observed in recent years was due to the favorable financing conditions, increased capacity factor, and decreased technology costs, including investment and operational costs. Based on the technological progress assessed for offshore wind and its contributing factors in this study, we also estimated the near-term offshore wind LCoE, 55 €/MWh in 2021–2023 and 48 €/MWh in 2024–2026, which aligns well with recent auction outcomes.