NOVEL SYNTHESIS OF POLYARYLENESULFONIUM CATIONS THROUGH A MULTI-ELECTRON TRANSFER PROCESS

The oxidative polymerization of aryl sulfoxides provides a novel polysulfo-nium compound, poly(methylsulfonio-1,4-phenylenethio-1,4-phenylene cation) in quantita-tive yield. The polymerization proceeds efficiently in an acidic solution under atmosphericconditions. Oxygen, chemical and electrochemical oxidations are available. Vanadyl acety-lacetonate and cerium ammonium nitrate act as an effective catalyst for the oxygen ox-idative polymerization. The polymerization mechanism involves multielectron oxidation ofthe sulfides followed by successive electrophilic substitution. The resulting polyarylenesul-fonium cations are useful as a soluble precursor for the synthesis of high molecular weight(M_w>10~5) poly(thio arylne)s.

Three-Stage Transfer Learning with AlexNet50 for MRI Image Multi-Class Classification with Optimal Learning Rate

In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological structures.MRI is particularly effective for detecting soft tissue anomalies.Traditionally,radiologists manually interpret these images,which can be labor-intensive and time-consuming due to the vast amount of data.To address this challenge,machine learning,and deep learning approaches can be utilized to improve the accuracy and efficiency of anomaly detection in MRI scans.This manuscript presents the use of the Deep AlexNet50 model for MRI classification with discriminative learning methods.There are three stages for learning;in the first stage,the whole dataset is used to learn the features.In the second stage,some layers of AlexNet50 are frozen with an augmented dataset,and in the third stage,AlexNet50 with an augmented dataset with the augmented dataset.This method used three publicly available MRI classification datasets:Harvard whole brain atlas(HWBA-dataset),the School of Biomedical Engineering of Southern Medical University(SMU-dataset),and The National Institute of Neuroscience and Hospitals brain MRI dataset(NINS-dataset)for analysis.Various hyperparameter optimizers like Adam,stochastic gradient descent(SGD),Root mean square propagation(RMS prop),Adamax,and AdamW have been used to compare the performance of the learning process.HWBA-dataset registers maximum classification performance.We evaluated the performance of the proposed classification model using several quantitative metrics,achieving an average accuracy of 98%.

Transfer RNA-derived small RNA serves as potential non-invasive diagnostic marker and a novel therapeutic target for acute pancreatitis

Transfer RNA(tRNA)-derived fragments,a new type of tRNA-derived small RNA(tsRNA),can be cleaved from tRNA by enzymes to regulate target gene expression at the transcriptional and translational levels.tsRNAs are not only degradation fragments but also have biological functions,including those in immune inflammation,metabolic disorders,and cell death.tsRNA dysregulation is closely associated with multiple diseases,including various cancers and acute pancreatitis(AP).AP is a common gastrointestinal disease,and its incidence increases annually.AP development is associated with tsRNAs,which regulate cell injury and induce inflammation,especially pyroptosis and ferroptosis.Notably,serum tRF36 has the potential to serve as a non-invasive diagnostic biomarker and leads to pancreatic acinar cell ferroptosis causing inflammation to promote AP.We show the characteristics of tsRNAs and their diagnostic value and function in AP,and discuss the potential opportunities and challenges of using tsRNAs in clinical applications and research.

Brachioradialis tendon transfer and palmaris longus tendon graft for thumb avulsion:A case report and review of literature

BACKGROUND Thumb replantation following complete traumatic avulsion requires complex techniques to restore function,especially in cases of avulsion at the level of the metacarpophalangeal joint(MCP I)and avulsion of the flexor pollicis longus(FPL)at the musculotendinous junction.Possible treatments include direct tendon suture or tendon transfer,most commonly from the ring finger.To optimize function and avoid donor finger complications,we performed thumb replantation with flexion restoration using brachioradialis(BR)tendon transfer with palmaris longus(PL)tendon graft.CASE SUMMARY A 20-year-old left-handed male was admitted for a complete traumatic left thumb amputation following an accident while sliding from the top of a handrail.The patient presented with skin and bone avulsion at the MCP I,avulsion of the FPL tendon at the musculotendinous junction(zone 5),avulsion of the extensor pollicis longus tendon(zone T3),and avulsion of the thumb’s collateral arteries and nerves.The patient was treated with two stage thumb repair.The first intervention consisted of thumb replantation with MCP I arthrodesis,resection of avulsed FPL tendon and implantation of a silicone tendon prosthesis.The second intervention consisted of PL tendon graft and BR tendon transfer.Follow-up at 10 months showed good outcomes with active interphalangeal flexion of 70°,grip strength of 45 kg,key pinch strength of 15 kg and two-point discrimination threshold of 4 mm.CONCLUSION Flexion restoration after complete thumb amputation with FPL avulsion at the musculotendinous junction can be achieved using BR tendon transfer with PL tendon graft.

Unlocking the multi-electron transfer reaction in NASICON-type cathode materials

The growing concern about scarcity and large-scale applications of lithium resources has attracted efforts to realize cost-effective phosphate-based cathode materials for next-generation Na-ion batteries(NIBs).In previous work,a series of materials(such as Na_(4)Fe_(3)(PO_(4))_(2)(P_(2)O_(7)),Na_(3)VCr(PO_(4))_(3),Na_(4)VMn(PO_(4))3,Na_(3)MnTi(PO_(4))_(3),Na_(3)MnZr(PO_(4))3,etc)with∼120 mAh g^(-1) specific capacity and high operating potential has been proposed.However,the mass ratio of the total transition metal in the above compounds is only∼22 wt%,which means that one-electron transfer for each transition metal shows a limited capacity(the mass ratio of Fe is 35.4 wt%in LiFePO_(4)).Therefore,a multi-electron transfer reaction is necessary to catch up to or go beyond the electrochemical performance of LiFePO_(4).This review summarizes the reported NASICON-type and other phosphate-based cathode materials.On the basis of the aforementioned experimental results,we pinpoint the multi-electron behavior of transition metals and shed light on designing rules for developing high-capacity cathodes in NIBs.

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.

Causal Analysis Between Rice Growth and Cadmium Accumulation and Transfer under Arbuscular Mycorrhizal Inoculation

Cadmium(Cd)contamination in rice has been a serious threat to human health.To investigate the effects of arbuscular mycorrhizal fungi(AMF)on the Cd translocation in rice,a controlled pot experiment was conducted.The results indicated that AMF significantly increased rice biomass,with an increase of up to 40.0%,particularly in root biomass by up to 68.4%.Notably,the number of prominent rice individuals also increased,and their plasticity was enhanced following AMF inoculation.AMF led to an increase in the net photosynthetic rate and antioxidant enzyme activity of rice.In the AMF treatment group,the Cd concentration in the rice roots was significantly higher(19.1%‒68.0%)compared with that in the control group.Conversely,the Cd concentration in the rice seeds was lower in the AMF treatment group,indicating that AMF facilitated the sequestration of Cd in rice roots and reduced Cd accumulation in the seeds.Path coefficients varied across different treatments,suggesting that AMF inoculation reduced the direct impact of soil Cd concentration on the total Cd accumulation in seeds.The translocation of Cd was consistently associated with simultaneous growth dilution and compensatory accumulation as a result of mycorrhizal effects.Our study quantitatively analyzed this process through path analysis and clarified the causal relationship between rice growth and Cd transfer under the influence of AMF.

Robust Information Hiding Based on Neural Style Transfer with Artificial Intelligence

This paper proposes an artificial intelligence-based robust information hiding algorithm to address the issue of confidential information being susceptible to noise attacks during transmission.The algorithm we designed aims to mitigate the impact of various noise attacks on the integrity of secret information during transmission.The method we propose involves encoding secret images into stylized encrypted images and applies adversarial transfer to both the style and content features of the original and embedded data.This process effectively enhances the concealment and imperceptibility of confidential information,thereby improving the security of such information during transmission and reducing security risks.Furthermore,we have designed a specialized attack layer to simulate real-world attacks and common noise scenarios encountered in practical environments.Through adversarial training,the algorithm is strengthened to enhance its resilience against attacks and overall robustness,ensuring better protection against potential threats.Experimental results demonstrate that our proposed algorithm successfully enhances the concealment and unknowability of secret information while maintaining embedding capacity.Additionally,it ensures the quality and fidelity of the stego image.The method we propose not only improves the security and robustness of information hiding technology but also holds practical application value in protecting sensitive data and ensuring the invisibility of confidential information.

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.

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.

Coherent optical frequency transfer via 972-km fiber link

We demonstrate coherent optical frequency dissemination over a distance of 972 km by cascading two spans where the phase noise is passively compensated for.Instead of employing a phase discriminator and a phase locking loop in the conventional active phase control scheme,the passive phase noise cancellation is realized by feeding double-trip beat-note frequency to the driver of the acoustic optical modulator at the local site.This passive scheme exhibits fine robustness and reliability,making it suitable for long-distance and noisy fiber links.An optical regeneration station is used in the link for signal amplification and cascaded transmission.The phase noise cancellation and transfer instability of the 972-km link is investigated,and transfer instability of 1.1×10^(-19)at 10^(4)s is achieved.This work provides a promising method for realizing optical frequency distribution over thousands of kilometers by using fiber links.

Label Recovery and Trajectory Designable Network for Transfer Fault Diagnosis of Machines With Incorrect Annotation

The success of deep transfer learning in fault diagnosis is attributed to the collection of high-quality labeled data from the source domain.However,in engineering scenarios,achieving such high-quality label annotation is difficult and expensive.The incorrect label annotation produces two negative effects:1)the complex decision boundary of diagnosis models lowers the generalization performance on the target domain,and2)the distribution of target domain samples becomes misaligned with the false-labeled samples.To overcome these negative effects,this article proposes a solution called the label recovery and trajectory designable network(LRTDN).LRTDN consists of three parts.First,a residual network with dual classifiers is to learn features from cross-domain samples.Second,an annotation check module is constructed to generate a label anomaly indicator that could modify the abnormal labels of false-labeled samples in the source domain.With the training of relabeled samples,the complexity of diagnosis model is reduced via semi-supervised learning.Third,the adaptation trajectories are designed for sample distributions across domains.This ensures that the target domain samples are only adapted with the pure-labeled samples.The LRTDN is verified by two case studies,in which the diagnosis knowledge of bearings is transferred across different working conditions as well as different yet related machines.The results show that LRTDN offers a high diagnosis accuracy even in the presence of incorrect annotation.

The regulation of ferrocene-based catalysts on heat transfer in highpressure combustion of ammonium perchlorate/hydroxyl-terminated polybutadiene/aluminum composite propellants

The regulation of the burning rate pressure exponent for the ammonium perchlorate/hydroxylterminated polybutadiene/aluminum(AP/HTPB/Al)composite propellants under high pressures is a crucial step for its application in high-pressure solid rocket motors.In this work,the combustion characteristics of AP/HTPB/Al composite propellants containing ferrocene-based catalysts were investigated,including the burning rate,thermal behavior,the local heat transfer,and temperature profile in the range of 7-28 MPa.The results showed that the exponent breaks were still observed in the propellants after the addition of positive catalysts(Ce-Fc-MOF),the burning rate inhibitor((Ferrocenylmethyl)trimethylammonium bromide,Fc Br)and the mixture of Fc Br/catocene(GFP).However,the characteristic pressure has increased,and the exponent decreased from 1.14 to 0.66,0.55,and 0.48 when the addition of Ce-FcMOF,Fc Br and Fc Br/GFP in the propellants.In addition,the temperature in the first decomposition stage was increased by 7.50℃ and 11.40℃ for the AP/Fc Br mixture and the AP/Fc Br/GFP mixture,respectively,compared to the pure AP.On the other hand,the temperature in the second decomposition stage decreased by 48.30℃ and 81.70℃ for AP/Fc Br and AP/Fc Br/GFP mixtures,respectively.It was also found that Fc Br might generate ammonia to cover the AP surface.In this case,a reaction between the methyl in Fc Br and perchloric acid caused more ammonia to appear at the AP surface,resulting in the suppression of ammonia desorption.In addition,the coarse AP particles on the quenched surface were of a concave shape relative to the binder matrix under low and high pressures when the catalysts were added.In the process,the decline at the AP/HTPB interface was only exhibited in the propellant with the addition of Ce-Fc-MOF.The ratio of the gas-phase temperature gradient of the propellants containing catalysts was reduced significantly below and above the characteristic pressure,rather than 3.6 times of the difference in the blank propellant.Overall,the obtained results demonstrated that the pressure exponent could be effectively regulated and controlled by adjusting the propellant local heat and mass transfer under high and low pressures.

Reliability analysis of slope stability by neural network,principal component analysis,and transfer learning techniques

The prediction of slope stability is considered as one of the critical concerns in geotechnical engineering.Conventional stochastic analysis with spatially variable slopes is time-consuming and highly computation-demanding.To assess the slope stability problems with a more desirable computational effort,many machine learning(ML)algorithms have been proposed.However,most ML-based techniques require that the training data must be in the same feature space and have the same distribution,and the model may need to be rebuilt when the spatial distribution changes.This paper presents a new ML-based algorithm,which combines the principal component analysis(PCA)-based neural network(NN)and transfer learning(TL)techniques(i.e.PCAeNNeTL)to conduct the stability analysis of slopes with different spatial distributions.The Monte Carlo coupled with finite element simulation is first conducted for data acquisition considering the spatial variability of cohesive strength or friction angle of soils from eight slopes with the same geometry.The PCA method is incorporated into the neural network algorithm(i.e.PCA-NN)to increase the computational efficiency by reducing the input variables.It is found that the PCA-NN algorithm performs well in improving the prediction of slope stability for a given slope in terms of the computational accuracy and computational effort when compared with the other two algorithms(i.e.NN and decision trees,DT).Furthermore,the PCAeNNeTL algorithm shows great potential in assessing the stability of slope even with fewer training data.

Stable multi-electron reaction stimulated by W doping VS_(4)for enhancing magnesium storage performance

Rechargeable magnesium batteries(RMBs)hold promise for offering higher volumetric energy density and safety features,attracting increasing research interest as the next post lithium-ion batteries.Developing high performance cathode material by inducing multi-electron reaction process as well as maintaining structural stability is the key to the development and application of RMBs.Herein,multielectron reaction occurred in VS_(4)by simple W doping strategy.W doping induces valence of partial V as V^(2+)and V^(3+)in VS_(4)structure,and then stimulates electrochemical reaction involving multi-electrons in 0.5%W-V-S.The flower-like microsphere morphology as well as rich S vacancies is also modulated by W doping to neutralize structure change in such multi-electron reaction process.The fabricated 0.5%W-V-S delivers higher specific capacity(149.3 m A h g^(-1)at 50 m A g^(-1),which is 1.6 times higher than that of VS_(4)),superior rate capability(76 mA h g^(-1)at 1000 mA g^(-1)),and stable cycling performance(1500cycles with capacity retention ratio of 93.8%).Besides that,pesudocapaticance-like contribution analysis as well as galvanostatic intermittent titration technique(GITT)further confirms the enhanced Mg^(2+)storage kinetics during such multi-electron involved electrochemical reaction process.Such discovery provides new insights into the designing of multi-electron reaction process in cathode as well as neutralizing structural change during such reaction for realizing superior electrochemical performance in energy storage devices.

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.

Tunneling Electrons Triggered Energy Transfer between Coherently Coupled Donor-Acceptor Molecules

Energy transfer is ubiquitous in natural and artificial lightharvesting systems,and coherent energy transfer,a highly efficient energy transfer process,has been accepted to play a vital role in such systems.However,the energy oscillation of coherent energy transfer is exceedingly difficult to capture because of its evanescence due to the interaction with a thermal environment.Here a microscopic quantum model is used to study the time evolution of electrons triggered energy transfer between coherently coupled donoracceptor molecules in scanning tunneling microscope(STM).A series of topics in the plasmonic nanocavity(PNC)coupled donor-acceptor molecules system are discussed,including resonant and nonresonant coherent energy transfer,dephasing assisted energy transfer,PNC coupling strength dependent energy transfer,Fano resonance of coherently coupled donor-acceptor molecules,and polariton-mediated energy transfer.

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.

New Game, New Stressors: Collegiate Men’s Basketball Coaches’ Mental Health in the NIL and Transfer Portal Era

Coaches experience considerable stress and anxiety in their pursuit of both team and individual success. Although there has been an increase in research aimed at promoting and managing the mental health of student-athletes, the mental well-being of coaches remains underexplored. Research indicates that the mental health profiles of coaches are similar to those of elite athletes [1] and should receive comparable attention. The introduction of Name, Image, and Likeness (NIL) legislation, along with the rise of the Transfer Portal in collegiate athletics, has presented student-athletes with new and unexpected options, which also bring novel challenges to coaches and potentially impact elite-level coaches’ mental health. This manuscript will identify three categories of stressors—performance, organizational, and self-related—that affect elite men’s college basketball coaches, highlighting how NIL and the Transfer Portal exacerbate those stressors and their impact on coaches’ mental health.