Embryo Transfer Strategies for Women with Recurrent Implantation Failure During the Frozen-thawed Embryo Transfer Cycles:Sequential Embryo Transfer or Double-blastocyst Transfer?

Objective Both sequential embryo transfer(SeET)and double-blastocyst transfer(DBT)can serve as embryo transfer strategies for women with recurrent implantation failure(RIF).This study aims to compare the effects of SeET and DBT on pregnancy outcomes.Methods Totally,261 frozen-thawed embryo transfer cycles of 243 RIF women were included in this multicenter retrospective analysis.According to different embryo quality and transfer strategies,they were divided into four groups:group A,good-quality SeET(GQ-SeET,n=38 cycles);group B,poor-quality or mixed-quality SeET(PQ/MQ-SeET,n=31 cycles);group C,good-quality DBT(GQ-DBT,n=121 cycles);and group D,poor-quality or mixed-quality DBT(PQ/MQ-DBT,n=71 cycles).The main outcome,clinical pregnancy rate,was compared,and the generalized estimating equation(GEE)model was used to correct potential confounders that might impact pregnancy outcomes.Results GQ-DBT achieved a significantly higher clinical pregnancy rate(aOR 2.588,95%CI 1.267–5.284,P=0.009)and live birth rate(aOR 3.082,95%CI 1.482–6.412,P=0.003)than PQ/MQ-DBT.Similarly,the clinical pregnancy rate was significantly higher in GQ-SeET than in PQ/MQ-SeET(aOR 4.047,95%CI 1.218–13.450,P=0.023).The pregnancy outcomes of GQ-SeET were not significantly different from those of GQ-DBT,and the same results were found between PQ/MQ-SeET and PQ/MQ-DBT.Conclusion SeET relative to DBT did not seem to improve pregnancy outcomes for RIF patients if the embryo quality was comparable between the two groups.Better clinical pregnancy outcomes could be obtained by transferring good-quality embryos,no matter whether in SeET or DBT.Embryo quality plays a more important role in pregnancy outcomes for RIF patients.

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.

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.

Improving Heat Transfer in Parabolic Trough Solar Collectors by Magnetic Nanofluids

A parabolic trough solar collector(PTSC)converts solar radiation into thermal energy.However,low thermal efficiency of PTSC poses a hindrance to the deployment of solar thermal power plants.Thermal performance of PTSC is enhanced in this study by incorporating magnetic nanoparticles into the working fluid.The circular receiver pipe,with dimensions of 66 mm diameter,2 mm thickness,and 24 m length,is exposed to uniform temperature and velocity conditions.The working fluid,Therminol-66,is supplemented with Fe3O4 magnetic nanoparticles at concentrations ranging from 1%to 4%.The findings demonstrate that the inclusion of nanoparticles increases the convective heat transfer coefficient(HTC)of the PTSC,with higher nanoparticle volume fractions leading to greater heat transfer but increased pressure drop.The thermal enhancement factor(TEF)of the PTSC is positively affected by the volume fraction of nanoparticles,both with and without a magnetic field.Notably,the scenario with a 4%nanoparticle volume fraction and a magnetic field strength of 250 G exhibits the highest TEF,indicating superior thermal performance.These findings offer potential avenues for improving the efficiency of PTSCs in solar thermal plants by introducing magnetic nanoparticles into the working fluid.

Generation of mitochondrial replacement monkeys by female pronucleus transfer

Mutations in mitochondrial DNA(mtDNA)are maternally inherited and have the potential to cause severe disorders.Mitochondrial replacement therapies,including spindle,polar body,and pronuclear transfers,are promising strategies for preventing the hereditary transmission of mtDNA diseases.While pronuclear transfer has been used to generate mitochondrial replacement mouse models and human embryos,its application in non-human primates has not been previously reported.In this study,we successfully generated four healthy cynomolgus monkeys(Macaca fascicularis)via female pronuclear transfer.These individuals all survived for more than two years and exhibited minimal mtDNA carryover(3.8%–6.7%),as well as relatively stable mtDNA heteroplasmy dynamics during development.The successful establishment of this nonhuman primate model highlights the considerable potential of pronuclear transfer in reducing the risk of inherited mtDNA diseases and provides a valuable preclinical research model for advancing mitochondrial replacement therapies in humans.

P-induced electron transfer interaction for enhanced selective hydrogenation rearrangement of furfural to cyclopentanone

Optimizing the intrinsic activity of non-noble metal by precisely tailoring electronic structure offers an appealing way to construct cost-effective catalysts for selective biomass valorization.Herein,we reported a P-doping bifunctional catalyst(Ni-P/mSiO_(2))that achieved 96.6%yield for the hydrogenation rearrangement of furfural to cyclopentanone at mild conditions(1 MPaH_(2),150°C).The turnover frequency of Ni-P/mSiO_(2)was 411.9 h^(-1),which was 3.2-fold than that of Ni/mSiO_(2)(127.2 h^(-1)).Detailed characterizations and differential charge density calculations revealed that the electron-deficient Niδ+species were generated by the electron transfer from Ni to P,which promoted the ring rearrangement reaction.Density functional theory calculations illustrated that the presence of P atoms endowed furfural tilted adsorb on the Ni surface by the C=O group and facilitated the desorption of cyclopentanone.This work unraveled the connection between the localized electronic structures and the catalytic properties,so as to provide a promising reference for designing advanced catalysts for biomass valorization.

Efficient energy transfer from self-trapped excitons to Mn^(2+) dopants in CsCdCl_(3):Mn^(2+) perovskite nanocrystals

Mn^(2+)doping has been adopted as an efficient approach to regulating the luminescence properties of halide perovskite nano-crystals(NCs).However,it is still difficult to understand the interplay of Mn^(2+)luminescence and the matrix self-trapped exciton(STE)emission therein.In this study,Mn^(2+)-doped CsCdCl_(3) NCs are prepared by hot injection,in which CsCdCl_(3) is selected because of its unique crystal structure suitable for STE emission.The blue emission at 441 nm of undoped CsCdCl_(3) NCs originates from the defect states in the NCs.Mn^(2+)doping promotes lattice distortion of CsCdCl_(3) and generates bright orange-red light emission at 656 nm.The en-ergy transfer from the STEs of CsCdCl_(3) to the excited levels of the Mn^(2+)ion is confirmed to be a significant factor in achieving efficient luminescence in CsCdCl_(3):Mn^(2+)NCs.This work highlights the crucial role of energy transfer from STEs to Mn^(2+)dopants in Mn^(2+)-doped halide NCs and lays the groundwork for modifying the luminescence of other metal halide perovskite NCs.

Effect of surfactant frequently used in soil flushing on oxygen mass transfer in micro-nano-bubble aeration system

In-site soil flushing and aeration are the typical synergetic remediation technology for contaminated sites.The surfactant present in flushing solutions is bound to affect the aeration efficiency.The purpose of this study is to evaluate the effect of surfactant frequently used in soil flushing on the oxygen mass transfer in micro-nano-bubble(MNB)aeration system.Firstly,bio-surfactants and chemical surfactants were used to investigate their effects on Sauter mean diameter of bubble(dBS),gas holdup(ε),volumetric mass-transfer coefficient(kLa)and liquid-side mass-transfer coefficient(kL)in the MNB aeration system.Then,based upon the experimental results,the Sardeing's and Frossling's models were modified to describe the effect of surfactant on kL in the MNB aeration.The results showed that,for the twenty aqueous surfactant solutions,with the increase in surfactant concentration,the value of dBS,kLa and kL decreased,while the value ofεand gas-liquid interfacial area(a)increased.These phenomena were mainly attributed to the synergistic effects of immobile bubble surface and the suppression of coalescence in the surfactant solutions.In addition,with the presence of electric charge,MNBs in anionic surfactant solutions were smaller and higher in number than in non-ionic surfactant solutions.Furthermore,the accumulation of surfactant on the gas-liquid interface was more conspicuous for small MNB,so the reduction of kL in anionic surfactant solutions was larger than that in non-ionic surfactant solutions.Besides,the modified Frossling's model predicted the effect of surfactant on kL in MNB aeration system with reasonable accuracy.

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.

Performance of composite foundations with different load transfer platforms and substratum stiffness over silty clay

The semi-rigid pile-supported composite foundation is widely used in highway projects due to its effectiveness in increasing the bearing capacity and stability of foundations.It is crucial to understand the stress distribution across the embankment width and the behaviour of unreinforced foundations.Thus,five centrifuge tests were conducted to examine the bearing and deformation behaviours of NPRS(Non-Connected Piled Raft Systems)and GRPS(GeosyntheticReinforced Pile-Supported systems)with varying substratum stiffness,then a comparative analysis was conducted on embankment settlement,pressures underneath the embankments,and axial forces along the piles.The results indicated that greater substratum stiffness correlates with reduced settlement and deformation at various depths.Deformation occurring 5 meters from the embankment toe includes settlement in NPRS and upward movement in GRPS.The potential sliding surface is primarily located within the embankment in NPRS,whereas it may extend through both the embankment and foundation in GRPS.The pile-soil stress ratio and efficiency in NPRS are higher than in GRPS across the embankment.The axial force borne by end-bearing piles is significantly greater than that by floating piles.As the buried depth increases,the axial force in GRPS initially rises then declines,whereas in NPRS,it remains relatively constant within a certain range before decreasing.This study aids in assessing the applicability of composite foundations in complex railway environments and provides a reference for procedural measures under similar conditions.

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.

Effect of bipolar-plates design on corrosion,mass and heat transfer in proton-exchange membrane fuel cells and water electrolyzers:A review

Attaining a decarbonized and sustainable energy system,which is the core solution to global energy issues,is accessible through the development of hydrogen energy.Proton-exchange membrane water electrolyzers(PEMWEs)are promising devices for hydrogen production,given their high efficiency,rapid responsiveness,and compactness.Bipolar plates account for a relatively high percentage of the total cost and weight compared with other components of PEMWEs.Thus,optimization of their design may accelerate the promotion of PEMWEs.This paper reviews the advances in materials and flow-field design for bipolar plates.First,the working conditions of proton-exchange membrane fuel cells(PEMFCs)and PEMWEs are compared,including reaction direction,operating temperature,pressure,input/output,and potential.Then,the current research status of bipolar-plate substrates and surface coatings is summarized,and some typical channel-rib flow fields and porous flow fields are presented.Furthermore,the effects of materials on mass and heat transfer and the possibility of reducing corrosion by improving the flow field structure are explored.Finally,this review discusses the potential directions of the development of bipolar-plate design,including material fabrication,flow-field geometry optimization using threedimensional printing,and surface-coating composition optimization based on computational materials science.

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.

Research on shell-side heat and mass transfer with multi-component in LNG spiral-wound heat exchanger under sloshing conditions

The spiral-wound heat exchanger(SWHE) is the primary low-temperature heat exchanger for large-scale LNG plants due to its high-pressure resistance, compact structure, and high heat exchange efficiency. This paper studied the shell-side heat and mass transfer characteristics of vapor-liquid two-phase mixed refrigerants in an SWHE by combining a multi-component model in FLUENT software with a customized multicomponent mass transfer model. Besides, the mathematical model under the sloshing condition was obtained through mathematical derivation, and the corresponding UDF code was loaded into FLUENT as the momentum source term. The results under the sloshing conditions were compared with the relevant parameters under the steady-state condition. The shell-side heat and mass transfer characteristics of the SWHE were investigated by adjusting the component ratio and other working conditions. It was found that the sloshing conditions enhance the heat transfer performance and sometimes have insignificant effects. The sloshing condition is beneficial to reduce the flow resistance. The comprehensive performance of multi-component refrigerants has been improved and the improvement is more significant under sloshing conditions, considering both the heat transfer and pressure drop.These results will provide theoretical support for the research and design of multi-component heat and mass transfer enhancement of LNG SWHE under ocean sloshing conditions.

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.

Predictive modeling of critical temperatures in magnesium compounds using transfer learning

This study presents a transfer learning approach for discovering potential Mg-based superconductors utilizing a comprehensive target dataset.Initially,a large source dataset(Bandgap dataset)comprising approximately∼75k compounds is utilized for pretraining,followed by fine-tuning with a smaller Critical Temperature(T_(c))dataset containing∼300 compounds.Comparatively,there is a significant improvement in the performance of the transfer learning model over the traditional deep learning(DL)model in predicting Tc.Subsequently,the transfer learning model is applied to predict the properties of approximately 150k compounds.Predictions are validated computationally using density functional theory(DFT)calculations based on lattice dynamics-related theory.Moreover,to demonstrate the extended predictive capability of the transfer learning model for new materials,a pool of virtual compounds derived from prototype crystal structures from the Materials Project(MP)database is generated.T_(c) predictions are obtained for∼3600 virtual compounds,which underwent screening for electroneutrality and thermodynamic stability.An Extra Trees-based model is trained to utilize E_(hull)values to obtain thermodynamically stable materials,employing a dataset containing Ehull values for approximately 150k materials for training.Materials with Ehull values exceeding 5 meV/atom were filtered out,resulting in a refined list of potential Mg-based superconductors.This study showcases the effectiveness of transfer learning in predicting superconducting properties and highlights its potential for accelerating the discovery of Mg-based materials in the field of superconductivity.

Influence of O-O formation pathways and charge transfer mediator on lipid bilayer membrane-like photoanodes for water oxidation

Inspired by the function of crucial components in photosystemⅡ(PSⅡ),electrochemical and dyesensitized photoelectrochemical(DSPEC)water oxidation devices were constructed by the selfassembly of well-designed amphipathic Ru(bda)-based catalysts(bda=2,2'-bipyrdine-6,6'-dicarbonoxyl acid)and aliphatic chain decorated electrode surfaces,forming lipid bilayer membrane(LBM)-like structures.The Ru(bda)catalysts on electrode-supported LBM films demonstrated remarkable water oxidation performance with different O-O formation mechanisms.However,compared to the slow charge transfer process,the O-O formation pathways did not determine the PEC water oxidation efficiency of the dyesensitized photoanodes,and the different reaction rates for similar catalysts with different catalytic paths did not determine the PEC performance of the DSPECs.Instead,charge transfer plays a decisive role in the PEC water oxidation rate.When an indolo[3,2-b]carbazole derivative was introduced between the Ru(bda)catalysts and aliphatic chain-modified photosensitizer in LBM films,serving as a charge transfer mediator for the tyrosine-histidine pair in PSⅡ,the PEC water oxidation performance of the corresponding photoanodes was dramatically enhanced.

Interpolated Bumpless Transfer Control for Asynchronously Switched Linear Systems

This paper revisits the problem of bumpless transfer control(BTC) for discrete-time nondeterministic switched linear systems. The general case of asynchronous switching is considered for the first time in the field of BTC for switched systems. A new approach called interpolated bumpless transfer control(IBTC) is proposed, where the bumpless transfer controllers are formulated with the combination of the two adjacent modedependent controller gains, and are interpolated for finite steps once the switching is detected. In contrast with the existing approaches, IBTC does not necessarily run through the full interval of subsystems, as well as possesses the time-varying controller gains(with more flexibility and less conservatism) achieved from a control synthesis allowing for the stability and other performance of the whole switched system. Sufficient conditions ensuring stability and H_(∞) performance of the underlying system by IBTC are developed, and numerical examples verify the theoretical findings.

IDS-INT:Intrusion detection system using transformer-based transfer learning for imbalanced network traffic

A network intrusion detection system is critical for cyber security against llegitimate attacks.In terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a subcategory of attack,host information,malicious scripts,etc.In terms of network perspectives,network traffic may contain an imbalanced number of harmful attacks when compared to normal traffic.It is challenging to identify a specific attack due to complex features and data imbalance issues.To address these issues,this paper proposes an Intrusion Detection System using transformer-based transfer learning for Imbalanced Network Traffic(IDS-INT).IDS-INT uses transformer-based transfer learning to learn feature interactions in both network feature representation and imbalanced data.First,detailed information about each type of attack is gathered from network interaction descriptions,which include network nodes,attack type,reference,host information,etc.Second,the transformer-based transfer learning approach is developed to learn detailed feature representation using their semantic anchors.Third,the Synthetic Minority Oversampling Technique(SMOTE)is implemented to balance abnormal traffic and detect minority attacks.Fourth,the Convolution Neural Network(CNN)model is designed to extract deep features from the balanced network traffic.Finally,the hybrid approach of the CNN-Long Short-Term Memory(CNN-LSTM)model is developed to detect different types of attacks from the deep features.Detailed experiments are conducted to test the proposed approach using three standard datasets,i.e.,UNsWNB15,CIC-IDS2017,and NSL-KDD.An explainable AI approach is implemented to interpret the proposed method and develop a trustable model.

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.