Deviation Of Density Research Articles

Analysis of a Navier–Stokes phase-field crystal system

We consider an evolution system modeling a flow of colloidal particles which are suspended in an incompressible fluid and accounts for colloidal crystallization. The system consists of the Navier–Stokes equations for the volume averaged velocity coupled with the so-called Phase-Field Crystal equation for the density deviation. Considering this system in a periodic domain and assuming that the viscosity as well as the mobility depend on the density deviation, we first prove the existence of a weak solution in dimension three. Then, in dimension two, we establish the existence of a (unique) strong solution.

OCTA evaluates changes in retinal microvasculature in renal hypertension patients

The objective of this study is to utilize optical coherence tomography angiography (OCTA) techniques for the purpose of identifying abnormalities in retinal and conjunctival vascular density among patients afflicted with renal hypertension. From October 2022 to October 2023, a cohort of sixteen patients diagnosed with renal hypertension (RH), comprising a total of 32 eyes, was selected from the Department of Nephrology at the First Affiliated Hospital of Nanchang University. Concurrently, a group of sixteen healthy individuals, carefully matched in characteristics, was recruited from volunteers at the Ophthalmology Research Center and designated as the healthy controls (HCs) group. Optical coherence tomography angiography was employed to assess and examine the superficial vascular plexus (SVP) and deep vascular plexus (DVP) of the macular retina in both eyes. Subsequently, a comparative analysis was conducted between the two groups, focusing on the superficial and deep retinal microvessels (MIR), macrovascular (MAR), and total microvascular (TMI). The present study employed the central annuli segmentation method (C1–C6), the hemispheric segmentation method (SL, IL, SR, IR), and the Early Treatment Diabetic Retinopathy Study (S, I, L, R) to evaluate deviations in retinal blood vessel density. The investigation aimed to examine the association between blood vessel density and TMI in conjunctival capillaries. A statistically significant difference (p < 0.05) in macular retinal vascular density was observed between the two groups based on the OCTA data. Specifically, in SVP, the density of TMI, MIR, and MAR in the RH group was significantly lower compared to the HCs group (p < 0.05). Additionally, the deep density of TMI and MIR in DVP of the RH group was significantly lower than that of the HCs group (p < 0.05). Furthermore, using the hemispheric segmentation method, both the superficial and deep retina showed a significant reduction in the density of SL, SR and IL regions (p < 0.05). In the ETDRS method, there was a significant decrease in superficial and deep retinal S, I, and L in the RH group (p < 0.05). When applying the central annuli segmentation methods, the RH group exhibited a significant decrease in the superficial retinal C1–3 region (p < 0.05) and a noticeable reduction in the deep retina in the C1–4 region (p < 0.05). Furthermore, a higher positive likelihood ratio was observed in the deep SL and superficial C2 region. There was a positive correlation between conjunctival capillary density and the region of TMI in depth. The results of the OCTA investigation revealed a significant disparity in the density of superficial and deep retinal blood vessels between RH group and the HCs group. Additionally, a notable correlation was observed between the depth of TMI and the density of conjunctival capillaries. These findings highlight the potential of retinal OCTA as a valuable tool for early detection and image-assisted diagnosis of retinopathy progression in patients with RH.

Research on Reactivity-Equivalent Physical Transformation Method for Double Heterogeneity in Pressurized Water Reactors Based on Machine Learning

Traditional computational methods for pressurized water reactors are unable to handle dispersed fuel particles as the double heterogeneity and the direct volumetric homogenization can result in significant errors. In contrast, reactivity-equivalent physical transformation techniques offer high precision for addressing the double heterogeneity introduced by dispersed fuel particles. This approach converts the double heterogeneity problem into a single heterogeneity problem, which is then subsequently investigated by using the conventional pressurized water reactor computational procedure. However, it is currently empirical and takes a lot of time to obtain the right k∞. In this paper, we train the RPT model by using the existing dataset of plate-dispersed fuel and rod-dispersed fuel by a machine learning method based on a linear regression model, and we then use the new data to make predictions and derive the corresponding similarity ratios. The burnup verification, density verification, fission rate verification, and neutron energy spectrum analysis are calculated through the OpenMC program. For plate-type fuel elements, the method maintains an accuracy within 200 pcm during depletion, with deviations in the 235U density and 235U fission rate within 0.1% and neutron energy spectrum errors within 6%. For rod-type fuel elements, the method maintains an accuracy within 100 pcm during depletion, with deviations in 235U and 239Pu density within 1.5% and neutron energy spectrum errors within 1%. The numerical validation indicates that the reactivity-equivalent physical transformation method based on the linear regression model not only greatly improves the computational efficiency, but also ensures a very high accuracy to deal with double heterogeneity in nuclear reactors.

Analysis of the Performance of the City Transport Route Network System (AKDP) in Gorontalo Province, Indonesia

This study evaluates the performance of the intercity public transportation route network system (AKDP) in Gorontalo Province, Indonesia, with a focus on aspects of service coverage, overlap, density, and route network deviations. Data were obtained through field surveys. The research method includes spatial analysis using Geographic Information Systems (GIS) to evaluate the coverage and distribution of routes, as well as statistical analysis to measure the density and ratio of routes based on operational data and field surveys. The research results show that the coverage of route services is not yet even, with some areas being underserved. Route overlaps were identified on several main corridors, resulting in operational inefficiencies. The density of the routes shows significant variation, with some routes experiencing overcrowding while others are underutilized. The route ratio also shows an imbalance between the number of vehicles and passenger needs. Deviations from the established route plan were also found, which could potentially reduce service reliability. Based on these findings, it is recommended to restructure the route network and improve operational management to enhance the efficiency and quality of transportation services in Gorontalo Province.

Application of Soft Magnetic Composite in XEV Motor Core Manufacturing: Process Effects and Performance Analysis

This study explores the application of AncorLam HR (Höganäs, Sweden), a soft magnetic composite material, in the stator core of an axial flux permanent magnet drive motor. Building on previous research that provided mechanical and thermal properties of the material, the focus is on analyzing how the manufacturing process affects the motor core’s shape. A bulk prototype was created based on case 3, which demonstrated the least deviation in density and internal stress. The prototypes were produced under the conditions of SPM 7 and 90 °C, and a heat treatment in a nitrogen atmosphere for 1 h, resulting in an average density error of 0.54%, confirming process effectiveness. A microstructural analysis using scanning electron microscopy (SEM) on Sample 2, with the highest density, confirmed consistency between simulation and prototype trends. Electron backscatter diffraction (EBSD) and X-ray diffraction (XRD) analyses revealed that the internal phase structure remained unchanged. Energy-dispersive spectroscopy (EDS) and transmission electron microscopy (TEM) identified the elimination of phosphorus (P) during molding, affecting the insulating layer, a critical factor for SMC materials. In motor simulations and actual measurements, the average torque was recorded as 37.7 N·m and 34.7 N·m at 1500 rpm and 27.7 N·m and 25.1 N·m at 2000 rpm, respectively. The torque comparison observed in the actual measurements compared to the simulation results indicates that the output loss increases in the actual measurements due to the deterioration of the insulation performance judged based on the microstructure evaluation. This study confirms the viability of using AncorLam HR in motor cores for electric vehicles and provides key data for improving the performance.

TAMie Force Field for Alkanethiols: Multifidelity Gaussian Processes for Dealing with Scarce Experimental Data.

This study extends the transferable anisotropic Mie potential (TAMie) to alkanethiols. The force field parameters are optimized by using an analytic equation of state as a surrogate model. Given the lack of experimental density data at elevated temperatures where Monte Carlo simulations have high statistical precision, the equation of state is supplemented by a linear multifidelity Gaussian process approach to bridge the temperature gap. Force field parameters are adjusted by minimizing squared deviations of calculated vapor pressures and liquid densities from experimental data of 1-propanethiol, 1-butanethiol and 1-pentanethiol leading to small mean absolute relative deviations in liquid densities and vapor pressures. The force field is transferable to higher 1-thiols, as shown for 1-hexanethiol and 1-octanethiol. Individual parameter sets are provided for methanethiol and ethanethiol. The shear viscosity of pure substances is predicted in fair agreement with experimental data, considering that it is not included in the parametrization. Further, the phase behavior of binary mixtures of alkanethiols with alkanes is studied, and predictions of the TAMie model are found in excellent agreement with experimental data.

Population Distribution in Guizhou’s Mountainous Cities: Evolution of Spatial Pattern and Driving Factors

Guizhou is a typical mountainous province and is also one of the lowland regions in China that has attracted a population influx. Here, using population density data from 2000 to 2020 as the basic dataset and the coefficient of variation method and standard deviation ellipse analysis, we investigated the spatial characteristics across different years. The results show: Firstly, Guizhou’s population has a distinct spatial distribution, characterized by a lower population density in the southeast and a higher density in the northwest as well as an increasing polarization of population concentration toward the centers of prefecture-level cities and provincial capitals. Fluctuations in population density resemble a central siphon effect, which is particularly pronounced in the provincial capital and show a significant gravitational pull. Secondly, the coefficient of variation in population density across Guizhou’s counties is spatially divided by Guiyang, showing higher values in the east and lower values in the west. Furthermore, the ellipse of the standard deviation of population density is gradually shrinking, indicating an increasingly concentrated population distribution. Thirdly, the explanatory power of the population and socio-economic systems on the population distribution in Guizhou is significantly greater than that of the natural systems. Population distribution and migration patterns have shifted from purely “economic driven” to coexisting with “economic and comfort-oriented” trends, and there is an urgent need to improve the comfort level of public services as a typical supply, in order to boost Guizhou’s population attraction.

The cutting-edge roles of lasers in endodontics: A bibliometric and scientometric analysis of the 100 most-cited articles

Purpose: This bibliometric and scientometric analysis aimed to delve into the forefront roles of lasers in endodontics from 1990 to 2024. Methods: A comprehensive electronic search was conducted using “Clarivate Analytics Web of Science, All Databases” to retrieve the most-cited articles pertaining to the topic. These articles were then ranked in descending order according to their citation counts and the top 100 were selected for further analysis. Parameters including citation density, publication year, journal, journal impact factor (IF), country, institution, author, study design, study field, evidence level, laser type, and keywords were meticulously analyzed. Results: The mean and standard deviations of total citation and citation density were 106.47 ± 65.76 and 7.61 ± 5.13, respectively. Positive and negative correlations were found between the number of citations and citation density and age of publication. While the mean number of citations was significantly higher in the period 2001–2010 compared to the other periods (P < 0.05), values were similar between the periods 1990–2000 and 2011–2014 (P > 0.05). Articles were mainly published in the Journal of Endodontics. The most productive country, institutions, and author were the United States, the University of Showa, and Koukichi Matsumoto. Diode and Er: YAG lasers were commonly investigated. Ex vivo studies were mainly performed followed by in vitro ones. The main study field was “antimicrobial effect”. Among keywords, “photodynamic therapy” was used more frequently. Conclusion: Lasers are predominantly utilized to leverage their antimicrobial efficacy. Advancements in technology will lead to improvements in the properties of lasers, thereby enhancing the disinfection of the root canal system.

Impact of driver prediction with density deviation and anticipation in lattice hydrodynamic model with passing

This paper presents an integral lattice hydrodynamic model to examine the impact of driver’s anticipation and driving prediction with density deviation of leading vehicle under passing behavior. Both linear and nonlinear investigations have been used to obtain the stability condition and ‘modified Korteweg–de Vries (mKdV)’ equation is derived to further classify the nonlinear behavior of vehicular flow in terms of density waves, respectively. The linear stability condition shows that the stable region can be increased by decreasing the coefficient of predicted density deviation. Additionally, the stable region expands with a positive value of driver anticipation but contracts with a negative value. In comparison of the Nagatani and Redhu models, it is observed that for fixed value of density deviation coefficient, the new model conveys greater stability zone. To verify the theoretical findings, ‘numerical simulation’ has been conducted to examine the evolution of traffic flow in the presence of a small disturbances. The analytical results have been discussed for different passing rate with fixed value of driver’s anticipation and different values of density deviation coefficient. Furthermore, it has been noted that the stable region decreases for all passing rates when driver become more aware of the average speed of any neighbouring vehicles. The obtained results in this paper show that the traffic behavior with the existing model is more realistic. Additionally, this model will help in boosting vehicle movement efficiency, reducing congestion and enhancing road safety effectively .

Loss of synaptic density in nucleus basalis of meynert indicates distinct neurodegeneration in Alzheimer's disease: the RJNB-D study.

The nucleus basalis of Meynert (NBM) is known to play a crucial role in the development and pathogenesis of Alzheimer's Disease (AD), particularly the cholinergic system within the NBM. However, the relationship between synaptic loss in the NBM and the clinical profile of AD remains unclear. In our study, we included 44 Aβ-negative normal controls (CN) and 76 Aβ-positive participants with cognitive impairment (CI). All participants underwent structural and diffusion magnetic resonance imaging (MRI), as well as positron emission tomography (PET) imaging to measure synaptic vesicle glycoprotein 2A (SV2A) levels (Trial registration: NCT05623124. Registered 21 November 2022). The SV2A standardized uptake value ratios (SUVR) distribution in the NBM of CN participants was used as the reference norm. We investigated the association between NBM synaptic density and clinical performance, traditional AD biomarkers, and white matter tracts that passed the NBM. Participants with cognitive impairment (CI) who had NBM synaptic density below 1.5 standard deviations (SD) or 0.5 SD of the norm exhibited worse cognitive performance compared to cognitively normal (CN) individuals. Crucially, the extent of deviation in synaptic density from the norm was directly proportional to the severity of cognitive impairment and neurodegeneration biomarkers. Furthermore, among patients with cognitive impairment, synaptic loss in the NBM was associated with potential impairment in the density and organization of neurites within the white matter tracts connected to the NBM. Finally, neurite density index in the medial tracts may play a mediating role in the relationship between NBM synaptic density and MMSE scores. The extent that synaptic density in NBM deviated from the norm suggested the extent of worse cognitive performance and severe neurodegeneration. Furthermore, cognitive impairment associated with synaptic loss in the NBM may be mediated by its pathological impact on NBM white matter tracts.

Statistics and Models of the Electron Plasma Density From the Van Allen Probes

AbstractWe use the full NASA Van Allen Probes mission (2012–2019) to extract the electron plasma density from the Electric and Magnetic Field Instrument Suite and Integrated Science (EMFISIS) and Electric Field and Waves (EFW) instruments and discuss the evolution of the plasmasphere. We generate new statistics including mean and standard deviations of the plasma density with respect to L‐shell, magnetic local time (MLT), and various geomagnetic indices. These statistics are generated to be applied in radiation belt physics and space weather codes (with fits provided). The mean plasmasphere is circular around Earth with respect to MLT for Kp &lt; 1. The mean 100 cm−3 level line is above L = 5 and mean 10 cm−3 level expands above the Van Allen Probes apogee for Kp &lt; 1. The outer electron belt lies within the plasmasphere for 60% of all times. As activity increases (Kp &gt; 2), a gradual MLT asymmetry forms with higher mean density in the afternoon sector due to plumes expanding outward. Conversely, the mean density decreases on the dawn and night sectors. The mean density is between ∼500 and ∼50 cm−3 between L ∼ 4 and L ∼ 6 during quiet and moderately active times (Kp &lt; 3), representing ∼80% of all times. Statistics in regions of high density below L = 2 are underdefined for intense activity. The highest standard deviation of density represents a factor 2.5 to 3 times the mean above L = 5 and for active times. We find the percent difference between the EFW and EMFISIS densities is bounded by ±20% for quiet and moderate activity (Kp &lt; 5) and goes up to ±100% for extreme activity.

Multi-objective optimization of custom implant abutment design for enhanced bone remodeling in single-crown implants using 3D finite element analysis

The optimal configuration of a customized implant abutment is crucial for bone remodeling and is influenced by various design parameters. This study introduces an optimization process for designing two-piece zirconia dental implant abutments. The aim is to enhance bone remodeling, increase bone density in the peri-implant region, and reduce the risk of late implant failure. A 12-month bone remodeling algorithm subroutine in finite element analysis to optimize three parameters: implant placement depth, abutment taper degree, and gingival height of the titanium base abutment. The response surface analysis shows that implant placement depth and gingival height significantly impact bone density and uniformity. The taper degree has a smaller effect on bone remodeling. The optimization identified optimal values of 1.5 mm for depth, 35° for taper, and 0.5 mm for gingival height. The optimum model significantly increased cortical bone density from 1.2 to 1.937 g/cm3 in 2 months, while the original model reached 1.91 g/cm3 in 11 months. The standard deviation of density showed more uniform bone apposition, with the optimum model showing values 2 to 6 times lower than the original over 12 months. The cancellous bone showed a similar trend. In conclusion, the depth and taper have a significant effect on bone remodeling. This optimized model significantly improves bone density uniformity.

Hybrid EEG data analysis for diagnosis of stress-related neurological disorder: SKY as an alternative therapy

There are many reasons associated with stress, long term stress induces neurological and psychosomatic disorders like hypertension, hypothyroidism, diabetes, anxiety and depression which affect the lifestyle of human beings. Consequently, behavioural activity and action gradually change in their surrounding environment and also perceived by others. In general, stressful respiration is relatively different from normal. To release stress and control all the neuropsychological hormones, multiple activities like playing games, watching a movie, listening to songs and music, etc. or intake of medicine/drugs such as (Allopathic /Homeopathic/Ayurvedic) are used. Medicines can provide easy stress evasion, but relief is only temporary. Thus, yoga and Sudarshan kriya (SK) meditation is a unique and alternate therapy identified by Gurudev Sri Sri Ravi Shankar by Art of living. It would be a healthy way to get rid of stress in peoples’ lives. Study of long-term effects of (SKY) Sudarshan kriya Yoga before and after and response of the brain regions in experienced (10–15 yrs) practitioners, mediocre (3–5 yrs) and novice(non-practitioners) is the main objective of this work. This study is planned in three phases, the first phase is an experiment on SKY practitioners for more than 10–15 years, in which their (EEG) Electroencephalogram is recorded just after a session of meditation and the common portion of excitation amongst the three subjects is mined and analysed, to draw inferences. This inference would help us draw a conclusion about (BLOC) base level of consciousness considered as benchmark. In the second phase, comparison of benchmark data with the Mediocre (3–5 yrs) measurement and in third phase, benchmark versus Novice data, is done. Next is the phase of interpretation of the response in the form of EEG spectral waves as Type I- 10 to 15 years SKY Practitioners (Superconscious), Type II- SKY practitioners 3 to 5 years (mediocre/semiconscious) and Type. III- Non-practitioner subjects (Novice/Un-conscious). The unconsciousness here means a state of complete unawareness of the self, though conscious of the external, physical world. Thus, power spectrum analysis (PSA) is carried out and frequency of each electrode is computed through segment analysis, Power Spectrum Density (PSD), Correlation coefficient, Mean and Standard Deviation, for finding the level of consciousness. The spectral waveform of these recordings is analysed programmatically using machine learning techniques (used Python Language run on the Jupyter notebook, Spyder, Google colab environment).Frequency analysis results are obtained by placing 21 electrodes in human brain in different lobes that is (Fz, C2, P2, FP1, FP2, F3, F4, C3, C4, P3, P4, O1, O2, F7, F8, T3, T4, T5, BP4, E.G, T6) those are frequency measuring electrodes/channels placed on the frontal lobe, temporal lobe, parietal lobe and occipital lobe over skull and brainwaves alpha (α)[8–12 Hz], beta (β)[12–16 Hz], delta (δ)[0.5–4 Hz], theta (Θ)[4–8 Hz], gamma (γ)[16–32 Hz] are synthesized. The interpretation of these analyses suggests alternative therapeutic techniques, to improve both mentally and psychologically and thus become socially acceptable.

Electrospray plume divergence: Background pressure influence

The influence of background pressure on electrospray plume evolution is observed by simulating the emission and propagation of an electrospray particle population into an electric field at a range of relevant background pressures. Differences in plume evolution from atmospheric pressure to one hundredth of atmospheric pressure are evident from plume characteristics such as (1) the overall domain of the resulting plumes and (2) the terminal angle at a downstream terminus of one standard deviation and three standard deviations of particle number density. Plume divergence and terminal angle are shown to correlate strongly with background pressure for pressures above which plume-background collision rates are significant, consistent with experimental observations of increased plume divergence with increased background pressure. The results suggest a simple expression for the pressure below which a system achieves minimum plume divergence: Pth=kT/7.7299σfld for a system of temperature T, background fluid molecules with cross-section σfl, and plume species of diameter d.

KCl-UCl3 molten salts investigated by Ab Initio Molecular Dynamics (AIMD) simulations: A comparative study with three dispersion models

Ab Initio Molecular Dynamics (AIMD) simulations are performed on molten KCl-UCl3 salt mixtures to determine energies, heat capacities, and densities. The density-dependent energy correction (DFT-dDsC), Grimme et al.'s DFT-D3, and Langreth & Lundqvist (vdW-cx) models are used for dispersion forces and combined with the Perdew-Burke-Ernzerhof (PBE) exchange-correlation potential with a Hubbard U parameter for the 5f electrons of uranium. After validating predictions for the end-member systems to literature data, KCl-UCl3 mixtures are studied at select temperatures. Densities and energies both deviate from ideal solution behavior, with the maximum deviation occurring around 36% UCl3 for mixing energies and slightly lower (29% UCl3) for densities. Compared to the NaCl-UCl3 system, which was previously investigated using the same simulation methodologies, the KCl-UCl3 density and mixing energy deviations from ideal solution behavior are larger by almost a factor of two. No deviation from ideal solution behavior for heat capacity was observed. The AIMD predictions for mixing energies and densities agree qualitatively with experimental data, though the spread in data obtained from the various dispersion force models utilized, measurements, and empirical estimates makes strong conclusions difficult. The dependence of thermodynamic and thermophysical properties on composition is correlated with the local chemistry of the solution phase, in particular, the tendency of UCl3 to form network structures.

Field emission from two-dimensional (2D) CdSSe flake flowers structure grown on gold coated silicon substrate: An efficient cold cathode.

Field emission finds a vital space in numerous scientific and technological applications, including high-resolution imaging at micro- and nano-scales, conducting high-energy physics experiments, molecule ionization in spectroscopy, and electronic uses. A continuous effort exists to develop new materials for enhanced field emission applications. In the present work, two-dimensional (2D) well-aligned CdSSe flake flowers (CdSSe-FFs) were successfully grown on gold-coated silicon substrate utilizing a simple and affordable chemical bath deposition approach at ambient temperature. The time-dependent growth mechanism from nanoparticles to FFs was observed at optimized parameters such as concentration of precursors, pH (~11), deposition time, and solution temperature. The crystalline nature of CdSSe-FFs is confirmed by high-resolution transmission electron microscopy (HRTEM) results, and selected area electron diffraction (SAED) observations reveal a hexagonal crystal structure. Additionally, the CdSSe-FFs thickness was confirmed by TEM analysis and found to be ~20-30 nm. The optical, photoelectric, and field emission (FE) characteristics are thoroughly explored which shows significant enhancement due to the formation of heterojunction between the gold-coated silicon substrate and CdSSe-FFs. The UV-visible absorption spectra of CdSSe-FFs show enhanced absorption at 700 nm, corresponding to the energy band gap (Eg) of 1.77 eV. The CdSSe-FFs exhibited field emission and photosensitive field emission (PSFE) characteristics. In FE study CdSSe-FFs shows an increase in current density of 387.2 μ A cm-2 in an applied field of 4.1 V m-1 which is 4.08 fold as compared to without light illumination (95.1 μ A cm-2). Furthermore, it shows excellent emission current stability at the preset value of 1.5 μA over 3 h with a deviation of the current density of less than 5% respectively. RESEARCH HIGHLIGHTS: Novel CdSSe flake flowers were grown on Au-coated Si substrate by a cost-effective chemical bath deposition route. The growth mechanism of CdSSe flake flowers is studied in detail. Field emission and Photoluminescence study of CdSSe flake flowers is characterized. CdSSe flake flowers with nanoflakes sharp edges exhibited enhanced field emission properties.

FIAO: Feature Information Aggregation Oversampling for imbalanced data classification

Classification performance often deteriorates when machine learning algorithms are trained on imbalanced data. Although oversampling methods have been successfully employed to address imbalanced data, existing approaches have limitations such as information loss, difficulty in parameter selection, and boundary effects when using and calculating nearest neighbors and densities. Therefore, this study introduces a novel oversampling method called Feature Information Aggregation Oversampling (FIAO). FIAO leverages feature information, including feature importance, feature density, and standard deviation, to guide the oversampling process. Initially, the feature information is employed to partition features into suitable intervals for feature generation. Subsequently, features are generated within these intervals. Finally, the generated features are integrated into the minority class data to achieve effective oversampling. The key advantage of FIAO lies in its ability to fully exploit the intrinsic information carried by the features themselves, thus circumventing issues related to parameter selection and boundary effects. To assess its efficacy, extensive experiments were conducted on 12 widely used benchmark datasets, comparing the performance of the proposed method against 10 popular resampling methods across four commonly used classifiers. The experimental results show that the proposed FIAO method shows ideal results in multiple application scenarios and achieves optimal performance.

Response of ion velocities of daytime ionospheric wavenumber-4 to solar activity observed by ROCSAT-1 and DEMETER

The electron/ion density/temperature and ion velocities observed by the ROCSAT-1 and DEMETER satellites are used to examine the daytime wavenumber-4 (WN4) feature in the equatorial/low latitude ionosphere during various months and solar activity levels of 1999–2010. A moving median process has been employed to isolate WN4 features and calculate their amplitudes, while the upward ion drift is used to estimate electric fields. The ROCSAT-1 and DEMETER ion density, ion temperature, and ion velocity generally yield prominent WN4 features over the center of Pacific Ocean, the west side of South America, the center of the Atlantic Ocean, and Southern India. The correlation coefficient between the deviation of ion density and upward ion drift is significant during high solar activity of 1999–2004, while it approaches to zero during low solar activity of 2004–2010. This confirms that the longitudinal variation of the upward ion drift is essential during high solar activity, and the associated amplitude of dynamo eastward electric field is in the range of 0.10–0.14 mV/m, which is 15–19% of daily dynamo electric field. By contrast, the deviation of the ion density and the northward field-aligned ion flow show a clear anti-correlation which yields a maximum coefficient in August during low solar activity but no correlation during high solar activity. These indicate that the longitudinal variation of the meridional field-aligned ion flow could play an important role during low solar activity, and its amplitude is in the range of 10.44–13.91 m/s, which is 10–13% of the ambient ion flows.

Revealing the activity of inert Zn-based ZIF-L/aerogel composites in Fenton-like catalysis: Electron-deficient N ([sbnd]C[dbnd]N[sbnd]C) triggering 1O2 evolution

The inertness of Zn with fully occupied 3d10 configurations makes original Zn-based MOFs inactive, which are rarely reported in Fenton-like reactions. Herein, the prepared ZIF-L (Zn) was immobilized on foaming gelatin aerogel to fabricate composite catalysts (ZIF-L/FGA150) for PMS-based Fenton-like reactions. Unexpectedly, ZIF-L/FGA150 exhibited high Fenton-like activity in activating PMS for antibiotic norfloxacin (NOF) removal, by which over 99 % NOF could be degraded in 30 min. 1O2 was proved to be the sole contributor to mediating the non-radical oxidization process without the participation and evolution of free radicals. Mechanism investigation further revealed that the uncoordinated N (CNC) at edge 2-methylimidazole bonding with one Zn core were electron-deficient as a result of the deviation of electron density towards Zn-N4 coordination, and the electron-deficient N were identified as the active sites responsible for triggering electron transfer from the nucleophilic peroxy bonds of PMS accompanied by PMS decomposition into SO5− to generate singlet oxygen (1O2), thus driving NOF degradation. Owing to the 1O2-mediating non-radical oxidization, ZIF-L/FGA150 possessed high Fenton-like activity, unaffected by the interference of various environmental factors and in multiple actual water matrixes. This work might provide a novel insight into the reactivity of Zn-based MOFs in Fenton-like catalysis and develop an effective approach for antibiotic remediation.

Soft measurement model for wet gas flow rate based on ultrasonic and differential pressure sensing

Non-separation measurement of gas–liquid two-phase flow is of great significance to industrial production and theoretical research on two-phase flow. In this paper, a soft measurement method of wet gas flow rate based on ultrasonic and differential pressure (DP) sensors is proposed. A total of 129 sets of experimental data are obtained in DN50 horizontal pipe, the superficial velocity of gas and liquid ranging from 5 m s−1 to 33 m s−1 and 0.015 m s−1 to 0.6 m s−1, respectively. Ten feature parameters of ultrasonic signals such as the time difference between two echoes and kurtosis factor are proposed from time and frequency domain. As well as the power spectral density, mean value and standard deviation value are analyzed based on DP signals. Furthermore, the Spearman correlation coefficient is introduced to evaluate these feature parameters quantitatively. And nine parameters which are highly correlated to gas–liquid flow rate are selected as the inputs of the soft measurement model. Finally, Decision Tree, least square support vector machines and artificial neural network (ANN) models are established through training with the experimental data. Comparison results show that ANN has the lowest prediction errors on the test set, with the mean absolute percentage errors of 1.49% and 3.2% for gas and liquid, respectively.