Density Distribution Research Articles

The Quantitative InVivo Assessment of Diabetic andNon-Diabetic Skin Wound Healing Using Phasor-FLIM Approach.

A quantitative assessment of wound status in a murine model was developed using phasor plot presentation of fluorescence lifetime imaging microscopy (FLIM) data. The quantitative assessment is based on calculating Bhattacharyya distance between g coordinates of FLIM data phasor plot density distributions of wound and healthy skin. The approach was validated for both diabetic and non-diabetic mice wounds, including during low-dose photodynamic therapy (LDPDT). Analysis revealed a shift in the FLIM data phasor plot g coordinates, suggesting altered metabolic processes involved in wound healing. Bhattacharyya distances in the LDPDT groups were closer to zero compared to the control group, which was not treated by LDPDT. Bhattacharyya distances in the non-diabetic LDPDT groups were closer to zero compared to the diabetic LDPDT groups that is consistent with the literature regarding the positive role of LDPDT in accelerating wound healing and the role of diabetes mellitus in impairing wound healing.

Predictors of debulking devices use by coronary artery calcification classification using cardiac CT

Abstract Background Multi-slice computed tomography (CT) is useful for the non-invasive detection and quantification of coronary calcium burden. However, a definitive parameter based on CT imaging for predicting the need for debulking devices, such as rotational atherectomy (RA) and orbital atherectomy (OAS), has yet to be established. Purpose We aimed to develop a calcification risk score using cardiac CT to predict the use of debulking devices during coronary intervention. Methods In this retrospective, multi-center, observational study in Japan, patients who underwent percutaneous coronary intervention (PCI) were screened. Inclusion criteria were (1) cardiac CT within 6 months prior to PCI, (2) moderate or severe coronary calcification detected on CT, and (3) de novo lesions in a native coronary artery. A total of 349 lesions in 343 patients from 13 hospitals, there were 124 lesions that were treated with debulking devices and 225 lesions without debulking devices. Cardiac CT images were analyzed for circumferential degree, longitudinal length, intracoronary distribution of calcification, peak density, mean density, vessel bend and presence of bifurcation lesions. Data from a random two-thirds (the derivation cohort, n=247) were used to develop a new risk prediction model, which was then tested in the remaining one-third (the validation cohort, n=102). Odds ratios and the risk prediction model were developed using a logistic regression model. The predicted risk was translated into a simplified scoring system (Figure 1A). Results RA was used in 111 lesions and OAS in 23 lesions. Factors selected in the model included circumferential degree (≥270°: 3 points), bifurcation (2 points), longitudinal length (≥8mm: 1 point), and mean density (≥600 HU: 1 point) (Figure 1B). This model demonstrated good discrimination (c-statistic=0.772) and calibration (Hosmer–Lemeshow χ²-statistic=0.885; P=0.64) in the validation cohort (Figure 2). Conclusions The new prediction model based on cardiac CT performed well in predicting the need for debulking devices. This could serve as an objective tool to support a clinical decision to use debulking devices even in cases where intravascular imaging is not applicable.Study flow and Cardiac CT calcium scoreDiscrimination and Calibration

A Short-term PV Power Prediction and Uncertainty Analysis Model Based on CEEMDAN and AHA-BP

The stochastic and intermittent nature of photovoltaic (PV) generation brings a series of scheduling problems to the power system. An effective prediction of PV power is essential to minimize the impact of uncertainty. Therefore, this paper presents an integrated prediction model with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), the artificial hummingbird algorithm (AHA), and the BP neural network (BPNN) for predicting power generation from PV power plants, and a methodology for uncertainty analysis by using the nonparametric kernel density estimation (NPKDE). First, one month of PV power is decomposed into an array of components using CEEMDAN. Then, the weights and thresholds of the BPNN are optimized by using AHA. These components are trained using the BPNN. Finally, the final prediction results are obtained by superimposing the components, and NPKDE is employed to compute the probability density and confidence interval of the prediction error. The proposed prediction method demonstrates superior predictive performance in comparison with other models. Also, the NPKDE approach better describes the accuracy of the probability density distribution.

Evolution of Tidal Disruption Event Disks with Magnetically Driven Winds

We present a time-dependent, one-dimensional, magnetically driven disk wind model based on magnetohydrodynamic (MHD) equations, in the context of tidal disruption events (TDEs). We assume that the disk is geometrically thin, is gas pressure dominated, and explicitly accounts for magnetic braking and turbulent viscosity through an extended α-viscosity prescription. We find a particular wind solution for a set of basic equations that satisfies the necessary and sufficient conditions for vertically unbound MHD flows. The solution shows that the disk evolves with mass loss due to wind and accretion from the initial Gaussian density distribution. We confirm that the mass accretion rate follows the power law of time t −19/16 at late times in the absence of wind, which matches the classical solution of J. K. Cannizzo et al. We find that the mass accretion rate is steeper than the t −19/16 curve when the wind is present. Mass accretion is also induced by magnetic braking, known as the wind-driven accretion mechanism, which results in a faster decay with time of both the mass accretion and mass-loss rates. In the disk emission, the ultraviolet (UV) luminosity is the highest among the optical, UV, and X-ray luminosities. While the optical and X-ray emission is observationally insignificant without magnetic braking, the X-ray emission is brighter at late times, especially in the presence of magnetic braking. This provides a possible explanation for observed delayed X-ray flares. Our model predicts that late-time bolometric light curves steeper than t −19/16 in UV-bright TDEs are potentially compelling indicators of magnetically driven winds.

Characteristics of the Spatial Structure of Traditional Villages in the Xinjiang Uygur Autonomous Region in China and Their Influence Mechanisms

Traditional villages are one of the basic types of rural revitalisation and one of the important carriers of cultural inheritance. This research is based on the data of 53 traditional villages in the Xinjiang Uyghur Autonomous Region with the aid of the ArcGIS10.8.1 spatial analysis platform. The study identifies the spatial evolution characteristics from the spatial distribution type, distribution direction, distribution density, distribution balance, etc., and explores their influence mechanisms. The study shows that 1. the spatial structure of traditional villages in Xinjiang was analysed as a cohesive structure type by using the nearest neighbour index method, which shows the evolutionary characteristics of the agglomerative tendency to increase gradually. Among them, Changji Hui Autonomous Prefecture and Turpan City have the highest degree of concentration. 2. The establishment and development of traditional villages in Xinjiang is mainly influenced by natural factors such as geographical features and hydrography. Social factors such as population distribution, transport conditions and economic progression have very important implications for the preservation and continuation of traditional villages.

A comprehensive study of nonuniformity properties of the LiCoO2 thin-film cathode fabricated by RF sputtering

The influence of nonuniformity properties of the LiCoO2 cathode film deposited by magnetron sputtering on the capacity of all-solid-state thin-film lithium-ion batteries (ASSLib) was studied. It was found that the film nonuniformity corresponds to the magnetron plasma density distribution and the angular distribution of sputtered particles. The capacity distribution of the ASSLib with LiCoO2 cathode depending on the distance to the substrate center was studied. The maximum capacity corresponded to the dense part of the toroidal region of the magnetron plasma. It was determined that the main causes of batteries capacity decline in the central part and on the edge of the substrate are the impurity phase of lithium cobaltate and the smaller thickness of the cathode layer, respectively.

The frequency spectrum and energy content in a pulse flux of terahertz radiation generated by a relativistic electron beam in a plasma column with different density distributions

This paper reports on the generation of a directed flux of electromagnetic radiation with an energy content of 10 J in the frequency range of 0.2—0.3 THz at a microsecond pulse duration in a beam-plasma system. The flux is generated when a relativistic electron beam (REB) pumps electron plasma waves in a magnetized plasma column. In the described experiments, this fundamentally new approach to generate terahertz radiation was carried out at the GOL-PET facility in the conditions of varying the beam current density and the plasma density in the appropriate ranges of 1—2 kA/cm2 and 1014—1015 cm−3. From the comparison of the flux energy spectrum measured experimentally in the frequency range 0.15—0.45 THz with the calculated one obtained using the previously proposed model of radiation generation in a beam–plasma system, it was shown that this process occurs through resonant pumping by REB of precisely the branch of upper-hybrid plasma waves. Mastering this new method to generate terahertz radiation opens the prospect of its use to obtain multi-megawatt radiation fluxes in the frequency range up to 1 terahertz and higher. For such a development approach the most promising beam for pumping plasma oscillations seems to be a kiloampere REB generated in a linear induction accelerator.

Distinctive features of measuring <I>T<SUB>e</SUB></I> and <I>n<SUB>e</SUB></I> spatial distributions in the Globus-M2 spherical tokamak using method of Thomson scattering of laser radiation

The results of measuring the electron temperature and density spatial distributions in plasma of the Globus-M2 tokamak using the Thomson scattering diagnostics are presented. The diagnostics provides measurements throughout the entire tokamak discharge, starting from time of gas breakdown. The Thomson scattering data were analyzed in order to determine the positions of the last closed flux surface, the plasma magnetic axis, and the radius of inversion during the saw-tooth oscillations. The results of measurements performed during the internal reconnection of magnetic field lines are presents, as well as the dynamics of spatial distributions of electron temperature, density and pressure during the plasma transition to the H-mode. The results of measuring the electron temperature distribution in the scrape-off layer using the Thomson scattering diagnostics are also presented for distances up to 4 cm outside the last closed flux surface.

Visualization of Aerial Droplet Distribution for Unmanned Aerial Spray Systems Based on Laser Imaging

Unmanned aerial spray systems (UASSs) are a commonly used spraying method for plant protection operations. However, their spraying parameters have complex effects on droplet distribution. The large-scale 3D droplet density distribution measurement method is insufficient, especially since the downwash wind is easily affected by the environment. Therefore, there is a need to develop a technique that can quickly visualize 3D droplet distribution. In this study, a laser imaging method was proposed to quickly scan moving droplets in the air, and a test method that can visualize 3D droplet distribution was constructed by using the traveling mode of the machine perpendicular to the scanning plane. The 3D droplet distribution of targeted and conventional UAVs was tested, and the methods for signal processing, noise reduction, and point cloud rebuilding for laser imaging were developed. Compared with the simulation results, laser imaging showed the pattern of droplet distribution from the two UAV structures well. The results showed that the laser imaging based method for detecting 3D droplet distribution is feasible, fast, and environmentally friendly.

Construction of Asymmetric Filler Density Mixed-Matrix MOF Membranes via Liquid-Phase Ion Activation for Advanced Oxidation Processes.

Mixed-matrix membrane (MMM) reactors incorporating metal-organic framework (MOF) fillers have significant applications in fields such as separation, catalysis, and chiral resolution. However, the traditional method of directly mixing MOF fillers with polymers results in a symmetric structure where most of the MOF fillers are uniformly distributed across the membrane reactor's cross-section. During usage, this leads to a pronounced trade-off between flux and efficiency and the waste of the MOF, as the rich pore characteristics and active sites of MOFs are not fully utilized. In this study, we propose a liquid-phase ion activation strategy that enables the in situ formation of a uniform and densely packed MOF shell layer on the membrane surface during the phase inversion process of MMM fabrication. This asymmetric density distribution of the MOF-based MMM reactor selectively induces the generation of considerable nonradical 1O2 in the peroxomonosulfate (PMS) system, demonstrating superior catalytic degradation performance against antibiotics like TC and dye molecules (with a flux of 1880 L m-2 h-1 and a Kapp value of 648 min-1), as well as outstanding cycling stability and environmental tolerance. This discovery sheds light on the design and engineering of composite membrane materials and applications for advanced oxidation processes in water purification.

Research on the Heterogeneous Deformation Behavior of Nickel Base Alloy Based on CPFEM

AbstractIn this paper, a crystal plasticity finite element method (CPFEM), considering the grain morphology and orientation, as well as the dislocation density, is used to research the tensile deformation behavior of GH4169 based on Electron Backscatter Diffraction (EBSD). The stress, plastic strain, and dislocation density distributions are obtained for different levels of deformation. Results show that the stress, plastic strain, and dislocation density exhibit obvious heterogeneous plastic deformation, and stress concentration and dislocation pileup mainly occurs near grain boundaries. The initial dislocation density mainly affects the stress–strain curve of the material, and it can obviously effect the yield strength but cannot influence the hardening ability of the material. The total dislocation density increases with plastic strain. However, the texture evolution has no evident change with increasing plastic strain, except for the increase of texture content in Cube (001)[100].

Quantum Chemical Analysis of the Molecular and Fragment Ion Formation of Butyrophenone by High-Electric Field Tunnel Ionization at Atmospheric Pressure.

The molecular ion M+· was observed when the liquid sample of butyrophenone was supplied using atmospheric pressure corona discharge (APCD). In contrast, the vapor supply resulted in the formation of the protonated molecule [M+H]+. The mass spectrum obtained with the liquid supply showed two distinctive fragment ions at m/z 105 and 120, resulting from α-cleavage and McLafferty rearrangement (McLR), respectively. The APCD spectrum showed peaks of M+· and the characteristic two fragment ions that were the same as the field ionization mass spectra of butyrophenone as reported by Chait et al. and Beckey et al. The formation of the molecular and fragment ions strongly indicated that high-electric field tunnel ionization (HEFTI) occurs by the HEF strength exceeding 108 V/m at the tip of the corona needle in APCD. The charge and spin density distributions of the molecular and fragment ions were analyzed by quantum chemical calculations using time-dependent density functional theory (TDDFT) and natural bond orbital (NBO) analysis.

Lensing Point-spread Function of Coherent Astrophysical Sources and Nontrivial Wave Effects

Most research on astrophysical lensing has been conducted using the geometric optics framework, where there exists a clear concept of lensing images. However, wave optics effects can be important for coherent sources, e.g., pulsars, fast radio bursts, and gravitational waves observed at long wavelengths. There, the concept of lensing images needs an extension. We introduce the concept of the “lensing point-spread function” (LPSF), the smoothed flux density distribution of a coherent point source after being lensed, as a generalization of the lensing image concept at finite frequencies. The frequency-dependent LPSF captures the gradual change of the flux density distribution of the source from discrete geometric images at high frequencies to a smooth distribution at low frequencies. It complements other generalizations of lensing images, notably the imaginary images and the Lefschetz thimbles. Being a footprint of a lensing system, the LPSF is useful for theoretical studies of lensing. Using the LPSF, we identify a frequency range with nontrivial wave effects, where both geometric optics and perturbative wave optics fail, and determine this range to be ∣κ∣−1 ≲ ν ≲ 10, with κ and ν being the dimensionless lens amplitude and the reduced observing frequency, respectively. Observation of LPSFs with nontrivial wave effects requires either very close-by lenses or very large observing wavelengths. The potential possibilities are the lensing of gravitational waves, the plasma lensing of Milky Way pulsars, and lensing by the solar gravitational lens.

Microscopic scale influence of functional groups on the displacement behavior of coalbed methane by hot humid flue gas: A molecular simulation study based on the dynamic injection process

Building upon the CO2-ECBM technology (CO2-Enhanced Coaled Methane Recovery Technology), hot flue gas injection for displacing coalbed methane has been proposed to improve methane recovery efficiency and reduce the costs associated with CO2 capture during the displacement process as well as flue gas desulfurization and denitrification at coal-fired power plants. To explore the microscopic mechanisms of displacement and the influence of functional groups, this study used Molecular Dynamics (MD) and Grand Canonical Monte Carlo (GCMC) methods with Materials Studio software to model slit pores grafted with various functional groups for gas adsorption simulations. The adsorption characteristics of eight key functional groups (-C=OCH₃, -COOH, -CH₂OH, Ar-OH, -OCH₃, -C6H6, -CH₃, -CH₂) for hot flue gas and methane were analyzed, focusing on adsorption strengths and underlying mechanisms. Simulations of the dynamic injection of hot flue gas were conducted, monitoring energy changes and methane density distribution to establish the relationship between adsorption strength and the difficulty of coalbed methane (CBM) displacement. The results indicate that in this simulation, the methane displacement efficiency using hot flue gas to displace coalbed methane exceeded 98%, demonstrating significantly better displacement performance compared to the use of pure carbon dioxide or nitrogen. Although both methane and hot flue gas show that greater interaction energy with pores makes displacement more difficult, the underlying causes differ. A comparison of the interaction energies between different functional groups and various gases reveals that oxygen-containing functional groups (-C=OCH3, -COOH, -CH2OH, Ar-OH, -OCH3) are unfavorable for the displacement of coalbed methane by wet hot flue gas, whereas aliphatic hydrocarbons (-CH3, -CH2) facilitate the displacement process.

Influence of operating conditions on heat and mass transfer in PEMFCs with platinum loading random distributions

The catalyst loading random distribution has a divergent effect on heat transfer and electrical property inside fuel cells than the catalyst loading homogeneous distribution, but the effects of different stoichiometric ratios and average platinum loadings on electrical property, heat as well as species transfer in the fuel cell considering catalyst content random setting are still unclear. Hence, the impacts of stoichiometric ratios and average platinum loadings on the electrical property of fuel cell considering catalyst content random distribution are explored in this paper by using a steady state, two-dimensional, two-phase, non-isothermal fuel cell model coupled the catalyst layer agglomerate model considering the multi-scale problem of catalyst layer. Results indicate that stoichiometric ratio and average platinum loading do not influence the effect direction of catalyst content distributed randomly on the output power. However, with the stoichiometric ratio rising, the impact degree of catalyst content distributed randomly on the output power first enhances and then diminishes. When the stoichiometric ratio is 1.3, the power density of uniform random distribution changes the most, decreasing by 3.91 %. As the average platinum loading rises, the impact degree of catalyst content distributed randomly on the output power gradually decreases. The power density of uniform random distribution decreases by 4.93 % when the catalyst content is 0.2 mg/cm2. Furthermore, the variation trends of temperature distribution, product and reactant content with stoichiometric ratio and average platinum loading under the platinum loading random distribution condition are consistent with that under the platinum loading homogeneous distribution condition. However, as the stoichiometric ratio rises, the reaction rate distribution becomes more uniform for normal random and homogeneous distributions, but the reaction rate distribution becomes uneven under uniform random distribution.

Identification of a double decay length ([formula omitted]) heat flux deposition shape with embedded thermal measurement and neural network

The estimation of the heat flux density distribution profiles in tokamak devices is a very important research topic for edge plasma physics purposes and also to ensure the safety of the machine. In the radial direction, the heat flux exhibits an exponential decay that could be captured by thermal sensors distributed in the plasma facing components. Radially distributed thermal sensors based on Fiber Bragg grating technology have been embedded in the WEST lower divertor to study the heat flux deposition profiles during plasma operation. The comparison between embedded measurements and a 3D finite element model shows a small decay length (5 – 10 mm) on top of a wider heat flux with a decay length around 30 to 50 mm. A tool using neural network has been developed in order to predict the values of the different parameters describing the deposited heat flux from embedded temperature measurements in steady state. A large span of deposited heat fluxes with maximum heat flux ranging from 1 to 9 MW/m2 and decay length from 5 to 50 mm were characterized using this tool over a database of more than 250 experimental L-mode pulses performed in WEST in attached divertor configuration. The comparison of the predicted heat flux parameters values with macroscopic plasma parameters have revealed the appearance of the narrow component with the increase of the divertor power load (Pdiv) with a threshold dependant of the plasma current (IP).

Convolutional network learning of self-consistent electron density via grid-projected atomic fingerprints

The self-consistent field (SCF) generation of the three-dimensional (3D) electron density distribution (ρ) represents a fundamental aspect of density functional theory (DFT) and related first-principles calculations, and how one can shorten or bypass the SCF loop represents a critical question in electronic structure theory from both practical and fundamental standpoints. Herein, a machine learning strategy, DeepSCF, is presented in which the map between the SCF ρ and the initial guess density (ρ0) constructed by the summation of neutral atomic densities is learned using 3D convolutional neural networks (CNNs). High accuracy and transferability of DeepSCF are achieved by first encoding ρ0 on a 3D grid and then expanding the input features to include atomic fingerprints beyond ρ0. The prediction of the residual density (δρ) rather than ρ itself is targeted, and given that δρ is indicative of chemical bonding information, a dataset of small-sized organic molecules featuring diverse bonding characters is adopted. The fidelity of DeepSCF is finally enhanced by subjecting the atomic geometries of the dataset to random rotations and strains. The effectiveness of DeepSCF is demonstrated using a complex carbon nanotube-based DNA sequencer model. This work evidences that the nearsightedness in electronic structure can be optimally represented via the spatial locality in CNNs, offering insight into the success of various machine learning-based atomistic materials simulations.

Structural Features of Porous Silicon Formed on Heavily Doped Plates of Single-Crystal Silicon with Electron Conductivity

Using scanning electron microscopy, the structures of the surface and internal regions of porous silicon obtained by anodizing heavily doped plates of single-crystal silicon with electron conductivity in a hydrofluoric acid solution at different current densities were studied. It is found that the porous silicon surface has dark gray and light gray pores, which differ in size and surface distribution density. Dark gray pores possess larger sizes, and their density is about 5–10 times less than that of light gray pores. Based on the cross-section imagery, it is shown that light gray pores correspond to underdeveloped channels of small depth, while dark gray pores are the entrance points of deep bottle-shaped channels passing from the surface into the depth of the silicon wafer. The equivalent diameters of light gray pores on the surface of porous silicon are 12–15 nm and are practically independent of the anodic current density. At the same time, the equivalent diameters of dark gray pores and average distances between their centers increase linearly from 15 to 35 nm on the surface and from 35 to 120 nm in the volume of porous silicon when the current density is increased from 30 to 90 mA/cm2. The average thickness of silicon skeleton elements is about 3 nm on the surface and increases to 5–6 nm in the volume. By setting the density of the anode current, it is possible to obtain layers of porous silicon with different structural parameters. The obtained research results have practical significance for the formation of composite materials based on porous silicon, which can be used as a porous matrix for the deposition of metals and semiconductors.

Research on the reliability of wire web in diamond multi-wire saw slicing photovoltaic monocrystalline silicon wafer

Diamond multi-wire slicing technology is the main method for producing the solar cell substrate based on monocrystalline silicon. To reduce the production cost and increase the production efficiency during the sawing process, the diameter of the diamond saw wire is becoming thinner, and the sawing speed is getting faster, which leads to an increasingly prominent problem of saw wire breakage during the slicing process. To understand the breaking characteristics of diamond saw wire and evaluate the reliability of the saw wire during the sawing process, the tensile testing of saw wires was carried out in this paper. And based on the Weibull function, the breaking force was analyzed statistically. A maximum tension force model for the saw wire during the sawing process was established. And based on the maximum tension force model and Weibull reliability function, the influence of various process parameters on the reliability of the wire web was analyzed. The results indicated that as the usage time of the saw wire increases, the breaking force gradually decreases and stabilizes. Compared to the fresh saw wire, the reliability of the used saw wires is significantly reduced. As the abrasive distribution density and the wire speed increases, the reliability of the wire web gradually increases. Conversely, as the feed speed and the pretension of the saw wire increase, the reliability of the wire web gradually decrease. The results of this paper provide a theoretical approach for assessing the reliability of diamond saw wire web during the sawing process. It also provides guidance for optimizing process parameters to enhance the reliability of the wire web.

Loss of pelagic fish and zooplankton density associated with subglacial upwelling in high Arctic estuaries may be mitigated by benthic habitat expansion following tidewater glacier retreat

Glacier fronts are hotspots of pelagic productivity due to upwelling of nutrient-rich water. As tidewater glaciers retreat into land, this subglacial circulation will disappear and sedimentation from terrestrial runoff will increase, leading to a decrease in pelagic productivity with a decline in the abundance of fish and zooplankton. We used Billefjorden, a high Arctic fjord with a glacier recently transitioned from sea- to land-terminating as a case study to identify spatial differences and small-scale environmental drivers of density and vertical distribution of fish and zooplankton along a gradient of glacier retreat (directly in front of the land-terminating glacier front, a river bay with terrestrial input from land-terminating glaciers further inland and a location with minimal glacial input). We developed a sustainable and efficient protocol to safely sample the glacier front and shallow coastal areas using hydroacoustics and a remote autonomous vehicle combined with oceanographic measurements and baited remote cameras. Over two years, pelagic density was lowest at the now land-terminating glacier front and highest at the site with lowest terrestrial input. Temperature, depth and turbidity explained less than 8% of the variation each.