Effect of bubble morphology and behavior on power consumption in non-Newtonian fluids’aeration process

Due to a prolonged operation time and low mass transfer efficiency, the primary challenge in the aeration process of non-Newtonian fluids is the high energy consumption, which is closely related to the form and rate of impeller, ventilation, rheological properties and bubble morphology in the reactor. In this perspective, through optimal computational fluid dynamics models and experiments, the relationship between power consumption, volumetric mass transfer rate(kLa) and initial bubble size(d0) was constructed to establish an efficient operation mode for the aeration process of non-Newtonian fluids. It was found that reducing the d0could significantly increase the oxygen mass transfer rate, resulting in an obvious decrease in the ventilation volume and impeller speed. When d0was regulated within 2-5 mm,an optimal kLa could be achieved, and 21% of power consumption could be saved, compared to the case of bubbles with a diameter of 10 mm.

Financial literacy,behavioral traits,and ePayment adoption and usage in Japan

This study investigates how financial literacy and behavioral traits affect the adoption of electronic payment(ePayment)services in Japan.We construct a financial literacy index using a representative sample of 25,000 individuals from the Bank of Japan’s 2019 Financial Literacy Survey.We then analyze the relationship between this index and the extensive and intensive usage of two types of payment services:electronic money(e-money)and mobile payment apps.Using an instrumental variable approach,we find that higher financial literacy is positively associated with a higher likelihood of adopting ePayment services.The empirical results suggest that individuals with higher financial literacy use payment services more frequently.We also find that risk-averse people are less likely to adopt and use ePayment services,whereas people with herd behavior tend to adopt and use ePayment services more.Our empirical results also suggest that the effects of financial literacy on the adoption and use of ePayment differ among people with different behavioral traits.

Optimum Profiles of Endwall Contouring for Enhanced Net Heat Flux Reduction and Aerodynamic Performance

Successfully utilized non-axisymmetric endwalls to enhance turbine efficiencies(aerodynamic and turbine inlet temperatures)by controlling the characteristics of the secondary flow in a blade passage.This is accomplished by steady-state numerical hydrodynamics and deep knowledge of the field of flow.Because of the interaction between mainstream and purge flow contributing supplementary losses in the stage,non-axisymmetric endwalls are highly susceptible to the inception of purge flow exit compared to the flat and any advantage rapidly vanishes.The conclusions reveal that the supreme endwall pattern could yield a lowering of the gross pressure loss at the design stage and is related to the size of the top-loss location being productively lowered.This has led to diminished global thermal exchange lowered in the passage of the vane alone.The reverse flow adjacent to the suction side corner of the endwall is migrated farther from the vane surface,as the deviated pressure spread on the endwall accelerates the flow and progresses the reverse flow core still downstream.The depleted association between the tornado-like vortex and the corner vortex adjacent to the suction side corner of the endwall is the dominant mechanism of control in the contoured end wall.In this publication,we show that the non-axisymmetric endwall contouring by selective numerical shape change method at most prominent locations is advantageous in lowering the thermal load in turbines to augment the net heat flux reduction as well as the aerodynamic performance using multi-objective optimization.

Inspiration of Foreign Innovative Drug Pricing Methods and Medical Insurance Payment Standards to China

Objective To study the innovative drug pricing methods and medical insurance payment standards in foreign countries and to provide reference for China’s government.Methods The official websites were searched for information and related literature,and literature review was used.Results and Conclusion In foreign countries,the clinical value of innovative drugs and their impact on medical insurance funds were comprehensively evaluated based on factors such as quality-adjusted life years,clinical benefit,and improvement of clinical benefit.Then,the evaluation results were taken as an important basis for whether innovative drugs were admitted to the medical insurance catalog and establishing medical insurance payment standards.By using international experience for reference,innovative drug pricing methods and medical insurance payment standards for China’s national conditions can be improved by establishing a basic database of clinical value and drug economic evaluation of innovative drugs,as well as innovative drug payment models based on decision thresholds.

Selective and Adaptive Incremental Transfer Learning with Multiple Datasets for Machine Fault Diagnosis

The visions of Industry 4.0 and 5.0 have reinforced the industrial environment.They have also made artificial intelligence incorporated as a major facilitator.Diagnosing machine faults has become a solid foundation for automatically recognizing machine failure,and thus timely maintenance can ensure safe operations.Transfer learning is a promising solution that can enhance the machine fault diagnosis model by borrowing pre-trained knowledge from the source model and applying it to the target model,which typically involves two datasets.In response to the availability of multiple datasets,this paper proposes using selective and adaptive incremental transfer learning(SA-ITL),which fuses three algorithms,namely,the hybrid selective algorithm,the transferability enhancement algorithm,and the incremental transfer learning algorithm.It is a selective algorithm that enables selecting and ordering appropriate datasets for transfer learning and selecting useful knowledge to avoid negative transfer.The algorithm also adaptively adjusts the portion of training data to balance the learning rate and training time.The proposed algorithm is evaluated and analyzed using ten benchmark datasets.Compared with other algorithms from existing works,SA-ITL improves the accuracy of all datasets.Ablation studies present the accuracy enhancements of the SA-ITL,including the hybrid selective algorithm(1.22%-3.82%),transferability enhancement algorithm(1.91%-4.15%),and incremental transfer learning algorithm(0.605%-2.68%).These also show the benefits of enhancing the target model with heterogeneous image datasets that widen the range of domain selection between source and target domains.

Performance Analysis and Optimization of Energy Harvesting Modulation for Multi-User Integrated Data and Energy Transfer

Integrated data and energy transfer(IDET)enables the electromagnetic waves to transmit wireless energy at the same time of data delivery for lowpower devices.In this paper,an energy harvesting modulation(EHM)assisted multi-user IDET system is studied,where all the received signals at the users are exploited for energy harvesting without the degradation of wireless data transfer(WDT)performance.The joint IDET performance is then analysed theoretically by conceiving a practical time-dependent wireless channel.With the aid of the AO based algorithm,the average effective data rate among users are maximized by ensuring the BER and the wireless energy transfer(WET)performance.Simulation results validate and evaluate the IDET performance of the EHM assisted system,which also demonstrates that the optimal number of user clusters and IDET time slots should be allocated,in order to improve the WET and WDT performance.

Fully polarized Compton scattering in plane waves and its polarization transfer

Fully polarized Compton scattering from a beam of spin-polarized electrons is investigated in plane-wave backgrounds in a broad intensity region from the perturbative to the nonperturbative regimes.In the perturbative regime,polarized linear Compton scattering is considered for investigating polarization transfer from a single laser photon to a scattered photon,and in the high-intensity region,the polarized locally monochromatic approximation and locally constant field approximation are established and are employed to study polarization transfer from an incoming electron to a scattered photon.The numerical results suggest an appreciable improvement of about 10%in the scattering probability in the intermediate-intensity region if the electron’s longitudinal spin is parallel to the laser rotation.The longitudinal spin of the incoming electron can be transferred to the scattered photon with an efficiency that increases with laser intensity and collisional energy.For collision between an optical laser with frequency1 eV and a 10 GeV electron,this polarization transfer efficiency can increase from about 20%in the perturbative regime to about 50%in the nonperturbative regime for scattered photons with relatively high energy.

Nullors, and Nullor Circuits;There Applications in Symbolic Circuit Analysis and Design

The objective in this presentation is to introduce some of the unique properties and applications of nullors in active circuit analysis and designs. The emphasis is to discuss the role nullors can play in symbolic representation of transfer functions. To show this we adopt the topological platform for the circuit analysis and use a recently developed Admittance Method (AM) to achieve the Sum of Tree Products (STP), replacing the determinant and cofactors of the Nodal Admittance Matrix (NAM) of the circuit. To construct a transfer function, we start with a given active circuit and convert all its controlled sources and I/O-ports to nullors. Now, with a solid nullor circuit (passive elements and nullors) we first eliminate the passive elements through AM operations. This produces the STPs. Second, the all-nullor circuit is then used to find the signs or the STPs. Finally, the transfer function (in symbolic, if chosen) is obtained from the ratio between the STPs.

Image Recognition Model of Fraudulent Websites Based on Image Leader Decision and Inception-V3 Transfer Learning

The fraudulent website image is a vital information carrier for telecom fraud.The efficient and precise recognition of fraudulent website images is critical to combating and dealing with fraudulent websites.Current research on image recognition of fraudulent websites is mainly carried out at the level of image feature extraction and similarity study,which have such disadvantages as difficulty in obtaining image data,insufficient image analysis,and single identification types.This study develops a model based on the entropy method for image leader decision and Inception-v3 transfer learning to address these disadvantages.The data processing part of the model uses a breadth search crawler to capture the image data.Then,the information in the images is evaluated with the entropy method,image weights are assigned,and the image leader is selected.In model training and prediction,the transfer learning of the Inception-v3 model is introduced into image recognition of fraudulent websites.Using selected image leaders to train the model,multiple types of fraudulent websites are identified with high accuracy.The experiment proves that this model has a superior accuracy in recognizing images on fraudulent websites compared to other current models.

Nitrogen rhizodeposition from corn and soybean,and its contribution to the subsequent wheat crops

Nitrogen(N)is a key factor in the positive response of cereal crops that follow leguminous crops when compared to gramineous crops in rotations,with the nonrecyclable rhizosphere-derived N playing an important role.However,quantitative assessments of differences in the N derived from rhizodeposition(NdfR)between legumes and gramineous crops are lacking,and comparative studies on their contributions to the subsequent cereals are scarce.In this study,we conducted a meta-analysis of NdfR from leguminous and gramineous crops based on 34 observations published worldwide.In addition,pot experiments were conducted to study the differences in the NdfR amounts,distributions and subsequent effects of two major wheat(Triticum aestivum L.)-preceding crops,corn(Zea mays L.)and soybean(Glycine max L.),by the cotton wick-labelling method in the main wheat-producing areas of China.The meta-analysis results showed that the NdfR of legumes was significantly greater by 138.93%compared to gramineous crops.In our pot experiment,the NdfR values from corn and soybean were 502.32 and 944.12 mg/pot,respectively,and soybean was also significantly higher than corn,accounting for 76.91 and 84.15%of the total belowground nitrogen of the plants,respectively.Moreover,in different soil particle sizes,NdfR was mainly enriched in the large macro-aggregates(>2 mm),followed by the small macro-aggregates(2–0.25 mm).The amount and proportion of NdfR in the macro-aggregates(>0.25 mm)of soybean were 3.48 and 1.66 times higher than those of corn,respectively,indicating the high utilization potential of soybean NdfR.Regarding the N accumulation of subsequent wheat,the contribution of soybean NdfR to wheat was approximately 3 times that of corn,accounting for 8.37 and 4.04%of the total N uptake of wheat,respectively.In conclusion,soybean NdfR is superior to corn in terms of the quantity and distribution ratio of soil macro-aggregates.In future field production,legume NdfR should be included in the nitrogen pool that can be absorbed and utilized by subsequent crops,and the role and potential of leguminous plants as nitrogen source providers in crop rotation systems should be fully utilized.

Effects of projectile parameters on the momentum transfer and projectile melting during hypervelocity impact

The effects of projectile/target impedance matching and projectile shape on energy,momentum transfer and projectile melting during collisions are investigated by numerical simulation.By comparing the computation results with the experimental results,the correctness of the calculation and the statistical method of momentum transfer coefficient is verified.Different shapes of aluminum,copper and heavy tungsten alloy projectiles striking aluminum,basalt,and pumice target for impacts up to 10 km/s are simulated.The influence mechanism of the shape of the projectile and projectile/target density on the momentum transfer was obtained.With an increase in projectile density and length-diameter ratio,the energy transfer time between the projectile and targets is prolonged.The projectile decelerates slowly,resulting in a larger cratering depth.The energy consumed by the projectile in the excavation stage increased,resulting in lower mass-velocity of ejecta and momentum transfer coefficient.The numerical simulation results demonstrated that for different projectile/target combinations,the higher the wave impedance of the projectile,the higher the initial phase transition velocity and the smaller the mass of phase transition.The results can provide theoretical guidance for kinetic impactor design and material selection.

Corrigendum to“Atomic-scale electromagnetic theory bridging optics in microscopic world and macroscopic world”

The signs of the electric field markers in Figs.2 and 4 of the paper[Chin.Phys.B 32104211(2023)]have been corrected.These modifications do not affect the results derived in the paper.

Optimal and robust control of population transfer in asymmetric quantum-dot molecules

We present an optimal and robust quantum control method for efficient population transfer in asymmetric double quantum-dot molecules.We derive a long-duration control scheme that allows for highly efficient population transfer by accurately controlling the amplitude of a narrow-bandwidth pulse.To overcome fluctuations in control field parameters,we employ a frequency-domain quantum optimal control theory method to optimize the spectral phase of a single pulse with broad bandwidth while preserving the spectral amplitude.It is shown that this spectral-phase-only optimization approach can successfully identify robust and optimal control fields,leading to efficient population transfer to the target state while concurrently suppressing population transfer to undesired states.The method demonstrates resilience to fluctuations in control field parameters,making it a promising approach for reliable and efficient population transfer in practical applications.

A Feature Selection Method Based on Hybrid Dung Beetle Optimization Algorithm and Slap Swarm Algorithm

Feature Selection(FS)is a key pre-processing step in pattern recognition and data mining tasks,which can effectively avoid the impact of irrelevant and redundant features on the performance of classification models.In recent years,meta-heuristic algorithms have been widely used in FS problems,so a Hybrid Binary Chaotic Salp Swarm Dung Beetle Optimization(HBCSSDBO)algorithm is proposed in this paper to improve the effect of FS.In this hybrid algorithm,the original continuous optimization algorithm is converted into binary form by the S-type transfer function and applied to the FS problem.By combining the K nearest neighbor(KNN)classifier,the comparative experiments for FS are carried out between the proposed method and four advanced meta-heuristic algorithms on 16 UCI(University of California,Irvine)datasets.Seven evaluation metrics such as average adaptation,average prediction accuracy,and average running time are chosen to judge and compare the algorithms.The selected dataset is also discussed by categorizing it into three dimensions:high,medium,and low dimensions.Experimental results show that the HBCSSDBO feature selection method has the ability to obtain a good subset of features while maintaining high classification accuracy,shows better optimization performance.In addition,the results of statistical tests confirm the significant validity of the method.

Balancing electron transfer and intermediate adsorption ability of metallic Ni-Fe-RE-P bifunctional catalysts via 4f-2p-3d electron interaction for enhanced water splitting

Balancing electron transfer and intermediate adsorption ability of bifunctional catalysts via tailoring electronic structures is crucial for green hydrogen production,while it still remains challenging due to lacking efficient strategies.Herein,one efficient and universal strategy is developed to greatly regulate electronic structures of the metallic Ni-Fe-P catalysts via in-situ introducing the rare earth(RE)atoms(Ni-Fe-RE-P,RE=La,Ce,Pr,and Nd).Accordingly,the as-prepared optimal Ni-Fe-Ce-P/CC self-supported bifunctional electrodes exhibited superior electrocatalytic activity and excellent stability with the low overpotentials of 247 and 331 mV at 100 mA cm^(-2) for HER and OER,respectively.In the assembled electrolyzer,the Ni-Fe-Ce-P/CC as bifunctional electrodes displayed low operation potential of 1.49 V to achieve a current density of 10 mA cm^(-2),and the catalytic performance can be maintained for 100 h.Experimental results combined with density functional theory(DFT)calculation reveal that Ce doping leads to electron decentralization and crystal structure distortion,which can tailor the band structures and d-band center of Ni-Fe-P,further increasing conductivity and optimizing intermediate adsorption energy.Our work not only proposes a valuable strategy to regulate the electron transfer and intermediate adsorption of electrocatalysts via RE atoms doping,but also provides a deep under-standing of regulation mechanism of metallic electrocatalysts for enhanced water splitting.

Preparation and Tribological Properties of Onion-like Carbon/Waterborne Polyurethane Coatings

Rice husk powder was used as a carbon source in a high-temperature carbonization reaction for the production of rice husk ash(RHA).Under the catalysis of ferric nitrate,onion-like carbon(OLC)nanomaterial with a particle size of approximately 200 nm was successfully prepared and incorporated into waterborne polyurethane(WPU).The tribological properties of the coatings were determined using a controlled-atmosphere tribometer(WMT-2E)under dry-friction conditions.Following the friction test,the friction mechanism was investigated by characterizing the abrasive spot surfaces of the test samples using 3D laser microscopy and scanning electron microscopy/energy dispersive spectrometer.The final results demonstrated that the thermal stability of WPU composite coatings containing various concentrations of OLC nanoparticles was significantly enhanced,binding forces between coatings and steel sheets increased,and hardness improved compared to pure WPU coatings.Tribological tests revealed a notable enhancement in the anti-wear properties of WPU coatings due to the presence of OLC particles.Specifically,the wear rate of the 1.5%OLC/WPU coating was reduced by 45.3%.The coating’s anti-wear mechanism was attributed to the improvement in the mechanical properties of WPU due to OLC,as well as OLC’s participation in the formation of a transfer film under induced friction,which protected the matrix.

Consistency of Tropospheric Water Vapor between Reanalyses and Himawari-8/AHI Measurements over East Asia

High spatiotemporal resolution radiances from the advanced imagers onboard the new generation of geostationary weather satellites provide a unique opportunity to evaluate the abilities of various reanalysis datasets to depict multilayer tropospheric water vapor(WV),thereby enhancing our understanding of the deficiencies of WV in reanalysis datasets.Based on daily measurements from the Advanced Himawari Imager(AHI)onboard the Himawari-8 satellite in 2016,the bias features of multilayer WV from six reanalysis datasets over East Asia are thoroughly evaluated.The assessments show that wet biases exist in the upper troposphere in all six reanalysis datasets;in particular,these biases are much larger in summer.Overall,we find better depictions of WV in the middle troposphere than in the upper troposphere.The accuracy of WV in the ERA5 dataset is the highest,in terms of the bias magnitude,dispersion,and pattern similarity.The characteristics of the WV bias over the Tibetan Plateau are significantly different from those over other parts of East Asia.In addition,the reanalysis datasets all capture the shift of the subtropical high very well,with ERA5 performing better overall.

Numerical and theoretical study of load transfer behavior during cascading pillar failure

To further study the load transfer mechanism of roofemulti-pillarefloor system during cascading pillar failure(CPF),numerical simulation and theoretical analysis were carried out to study the three CPF modes according to the previous experimental study on treble-pillar specimens,e.g.successive failure mode(SFM),domino failure mode(DFM)and compound failure mode(CFM).Based on the finite element code rock failure process analysis(RFPA^(2D)),numerical models of treble-pillar specimen with different mechanical properties were established to reproduce and verify the experimental results of the three CPF modes.Numerical results show that the elastic rebound of roofefloor system induced by pillar instability causes dynamic disturbance to adjacent pillars,resulting in sudden load increases and sudden jump displacement of adjacent pillars.The phenomena of load transfer in the roofemulti-pillarefloor system,as well as the induced accelerated damage behavior in adjacent pillars,were discovered and studied.In addition,based on the catastrophe theory and the proposed mechanical model of treble-pillar specimen edisc spring group system,a potential function that characterizes the evolution characteristics of roof emulti-pillarefloor system was established.The analytical expressions of sudden jump and energy release of treble-pillar specimenedisc spring group system of the three CPF modes were derived according to the potential function.The numerical and theoretical results show good agreement with the experimental results.This study further reveals the physical essence of load transfer during CPF of roof emulti-pillarefloor system,which provides references for mine design,construction and disaster prevention.

Optimizing Deep Learning for Computer-Aided Diagnosis of Lung Diseases: An Automated Method Combining Evolutionary Algorithm, Transfer Learning, and Model Compression

Recent developments in Computer Vision have presented novel opportunities to tackle complex healthcare issues,particularly in the field of lung disease diagnosis.One promising avenue involves the use of chest X-Rays,which are commonly utilized in radiology.To fully exploit their potential,researchers have suggested utilizing deep learning methods to construct computer-aided diagnostic systems.However,constructing and compressing these systems presents a significant challenge,as it relies heavily on the expertise of data scientists.To tackle this issue,we propose an automated approach that utilizes an evolutionary algorithm(EA)to optimize the design and compression of a convolutional neural network(CNN)for X-Ray image classification.Our approach accurately classifies radiography images and detects potential chest abnormalities and infections,including COVID-19.Furthermore,our approach incorporates transfer learning,where a pre-trainedCNNmodel on a vast dataset of chest X-Ray images is fine-tuned for the specific task of detecting COVID-19.This method can help reduce the amount of labeled data required for the task and enhance the overall performance of the model.We have validated our method via a series of experiments against state-of-the-art architectures.

Construction of Pd-doped RuO_(2) nanosheets for efficient and stable acidic water oxidation

RuO_(2) has been considered a potential alternative to commercial IrO_(2) for the oxygen evolution reaction(OER)due to its superior intrinsic activity.However,its inherent structure dissolution in acidic environments restricts its commercial applications.In this study,we report a novel Pd-doped ruthenium oxide(Pd–RuO_(2))nanosheet catalyst that exhibits improved activity and stability through a synergistic effect of Pd modulation of Ru electronic structure and the two-dimensional structure.The catalyst exhibits excellent performance,achieving an overpotential of only 204 mVat a current density of 10 mA cm^(-2).Impressively,after undergoing 8000 cycles of cyclic voltammetry testing,the overpotential merely decreased by 5 mV.The PEM electrolyzer with Pd0.08Ru0.92O_(2) as an anode catalyst survived an almost 130 h operation at 200 mA cm^(-2).To elucidate the underlying mechanisms responsible for the enhanced stability,we conducted an X-ray photoelectron spectroscopy(XPS)analysis,which reveals that the electron transfer from Pd to Ru effectively circumvents the over-oxidation of Ru,thus playing a crucial role in enhancing the catalyst's stability.Furthermore,density functional theory(DFT)calculations provide compelling evidence that the introduction of Pd into RuO_(2) effectively modulates electron correlations and facilitates the electron transfer from Pd to Ru,thereby preventing the overoxidation of Ru.Additionally,the application of the two-dimensional structure effectively inhibited the aggregation and growth of nanoparticles,further bolstering the structural integrity of the catalyst.