Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained promine...Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained prominence as a central focus of research in the field of fault diagnosis by strong fault feature extraction ability and end-to-end fault diagnosis efficiency.Recently,utilizing the respective advantages of convolution neural network(CNN)and Transformer in local and global feature extraction,research on cooperating the two have demonstrated promise in the field of fault diagnosis.However,the cross-channel convolution mechanism in CNN and the self-attention calculations in Transformer contribute to excessive complexity in the cooperative model.This complexity results in high computational costs and limited industrial applicability.To tackle the above challenges,this paper proposes a lightweight CNN-Transformer named as SEFormer for rotating machinery fault diagnosis.First,a separable multiscale depthwise convolution block is designed to extract and integrate multiscale feature information from different channel dimensions of vibration signals.Then,an efficient self-attention block is developed to capture critical fine-grained features of the signal from a global perspective.Finally,experimental results on the planetary gearbox dataset and themotor roller bearing dataset prove that the proposed framework can balance the advantages of robustness,generalization and lightweight compared to recent state-of-the-art fault diagnosis models based on CNN and Transformer.This study presents a feasible strategy for developing a lightweight rotating machinery fault diagnosis framework aimed at economical deployment.展开更多
Located in northern China,the Hetao Plain is an important agro-economic zone and population centre.The deterioration of local groundwater quality has had a serious impact on human health and economic development.Nowad...Located in northern China,the Hetao Plain is an important agro-economic zone and population centre.The deterioration of local groundwater quality has had a serious impact on human health and economic development.Nowadays,the groundwater vulnerability assessment(GVA)has become an essential task to identify the current status and development trend of groundwater quality.In this study,the Convolutional Neural Network(CNN)and Long Short-Term Memory(LSTM)models are integrated to realize the spatio-temporal prediction of regional groundwater vulnerability by introducing the Self-attention mechanism.The study firstly builds the CNN-LSTM modelwith self-attention(SA)mechanism and evaluates the prediction accuracy of the model for groundwater vulnerability compared to other common machine learning models such as Support Vector Machine(SVM),Random Forest(RF),and Extreme Gradient Boosting(XGBoost).The results indicate that the CNNLSTM model outperforms thesemodels,demonstrating its significance in groundwater vulnerability assessment.It can be posited that the predictions indicate an increased risk of groundwater vulnerability in the study area over the coming years.This increase can be attributed to the synergistic impact of global climate anomalies and intensified local human activities.Moreover,the overall groundwater vulnerability risk in the entire region has increased,evident fromboth the notably high value and standard deviation.This suggests that the spatial variability of groundwater vulnerability in the area is expected to expand in the future due to the sustained progression of climate change and human activities.The model can be optimized for diverse applications across regional environmental assessment,pollution prediction,and risk statistics.This study holds particular significance for ecological protection and groundwater resource management.展开更多
Lightweight convolutional neural networks(CNNs)have simple structures but struggle to comprehensively and accurately extract important semantic information from images.While attention mechanisms can enhance CNNs by le...Lightweight convolutional neural networks(CNNs)have simple structures but struggle to comprehensively and accurately extract important semantic information from images.While attention mechanisms can enhance CNNs by learning distinctive representations,most existing spatial and hybrid attention methods focus on local regions with extensive parameters,making them unsuitable for lightweight CNNs.In this paper,we propose a self-attention mechanism tailored for lightweight networks,namely the brief self-attention module(BSAM).BSAM consists of the brief spatial attention(BSA)and advanced channel attention blocks.Unlike conventional self-attention methods with many parameters,our BSA block improves the performance of lightweight networks by effectively learning global semantic representations.Moreover,BSAM can be seamlessly integrated into lightweight CNNs for end-to-end training,maintaining the network’s lightweight and mobile characteristics.We validate the effectiveness of the proposed method on image classification tasks using the Food-101,Caltech-256,and Mini-ImageNet datasets.展开更多
A healthy brain is vital to every person since the brain controls every movement and emotion.Sometimes,some brain cells grow unexpectedly to be uncontrollable and cancerous.These cancerous cells are called brain tumor...A healthy brain is vital to every person since the brain controls every movement and emotion.Sometimes,some brain cells grow unexpectedly to be uncontrollable and cancerous.These cancerous cells are called brain tumors.For diagnosed patients,their lives depend mainly on the early diagnosis of these tumors to provide suitable treatment plans.Nowadays,Physicians and radiologists rely on Magnetic Resonance Imaging(MRI)pictures for their clinical evaluations of brain tumors.These evaluations are time-consuming,expensive,and require expertise with high skills to provide an accurate diagnosis.Scholars and industrials have recently partnered to implement automatic solutions to diagnose the disease with high accuracy.Due to their accuracy,some of these solutions depend on deep-learning(DL)methodologies.These techniques have become important due to their roles in the diagnosis process,which includes identification and classification.Therefore,there is a need for a solid and robust approach based on a deep-learning method to diagnose brain tumors.The purpose of this study is to develop an intelligent automatic framework for brain tumor diagnosis.The proposed solution is based on a novel dense dynamic residual self-attention transfer adaptive learning fusion approach(NDDRSATALFA),carried over two implemented deep-learning networks:VGG19 and UNET to identify and classify brain tumors.In addition,this solution applies a transfer learning approach to exchange extracted features and data within the two neural networks.The presented framework is trained,validated,and tested on six public datasets of MRIs to detect brain tumors and categorize these tumors into three suitable classes,which are glioma,meningioma,and pituitary.The proposed framework yielded remarkable findings on variously evaluated performance indicators:99.32%accuracy,98.74%sensitivity,98.89%specificity,99.01%Dice,98.93%Area Under the Curve(AUC),and 99.81%F1-score.In addition,a comparative analysis with recent state-of-the-art methods was performed and according to the comparative analysis,NDDRSATALFA shows an admirable level of reliability in simplifying the timely identification of diverse brain tumors.Moreover,this framework can be applied by healthcare providers to assist radiologists,pathologists,and physicians in their evaluations.The attained outcomes open doors for advanced automatic solutions that improve clinical evaluations and provide reasonable treatment plans.展开更多
Medical image analysis based on deep learning has become an important technical requirement in the field of smart healthcare.In view of the difficulties in collaborative modeling of local details and global features i...Medical image analysis based on deep learning has become an important technical requirement in the field of smart healthcare.In view of the difficulties in collaborative modeling of local details and global features in multimodal image analysis of ophthalmology,as well as the existence of information redundancy in cross-modal data fusion,this paper proposes amultimodal fusion framework based on cross-modal collaboration and weighted attention mechanism.In terms of feature extraction,the framework collaboratively extracts local fine-grained features and global structural dependencies through a parallel dual-branch architecture,overcoming the limitations of traditional single-modality models in capturing either local or global information;in terms of fusion strategy,the framework innovatively designs a cross-modal dynamic fusion strategy,combining overlappingmulti-head self-attention modules with a bidirectional feature alignment mechanism,addressing the bottlenecks of low feature interaction efficiency and excessive attention fusion computations in traditional parallel fusion,and further introduces cross-domain local integration technology,which enhances the representation ability of the lesion area through pixel-level feature recalibration and optimizes the diagnostic robustness of complex cases.Experiments show that the framework exhibits excellent feature expression and generalization performance in cross-domain scenarios of ophthalmic medical images and natural images,providing a high-precision,low-redundancy fusion paradigm for multimodal medical image analysis,and promoting the upgrade of intelligent diagnosis and treatment fromsingle-modal static analysis to dynamic decision-making.展开更多
Deep Learning-based systems for Finger vein recognition have gained rising attention in recent years due to improved efficiency and enhanced security.The performance of existing CNN-based methods is limited by the pun...Deep Learning-based systems for Finger vein recognition have gained rising attention in recent years due to improved efficiency and enhanced security.The performance of existing CNN-based methods is limited by the puny generalization of learned features and deficiency of the finger vein image training data.Considering the concerns of existing methods,in this work,a simplified deep transfer learning-based framework for finger-vein recognition is developed using an EfficientNet model of deep learning with a self-attention mechanism.Data augmentation using various geometrical methods is employed to address the problem of training data shortage required for a deep learning model.The proposed model is tested using K-fold cross-validation on three publicly available datasets:HKPU,FVUSM,and SDUMLA.Also,the developed network is compared with other modern deep nets to check its effectiveness.In addition,a comparison of the proposed method with other existing Finger vein recognition(FVR)methods is also done.The experimental results exhibited superior recognition accuracy of the proposed method compared to other existing methods.In addition,the developed method proves to be more effective and less sophisticated at extracting robust features.The proposed EffAttenNet achieves an accuracy of 98.14%on HKPU,99.03%on FVUSM,and 99.50%on SDUMLA databases.展开更多
For image compression sensing reconstruction,most algorithms use the method of reconstructing image blocks one by one and stacking many convolutional layers,which usually have defects of obvious block effects,high com...For image compression sensing reconstruction,most algorithms use the method of reconstructing image blocks one by one and stacking many convolutional layers,which usually have defects of obvious block effects,high computational complexity,and long reconstruction time.An image compressed sensing reconstruction network based on self-attention mechanism(SAMNet)was proposed.For the compressed sampling,self-attention convolution was designed,which was conducive to capturing richer features,so that the compressed sensing measurement value retained more image structure information.For the reconstruction,a self-attention mechanism was introduced in the convolutional neural network.A reconstruction network including residual blocks,bottleneck transformer(BoTNet),and dense blocks was proposed,which strengthened the transfer of image features and reduced the amount of parameters dramatically.Under the Set5 dataset,when the measurement rates are 0.01,0.04,0.10,and 0.25,the average peak signal-to-noise ratio(PSNR)of SAMNet is improved by 1.27,1.23,0.50,and 0.15 dB,respectively,compared to the CSNet+.The running time of reconstructing a 256×256 image is reduced by 0.1473,0.1789,0.2310,and 0.2524 s compared to ReconNet.Experimental results showed that SAMNet improved the quality of reconstructed images and reduced the reconstruction time.展开更多
As the complexity of scientific satellite missions increases,the requirements for their magnetic fields,magnetic field fluctuations,and even magnetic field gradients and variations become increasingly stringent.Additi...As the complexity of scientific satellite missions increases,the requirements for their magnetic fields,magnetic field fluctuations,and even magnetic field gradients and variations become increasingly stringent.Additionally,there is a growing need to address the alternating magnetic fields produced by the spacecraft itself.This paper introduces a novel modeling method for spacecraft magnetic dipoles using an integrated self-attention mechanism and a transformer combined with Kolmogorov-Arnold Networks.The self-attention mechanism captures correlations among globally sparse data,establishing dependencies b.etween sparse magnetometer readings.Concurrently,the Kolmogorov-Arnold Network,proficient in modeling implicit numerical relationships between data features,enhances the ability to learn subtle patterns.Comparative experiments validate the capability of the proposed method to precisely model magnetic dipoles,achieving maximum Root Mean Square Errors of 24.06 mA·m^(2)and 0.32 cm for size and location modeling,respectively.The spacecraft magnetic model established using this method accurately computes magnetic fields and alternating magnetic fields at designated surfaces or points.This approach facilitates the rapid and precise construction of individual and complete spacecraft magnetic models,enabling the verification of magnetic specifications from the spacecraft design phase.展开更多
The development of deep learning has made non-biochemical methods for molecular property prediction screening a reality,which can increase the experimental speed and reduce the experimental cost of relevant experiment...The development of deep learning has made non-biochemical methods for molecular property prediction screening a reality,which can increase the experimental speed and reduce the experimental cost of relevant experiments.There are currently two main approaches to representing molecules:(a)representing molecules by fixing molecular descriptors,and(b)representing molecules by graph convolutional neural networks.Currently,both of these Representative methods have achieved some results in their respective experiments.Based on past efforts,we propose a Dual Self-attention Fusion Message Neural Network(DSFMNN).DSFMNN uses a combination of dual self-attention mechanism and graph convolutional neural network.Advantages of DSFMNN:(1)The dual self-attention mechanism focuses not only on the relationship between individual subunits in a molecule but also on the relationship between the atoms and chemical bonds contained in each subunit.(2)On the directed molecular graph,a message delivery approach centered on directed molecular bonds is used.We test the performance of the model on eight publicly available datasets and compare the performance with several models.Based on the current experimental results,DSFMNN has superior performance compared to previous models on the datasets applied in this paper.展开更多
Currently,most trains are equipped with dedicated cameras for capturing pantograph videos.Pantographs are core to the high-speed-railway pantograph-catenary system,and their failure directly affects the normal operati...Currently,most trains are equipped with dedicated cameras for capturing pantograph videos.Pantographs are core to the high-speed-railway pantograph-catenary system,and their failure directly affects the normal operation of high-speed trains.However,given the complex and variable real-world operational conditions of high-speed railways,there is no real-time and robust pantograph fault-detection method capable of handling large volumes of surveillance video.Hence,it is of paramount importance to maintain real-time monitoring and analysis of pantographs.Our study presents a real-time intelligent detection technology for identifying faults in high-speed railway pantographs,utilizing a fusion of self-attention and convolution features.We delved into lightweight multi-scale feature-extraction and fault-detection models based on deep learning to detect pantograph anomalies.Compared with traditional methods,this approach achieves high recall and accuracy in pantograph recognition,accurately pinpointing issues like discharge sparks,pantograph horns,and carbon pantograph-slide malfunctions.After experimentation and validation with actual surveillance videos of electric multiple-unit train,our algorithmic model demonstrates real-time,high-accuracy performance even under complex operational conditions.展开更多
Brain tumor identification is a challenging task in neuro-oncology.The brain’s complex anatomy makes it a crucial part of the central nervous system.Accurate tumor classification is crucial for clinical diagnosis and...Brain tumor identification is a challenging task in neuro-oncology.The brain’s complex anatomy makes it a crucial part of the central nervous system.Accurate tumor classification is crucial for clinical diagnosis and treatment planning.This research presents a significant advancement in the multi-classification of brain tumors.This paper proposed a novel architecture that integrates Enhanced ResNeXt 101_32×8d,a Convolutional Neural Network(CNN)with a multi-head self-attention(MHSA)mechanism.This combination harnesses the strengths of the feature extraction,feature representation by CNN,and long-range dependencies by MHSA.Magnetic Resonance Imaging(MRI)datasets were employed to check the effectiveness of the proposed architecture.The first dataset(DS-1,Msoud)included four brain tumor classes,and the second dataset(DS-2)contained seven brain tumor classes.This methodology effectively distinguished various tumor classes,achieving high accuracies of 99.75% on DS-1 and 98.80% on DS-2.These impressive results indicate the superior performance and adaptability of our model for multiclass brain tumor classification.Evaluationmetrics such as accuracy,precision,recall,F1 score,and ROC(receiver operating characteristic)curve were utilized to comprehensively evaluate model validity.The performance results showed that the model is well-suited for clinical applications,with reduced errors and high accuracy.展开更多
Introduction:Accurate prediction of protocadherin 8(PCDH8)gene expression status from whole-slide images(WSIs)is critical for thyroid cancer diagnosis and prognosis,as PCDH8 overexpression is associated with tumor agg...Introduction:Accurate prediction of protocadherin 8(PCDH8)gene expression status from whole-slide images(WSIs)is critical for thyroid cancer diagnosis and prognosis,as PCDH8 overexpression is associated with tumor aggressiveness and poor outcomes.Existing methods for PCDH8 detection are often costly,time-consuming,or require specialized expertise.To address these limitations,we developed a novel depth-wise separable residual neural network(DSRNet)for noninvasive PCDH8 status prediction directly from WSIs.Materials and methods:We collected 403 thyroid cancer WSIs from The Cancer Genome Atlas(TCGA),with PCDH8 expression status classified as high or low based on median expression values.Each WSI was divided into 512×512 pixel tiles,with the top 100 non-white tiles selected per slide.DSRNet integrates depth-wise separable convolutions,residual connections,and a deformable convolutional pyramid pooling module to efficiently capture multiscale and long-range features in gigapixel WSIs.The model was trained using tenfold cross-validation.Results:DSRNet achieved state-of-the-art performance with 92.76%accuracy,91.92%precision,92.69%recall,and 0.93 area under the curve on the thyroid cancer dataset(TCGA-THCA),significantly outperforming leading convolutional neural networks and Transformer models.Ablation studies confirmed the contributions of each component,and attention visualization showed that DSRNet focuses on biologically relevant regions.The model also generalized well to a breast cancer dataset(TCGA-BRCA),achieving 89.13%accuracy.Conclusions:We developed DSRNet,a deep learning-based model for predicting PCDH8 status directly from routine hematoxylin and eosin-stained pathological images.DSRNet combines the efficiency of convolutional operations with enhanced long-range dependency modeling,providing a noninvasive,accurate,and interpretable tool for auxiliary thyroid cancer diagnosis and prognosis.The results demonstrate its strong potential for clinical translation,though further multicenter validation is warranted.展开更多
This paper examines a model that combines vortex generators and leading-edge tubercles for controlling the laminar separation bubble(LSB)over an airfoil at low Reynolds numbers(Re).This new concept of passive flow con...This paper examines a model that combines vortex generators and leading-edge tubercles for controlling the laminar separation bubble(LSB)over an airfoil at low Reynolds numbers(Re).This new concept of passive flow control technique utilizing a tubercle and vortex generator(VG)close to the leading edge was analyzed numerically for a NACA0015 airfoil.In this study,the Shear Stress Transport(SST)turbulence model was employed in the numerical modelling.Numerical modelling was completed using the ANSYS-Fluent 18.2 solver.Analyses were conducted to investigate the flow pattern and understand the underlying LSB control phenomena that enabled the new passive flow control method to provide this significant performance benefit.The findings indicated that the new concept of passive flow control technique suppressed the formation of an LSB at the suction surface of the NACA0015 airfoil,resulting in a higher lift coefficient and improved aerodynamic performance.Improvements in LSB dynamics and aerodynamic performance through the passive flow control method lead to increased energy output and enhanced stability.展开更多
With the legislative development,the organic and inorganic composition separation has become the primary requirement for sewer sediment disposal,however the relevant technology has been rarely reported and the driving...With the legislative development,the organic and inorganic composition separation has become the primary requirement for sewer sediment disposal,however the relevant technology has been rarely reported and the driving mechanism was still unclear.In this study,direct disintegration of biopolymers and indirect broken of connection point were investigated on the hydrolysis and component separation.Three typical sewer sediment treatment approaches,i.e.,alkaline,thermal and cation exchange treatments were proposed,which represented the hydrolysis-driving forces of chemical hydrolysis,physical hydrolysis and innovative cation bridging break-age.The results showed that the organic and inorganic separation rates of sewer sediment driven by alkaline,thermal and cation exchange treatments reached 21.26%,23.80%,and 19.56%-48.0%,respectively,compared to 4.43%in control.The secondary structure of proteins was disrupted,transitioning from𝛼α-helix to𝛽β-turn and random coil.Meanwhile,much biopolymers were released from solid to the liquid phase.From thermody-namic perspective,sewer sediment deposition was controlled by short-range interfacial interactions described by extended Derjaguin-Landau-Verwey-Overbeek theory.Additionally,the separation of organic and inorganic components was positively correlated with the thermodynamic parameters(Corr=0.87),highlighted the robust-ness of various driving forces.And the flocculation energy barriers were 2.40(alkaline),1.60 times(thermal),and 4.02–4.97 times(cation exchange)compared to control group.The findings revealed the contrition differ-ence of direct disintegration of gelatinous biopolymers and indirect breakage of composition connection sites in sediment composition separation,filling the critical gaps in understanding the specific mechanisms of sediment biopolymer disintegration and intermolecular connection breakage.展开更多
Lithium-sulfur batteries(LSBs)represent a next-generation energy storage technology,but widespread applications are restricted by the shuttle of lithium polysulfides(LiPSs).The rational design of separators has been d...Lithium-sulfur batteries(LSBs)represent a next-generation energy storage technology,but widespread applications are restricted by the shuttle of lithium polysulfides(LiPSs).The rational design of separators has been demonstrated to be one of the most efficient and cost-effective strategies to curb the shuttle effect,and tremendous research progress has been achieved.The efficiency of a separator depends on its interaction with LiPSs,which is governed by the surface energy and binding strength.Despite several review works that have been reported to advance the separators,most of them primarily focus on active material innovation and construction.The most crucial issues of surface binding energy have not been systematically reviewed,limiting the precise design of efficient separators.In this review,fundamentals related to surface energy and binding interactions with LiPSs are comprehensively analyzed and discussed.With surface binding and energy main lines,the advancements in separator engineering strategies are elaborately summarized and discussed.Moreover,techniques for evaluating affinity to LiPSs are thoroughly analyzed to avoid any ambiguities in measurement.Based on the research context,valuable research directions are suggested to construct efficient separators.This work provides guidelines to regulate the surface binding and energy of separators for high-performance LSBs.展开更多
Vehicle-induced response separation is a crucial issue in structural health monitoring(SHM).This paper proposes a block-wise sliding recursive wavelet transform algorithm to meet the real-time processing requirements ...Vehicle-induced response separation is a crucial issue in structural health monitoring(SHM).This paper proposes a block-wise sliding recursive wavelet transform algorithm to meet the real-time processing requirements of monitoring data.To extend the separation target from a fixed dataset to a continuously updating data stream,a block-wise sliding framework is first developed.This framework is further optimized considering the characteristics of real-time data streams,and its advantage in computational efficiency is theoretically demonstrated.During the decomposition and reconstruction processes,information from neighboring data blocks is fully utilized to reduce algorithmic complexity.In addition,a delay-setting strategy is introduced for each processing window to mitigate boundary effects,thereby balancing accuracy and efficiency.Simulated signal experiments are conducted to determine the optimal delay configuration and to verify the algorithm’s superior performance,achieving a lower Root Mean Square Error(RMSE)and only 0.0249 times the average computational time compared with the original algorithm.Furthermore,strain signals from the Lieshi River Bridge are employed to validate the method.The proposed algorithm successfully separates the static trend from vehicle-induced responses in real time across different sampling frequencies,demonstrating its effectiveness and applicability in real-time bridge monitoring.展开更多
Lignocellulosic biomass is the most abundant re-newable resource on Earth,boasting advan-tages such as wide avail-ability and negative car-bon emissions.Especial-ly,efficient separation of lignocellulose into cellu-lo...Lignocellulosic biomass is the most abundant re-newable resource on Earth,boasting advan-tages such as wide avail-ability and negative car-bon emissions.Especial-ly,efficient separation of lignocellulose into cellu-lose,hemicellulose and lignin,and realizing val-orization of these compo-nents are more responsive to the development needs of biomass refinery and the green chem-istry era.This review outlines the main components of lignocellulose and briefly summerizes their utilization in chemical raw materials and energy production.It mainly focused on cur-rent advances in component separation methods of lignocellulose by organic solvents,ionic liquids and deep eutectic solvents.The design of separation methods,understanding of sepa-ration mechanisms,and optimization of reaction systems in each method are highlighted in detail.Furthermore,the ongoing challenges and future directions based on mechanism and in-dustrialization are critically discussed.Our goal is to elucidate the separation mechanisms and principles of method design,providing guidance for the development of highly efficient com-ponent separation methods of lignocellulose.展开更多
To develop an efficient filter for removing white blood cells from whole blood,hydrophilic large-pore blended membranes of poly(vinylidene fluoride)(PVDF),polyvinyl pyrrolidone and polyethylene glycol,with good biocom...To develop an efficient filter for removing white blood cells from whole blood,hydrophilic large-pore blended membranes of poly(vinylidene fluoride)(PVDF),polyvinyl pyrrolidone and polyethylene glycol,with good biocompatibility,were prepared using the process of vapor-induced phase separation at various PVDF concentrations.The results demonstrated that at a PVDF mass concentration of 14%,the membrane had increased surface roughness,significantly enhanced hydrophilicity and wettability,and a wetting time of 8 s.The surface roughness of the membrane was also reduced to 31.637 nm.Furthermore,hemolysis rate and protein adsorption tests indicated that the blended membranes possessed excellent biocompatibility.They were reduced to 2.48%and 34.44μg·cm^(−2),respectively.The pore size of the fabricated membrane was relatively large,which reached approximately 8μm respectively,satisfying the filtration requirements.Lastly,the effects of different temperatures and multi-layered filters on leukocyte removal and the retention of red blood cells and platelets from whole blood were evaluated.The results revealed that the leukocyte removal rate was highest at 4℃ and with three membrane layers,the leukocyte removal rate was highest,reaching 98.36%,while the RBC and platelet content remained nearly unchanged compared with the original blood.This study provides a new approach for blood cell separation that is expected to play a significant role in medical fields such as blood transfusion demonstrating great potential for application and innovation.展开更多
Objective:To analyze the impact of maternal-infant separation on the physical and mental state of high-risk pregnancy patients and explore the clinical efficacy of targeted nursing interventions.Methods:A total of 80 ...Objective:To analyze the impact of maternal-infant separation on the physical and mental state of high-risk pregnancy patients and explore the clinical efficacy of targeted nursing interventions.Methods:A total of 80 high-risk pregnancy patients treated in our hospital from January 2023 to January 2024 were selected as the study subjects.These patients were randomly divided into an observation group and a control group(40 cases each)using a random number table.The control group received routine high-risk pregnancy nursing care,while the observation group received specialized maternal-infant separation nursing interventions in addition to routine care.The psychological and physiological states and nursing satisfaction of the two groups were compared before and after the intervention.Results:The SAS scores,SDS scores,and sleep quality scores of the observation group were significantly lower than those of the control group,with statistically significant differences(p<0.05).The incidence of postpartum hemorrhage in the observation group was significantly lower than that in the control group,and the initiation time of lactation was significantly earlier than that in the control group,with both differences being statistically significant(p<0.05).The nursing satisfaction of the observation group was significantly higher than that of the control group(80%vs.32/40),with a statistically significant difference(p<0.05).Conclusion:Maternal-infant separation exacerbates anxiety and depression in high-risk pregnancy patients,reduces sleep quality,increases the risk of postpartum hemorrhage,and delays the initiation of lactation.Specialized nursing interventions for maternal-infant separation can improve the physical and mental state of high-risk pregnancy patients,reduce the incidence of postpartum complications,and enhance nursing satisfaction,making them worthy of clinical application and promotion.展开更多
Zn-I_(2) batteries have emerged as promising next-generation energy storage systems owing to their inherent safety,environmental compatibility,rapid reaction kinetics,and small voltage hysteresis.Nevertheless,two crit...Zn-I_(2) batteries have emerged as promising next-generation energy storage systems owing to their inherent safety,environmental compatibility,rapid reaction kinetics,and small voltage hysteresis.Nevertheless,two critical challenges,i.e.,zinc dendrite growth and polyiodide shuttle effect,severely impede their commercial viability.To conquer these limitations,this study develops a multifunctional separator fabricated from straw-derived carboxylated nanocellulose,with its negative charge density further reinforced by anionic polyacrylamide incorporation.This modification simultaneously improves the separator’s mechanical properties,ionic conductivity,and Zn^(2+)ion transfer number.Remarkably,despite its ultrathin 20μm profile,the engineered separator demonstrates exceptional dendrite suppression and parasitic reaction inhibition,enabling Zn//Zn symmetric cells to achieve impressive cycle life(>1800 h at 2 m A cm^(-2)/2 m Ah cm^(-2))while maintaining robust performance even at ultrahigh areal capacities(25 m Ah cm^(-2)).Additionally,the separator’s anionic characteristic effectively blocks polyiodide migration through electrostatic repulsion,yielding Zn-I_(2) batteries with outstanding rate capability(120.7 m Ah g^(-1)at 5 A g^(-1))and excellent cyclability(94.2%capacity retention after 10,000 cycles).And superior cycling stability can still be achieved under zinc-deficient condition and pouch cell configuration.This work establishes a new paradigm for designing high-performance zinc-based energy storage systems through rational separator engineering.展开更多
基金supported by the National Natural Science Foundation of China(No.52277055).
文摘Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained prominence as a central focus of research in the field of fault diagnosis by strong fault feature extraction ability and end-to-end fault diagnosis efficiency.Recently,utilizing the respective advantages of convolution neural network(CNN)and Transformer in local and global feature extraction,research on cooperating the two have demonstrated promise in the field of fault diagnosis.However,the cross-channel convolution mechanism in CNN and the self-attention calculations in Transformer contribute to excessive complexity in the cooperative model.This complexity results in high computational costs and limited industrial applicability.To tackle the above challenges,this paper proposes a lightweight CNN-Transformer named as SEFormer for rotating machinery fault diagnosis.First,a separable multiscale depthwise convolution block is designed to extract and integrate multiscale feature information from different channel dimensions of vibration signals.Then,an efficient self-attention block is developed to capture critical fine-grained features of the signal from a global perspective.Finally,experimental results on the planetary gearbox dataset and themotor roller bearing dataset prove that the proposed framework can balance the advantages of robustness,generalization and lightweight compared to recent state-of-the-art fault diagnosis models based on CNN and Transformer.This study presents a feasible strategy for developing a lightweight rotating machinery fault diagnosis framework aimed at economical deployment.
基金supported by the National Key Research and Development Program of China(No.2021YFA0715900).
文摘Located in northern China,the Hetao Plain is an important agro-economic zone and population centre.The deterioration of local groundwater quality has had a serious impact on human health and economic development.Nowadays,the groundwater vulnerability assessment(GVA)has become an essential task to identify the current status and development trend of groundwater quality.In this study,the Convolutional Neural Network(CNN)and Long Short-Term Memory(LSTM)models are integrated to realize the spatio-temporal prediction of regional groundwater vulnerability by introducing the Self-attention mechanism.The study firstly builds the CNN-LSTM modelwith self-attention(SA)mechanism and evaluates the prediction accuracy of the model for groundwater vulnerability compared to other common machine learning models such as Support Vector Machine(SVM),Random Forest(RF),and Extreme Gradient Boosting(XGBoost).The results indicate that the CNNLSTM model outperforms thesemodels,demonstrating its significance in groundwater vulnerability assessment.It can be posited that the predictions indicate an increased risk of groundwater vulnerability in the study area over the coming years.This increase can be attributed to the synergistic impact of global climate anomalies and intensified local human activities.Moreover,the overall groundwater vulnerability risk in the entire region has increased,evident fromboth the notably high value and standard deviation.This suggests that the spatial variability of groundwater vulnerability in the area is expected to expand in the future due to the sustained progression of climate change and human activities.The model can be optimized for diverse applications across regional environmental assessment,pollution prediction,and risk statistics.This study holds particular significance for ecological protection and groundwater resource management.
文摘Lightweight convolutional neural networks(CNNs)have simple structures but struggle to comprehensively and accurately extract important semantic information from images.While attention mechanisms can enhance CNNs by learning distinctive representations,most existing spatial and hybrid attention methods focus on local regions with extensive parameters,making them unsuitable for lightweight CNNs.In this paper,we propose a self-attention mechanism tailored for lightweight networks,namely the brief self-attention module(BSAM).BSAM consists of the brief spatial attention(BSA)and advanced channel attention blocks.Unlike conventional self-attention methods with many parameters,our BSA block improves the performance of lightweight networks by effectively learning global semantic representations.Moreover,BSAM can be seamlessly integrated into lightweight CNNs for end-to-end training,maintaining the network’s lightweight and mobile characteristics.We validate the effectiveness of the proposed method on image classification tasks using the Food-101,Caltech-256,and Mini-ImageNet datasets.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,Saudi Arabia under Grant No.(GPIP:1055-829-2024).
文摘A healthy brain is vital to every person since the brain controls every movement and emotion.Sometimes,some brain cells grow unexpectedly to be uncontrollable and cancerous.These cancerous cells are called brain tumors.For diagnosed patients,their lives depend mainly on the early diagnosis of these tumors to provide suitable treatment plans.Nowadays,Physicians and radiologists rely on Magnetic Resonance Imaging(MRI)pictures for their clinical evaluations of brain tumors.These evaluations are time-consuming,expensive,and require expertise with high skills to provide an accurate diagnosis.Scholars and industrials have recently partnered to implement automatic solutions to diagnose the disease with high accuracy.Due to their accuracy,some of these solutions depend on deep-learning(DL)methodologies.These techniques have become important due to their roles in the diagnosis process,which includes identification and classification.Therefore,there is a need for a solid and robust approach based on a deep-learning method to diagnose brain tumors.The purpose of this study is to develop an intelligent automatic framework for brain tumor diagnosis.The proposed solution is based on a novel dense dynamic residual self-attention transfer adaptive learning fusion approach(NDDRSATALFA),carried over two implemented deep-learning networks:VGG19 and UNET to identify and classify brain tumors.In addition,this solution applies a transfer learning approach to exchange extracted features and data within the two neural networks.The presented framework is trained,validated,and tested on six public datasets of MRIs to detect brain tumors and categorize these tumors into three suitable classes,which are glioma,meningioma,and pituitary.The proposed framework yielded remarkable findings on variously evaluated performance indicators:99.32%accuracy,98.74%sensitivity,98.89%specificity,99.01%Dice,98.93%Area Under the Curve(AUC),and 99.81%F1-score.In addition,a comparative analysis with recent state-of-the-art methods was performed and according to the comparative analysis,NDDRSATALFA shows an admirable level of reliability in simplifying the timely identification of diverse brain tumors.Moreover,this framework can be applied by healthcare providers to assist radiologists,pathologists,and physicians in their evaluations.The attained outcomes open doors for advanced automatic solutions that improve clinical evaluations and provide reasonable treatment plans.
基金funded by the Ongoing Research Funding Program(ORF-2025-102),King Saud University,Riyadh,Saudi Arabiaby the Science and Technology Research Programof Chongqing Municipal Education Commission(Grant No.KJQN202400813)by the Graduate Research Innovation Project(Grant Nos.yjscxx2025-269-193 and CYS25618).
文摘Medical image analysis based on deep learning has become an important technical requirement in the field of smart healthcare.In view of the difficulties in collaborative modeling of local details and global features in multimodal image analysis of ophthalmology,as well as the existence of information redundancy in cross-modal data fusion,this paper proposes amultimodal fusion framework based on cross-modal collaboration and weighted attention mechanism.In terms of feature extraction,the framework collaboratively extracts local fine-grained features and global structural dependencies through a parallel dual-branch architecture,overcoming the limitations of traditional single-modality models in capturing either local or global information;in terms of fusion strategy,the framework innovatively designs a cross-modal dynamic fusion strategy,combining overlappingmulti-head self-attention modules with a bidirectional feature alignment mechanism,addressing the bottlenecks of low feature interaction efficiency and excessive attention fusion computations in traditional parallel fusion,and further introduces cross-domain local integration technology,which enhances the representation ability of the lesion area through pixel-level feature recalibration and optimizes the diagnostic robustness of complex cases.Experiments show that the framework exhibits excellent feature expression and generalization performance in cross-domain scenarios of ophthalmic medical images and natural images,providing a high-precision,low-redundancy fusion paradigm for multimodal medical image analysis,and promoting the upgrade of intelligent diagnosis and treatment fromsingle-modal static analysis to dynamic decision-making.
文摘Deep Learning-based systems for Finger vein recognition have gained rising attention in recent years due to improved efficiency and enhanced security.The performance of existing CNN-based methods is limited by the puny generalization of learned features and deficiency of the finger vein image training data.Considering the concerns of existing methods,in this work,a simplified deep transfer learning-based framework for finger-vein recognition is developed using an EfficientNet model of deep learning with a self-attention mechanism.Data augmentation using various geometrical methods is employed to address the problem of training data shortage required for a deep learning model.The proposed model is tested using K-fold cross-validation on three publicly available datasets:HKPU,FVUSM,and SDUMLA.Also,the developed network is compared with other modern deep nets to check its effectiveness.In addition,a comparison of the proposed method with other existing Finger vein recognition(FVR)methods is also done.The experimental results exhibited superior recognition accuracy of the proposed method compared to other existing methods.In addition,the developed method proves to be more effective and less sophisticated at extracting robust features.The proposed EffAttenNet achieves an accuracy of 98.14%on HKPU,99.03%on FVUSM,and 99.50%on SDUMLA databases.
基金supported by National Natural Science Foundation of China(Nos.61261016,61661025)Science and Technology Plan of Gansu Province(No.20JR10RA273).
文摘For image compression sensing reconstruction,most algorithms use the method of reconstructing image blocks one by one and stacking many convolutional layers,which usually have defects of obvious block effects,high computational complexity,and long reconstruction time.An image compressed sensing reconstruction network based on self-attention mechanism(SAMNet)was proposed.For the compressed sampling,self-attention convolution was designed,which was conducive to capturing richer features,so that the compressed sensing measurement value retained more image structure information.For the reconstruction,a self-attention mechanism was introduced in the convolutional neural network.A reconstruction network including residual blocks,bottleneck transformer(BoTNet),and dense blocks was proposed,which strengthened the transfer of image features and reduced the amount of parameters dramatically.Under the Set5 dataset,when the measurement rates are 0.01,0.04,0.10,and 0.25,the average peak signal-to-noise ratio(PSNR)of SAMNet is improved by 1.27,1.23,0.50,and 0.15 dB,respectively,compared to the CSNet+.The running time of reconstructing a 256×256 image is reduced by 0.1473,0.1789,0.2310,and 0.2524 s compared to ReconNet.Experimental results showed that SAMNet improved the quality of reconstructed images and reduced the reconstruction time.
基金supported by the National Key Research and Development Program of China(2020YFC2200901)。
文摘As the complexity of scientific satellite missions increases,the requirements for their magnetic fields,magnetic field fluctuations,and even magnetic field gradients and variations become increasingly stringent.Additionally,there is a growing need to address the alternating magnetic fields produced by the spacecraft itself.This paper introduces a novel modeling method for spacecraft magnetic dipoles using an integrated self-attention mechanism and a transformer combined with Kolmogorov-Arnold Networks.The self-attention mechanism captures correlations among globally sparse data,establishing dependencies b.etween sparse magnetometer readings.Concurrently,the Kolmogorov-Arnold Network,proficient in modeling implicit numerical relationships between data features,enhances the ability to learn subtle patterns.Comparative experiments validate the capability of the proposed method to precisely model magnetic dipoles,achieving maximum Root Mean Square Errors of 24.06 mA·m^(2)and 0.32 cm for size and location modeling,respectively.The spacecraft magnetic model established using this method accurately computes magnetic fields and alternating magnetic fields at designated surfaces or points.This approach facilitates the rapid and precise construction of individual and complete spacecraft magnetic models,enabling the verification of magnetic specifications from the spacecraft design phase.
文摘The development of deep learning has made non-biochemical methods for molecular property prediction screening a reality,which can increase the experimental speed and reduce the experimental cost of relevant experiments.There are currently two main approaches to representing molecules:(a)representing molecules by fixing molecular descriptors,and(b)representing molecules by graph convolutional neural networks.Currently,both of these Representative methods have achieved some results in their respective experiments.Based on past efforts,we propose a Dual Self-attention Fusion Message Neural Network(DSFMNN).DSFMNN uses a combination of dual self-attention mechanism and graph convolutional neural network.Advantages of DSFMNN:(1)The dual self-attention mechanism focuses not only on the relationship between individual subunits in a molecule but also on the relationship between the atoms and chemical bonds contained in each subunit.(2)On the directed molecular graph,a message delivery approach centered on directed molecular bonds is used.We test the performance of the model on eight publicly available datasets and compare the performance with several models.Based on the current experimental results,DSFMNN has superior performance compared to previous models on the datasets applied in this paper.
基金supported by the National Key R&D Program of China(No.2022YFB4301102).
文摘Currently,most trains are equipped with dedicated cameras for capturing pantograph videos.Pantographs are core to the high-speed-railway pantograph-catenary system,and their failure directly affects the normal operation of high-speed trains.However,given the complex and variable real-world operational conditions of high-speed railways,there is no real-time and robust pantograph fault-detection method capable of handling large volumes of surveillance video.Hence,it is of paramount importance to maintain real-time monitoring and analysis of pantographs.Our study presents a real-time intelligent detection technology for identifying faults in high-speed railway pantographs,utilizing a fusion of self-attention and convolution features.We delved into lightweight multi-scale feature-extraction and fault-detection models based on deep learning to detect pantograph anomalies.Compared with traditional methods,this approach achieves high recall and accuracy in pantograph recognition,accurately pinpointing issues like discharge sparks,pantograph horns,and carbon pantograph-slide malfunctions.After experimentation and validation with actual surveillance videos of electric multiple-unit train,our algorithmic model demonstrates real-time,high-accuracy performance even under complex operational conditions.
文摘Brain tumor identification is a challenging task in neuro-oncology.The brain’s complex anatomy makes it a crucial part of the central nervous system.Accurate tumor classification is crucial for clinical diagnosis and treatment planning.This research presents a significant advancement in the multi-classification of brain tumors.This paper proposed a novel architecture that integrates Enhanced ResNeXt 101_32×8d,a Convolutional Neural Network(CNN)with a multi-head self-attention(MHSA)mechanism.This combination harnesses the strengths of the feature extraction,feature representation by CNN,and long-range dependencies by MHSA.Magnetic Resonance Imaging(MRI)datasets were employed to check the effectiveness of the proposed architecture.The first dataset(DS-1,Msoud)included four brain tumor classes,and the second dataset(DS-2)contained seven brain tumor classes.This methodology effectively distinguished various tumor classes,achieving high accuracies of 99.75% on DS-1 and 98.80% on DS-2.These impressive results indicate the superior performance and adaptability of our model for multiclass brain tumor classification.Evaluationmetrics such as accuracy,precision,recall,F1 score,and ROC(receiver operating characteristic)curve were utilized to comprehensively evaluate model validity.The performance results showed that the model is well-suited for clinical applications,with reduced errors and high accuracy.
基金partially supported by the Henan Provincial Key Research and Promotion Projects(Grant No.:242102211012)the Ministry of Education in China Project of Humanities and Social Sciences(Grant No.:24YJCZH261).
文摘Introduction:Accurate prediction of protocadherin 8(PCDH8)gene expression status from whole-slide images(WSIs)is critical for thyroid cancer diagnosis and prognosis,as PCDH8 overexpression is associated with tumor aggressiveness and poor outcomes.Existing methods for PCDH8 detection are often costly,time-consuming,or require specialized expertise.To address these limitations,we developed a novel depth-wise separable residual neural network(DSRNet)for noninvasive PCDH8 status prediction directly from WSIs.Materials and methods:We collected 403 thyroid cancer WSIs from The Cancer Genome Atlas(TCGA),with PCDH8 expression status classified as high or low based on median expression values.Each WSI was divided into 512×512 pixel tiles,with the top 100 non-white tiles selected per slide.DSRNet integrates depth-wise separable convolutions,residual connections,and a deformable convolutional pyramid pooling module to efficiently capture multiscale and long-range features in gigapixel WSIs.The model was trained using tenfold cross-validation.Results:DSRNet achieved state-of-the-art performance with 92.76%accuracy,91.92%precision,92.69%recall,and 0.93 area under the curve on the thyroid cancer dataset(TCGA-THCA),significantly outperforming leading convolutional neural networks and Transformer models.Ablation studies confirmed the contributions of each component,and attention visualization showed that DSRNet focuses on biologically relevant regions.The model also generalized well to a breast cancer dataset(TCGA-BRCA),achieving 89.13%accuracy.Conclusions:We developed DSRNet,a deep learning-based model for predicting PCDH8 status directly from routine hematoxylin and eosin-stained pathological images.DSRNet combines the efficiency of convolutional operations with enhanced long-range dependency modeling,providing a noninvasive,accurate,and interpretable tool for auxiliary thyroid cancer diagnosis and prognosis.The results demonstrate its strong potential for clinical translation,though further multicenter validation is warranted.
基金the Scientific Research Projects Unit of Erciyes University under contract no:FDS-2022-11532 and FOA-2025-14773.
文摘This paper examines a model that combines vortex generators and leading-edge tubercles for controlling the laminar separation bubble(LSB)over an airfoil at low Reynolds numbers(Re).This new concept of passive flow control technique utilizing a tubercle and vortex generator(VG)close to the leading edge was analyzed numerically for a NACA0015 airfoil.In this study,the Shear Stress Transport(SST)turbulence model was employed in the numerical modelling.Numerical modelling was completed using the ANSYS-Fluent 18.2 solver.Analyses were conducted to investigate the flow pattern and understand the underlying LSB control phenomena that enabled the new passive flow control method to provide this significant performance benefit.The findings indicated that the new concept of passive flow control technique suppressed the formation of an LSB at the suction surface of the NACA0015 airfoil,resulting in a higher lift coefficient and improved aerodynamic performance.Improvements in LSB dynamics and aerodynamic performance through the passive flow control method lead to increased energy output and enhanced stability.
基金supported by Shaanxi Key Research and Development Program(No.2024SF-YBXM-546)the National Natural Science Foundation of China(No.52470161)the State Key Laboratory of Pollution Control and Resource Reuse Foundation(No.PCRRF21007).
文摘With the legislative development,the organic and inorganic composition separation has become the primary requirement for sewer sediment disposal,however the relevant technology has been rarely reported and the driving mechanism was still unclear.In this study,direct disintegration of biopolymers and indirect broken of connection point were investigated on the hydrolysis and component separation.Three typical sewer sediment treatment approaches,i.e.,alkaline,thermal and cation exchange treatments were proposed,which represented the hydrolysis-driving forces of chemical hydrolysis,physical hydrolysis and innovative cation bridging break-age.The results showed that the organic and inorganic separation rates of sewer sediment driven by alkaline,thermal and cation exchange treatments reached 21.26%,23.80%,and 19.56%-48.0%,respectively,compared to 4.43%in control.The secondary structure of proteins was disrupted,transitioning from𝛼α-helix to𝛽β-turn and random coil.Meanwhile,much biopolymers were released from solid to the liquid phase.From thermody-namic perspective,sewer sediment deposition was controlled by short-range interfacial interactions described by extended Derjaguin-Landau-Verwey-Overbeek theory.Additionally,the separation of organic and inorganic components was positively correlated with the thermodynamic parameters(Corr=0.87),highlighted the robust-ness of various driving forces.And the flocculation energy barriers were 2.40(alkaline),1.60 times(thermal),and 4.02–4.97 times(cation exchange)compared to control group.The findings revealed the contrition differ-ence of direct disintegration of gelatinous biopolymers and indirect breakage of composition connection sites in sediment composition separation,filling the critical gaps in understanding the specific mechanisms of sediment biopolymer disintegration and intermolecular connection breakage.
基金supported by the National Natural Science Foundation of China (52172228)the Natural Science Foundation of Fujian Province (2024J01475 and 2023J05127)
文摘Lithium-sulfur batteries(LSBs)represent a next-generation energy storage technology,but widespread applications are restricted by the shuttle of lithium polysulfides(LiPSs).The rational design of separators has been demonstrated to be one of the most efficient and cost-effective strategies to curb the shuttle effect,and tremendous research progress has been achieved.The efficiency of a separator depends on its interaction with LiPSs,which is governed by the surface energy and binding strength.Despite several review works that have been reported to advance the separators,most of them primarily focus on active material innovation and construction.The most crucial issues of surface binding energy have not been systematically reviewed,limiting the precise design of efficient separators.In this review,fundamentals related to surface energy and binding interactions with LiPSs are comprehensively analyzed and discussed.With surface binding and energy main lines,the advancements in separator engineering strategies are elaborately summarized and discussed.Moreover,techniques for evaluating affinity to LiPSs are thoroughly analyzed to avoid any ambiguities in measurement.Based on the research context,valuable research directions are suggested to construct efficient separators.This work provides guidelines to regulate the surface binding and energy of separators for high-performance LSBs.
基金the support of the Major Science and Technology Project of Yunnan Province,China(Grant No.202502AD080007)the National Natural Science Foundation of China(Grant No.52378288)。
文摘Vehicle-induced response separation is a crucial issue in structural health monitoring(SHM).This paper proposes a block-wise sliding recursive wavelet transform algorithm to meet the real-time processing requirements of monitoring data.To extend the separation target from a fixed dataset to a continuously updating data stream,a block-wise sliding framework is first developed.This framework is further optimized considering the characteristics of real-time data streams,and its advantage in computational efficiency is theoretically demonstrated.During the decomposition and reconstruction processes,information from neighboring data blocks is fully utilized to reduce algorithmic complexity.In addition,a delay-setting strategy is introduced for each processing window to mitigate boundary effects,thereby balancing accuracy and efficiency.Simulated signal experiments are conducted to determine the optimal delay configuration and to verify the algorithm’s superior performance,achieving a lower Root Mean Square Error(RMSE)and only 0.0249 times the average computational time compared with the original algorithm.Furthermore,strain signals from the Lieshi River Bridge are employed to validate the method.The proposed algorithm successfully separates the static trend from vehicle-induced responses in real time across different sampling frequencies,demonstrating its effectiveness and applicability in real-time bridge monitoring.
基金supported by National Key Technolo-gy R&D Program of China(2023YFD1701505)De-velopment Projects in Anhui Province(2022107020013).
文摘Lignocellulosic biomass is the most abundant re-newable resource on Earth,boasting advan-tages such as wide avail-ability and negative car-bon emissions.Especial-ly,efficient separation of lignocellulose into cellu-lose,hemicellulose and lignin,and realizing val-orization of these compo-nents are more responsive to the development needs of biomass refinery and the green chem-istry era.This review outlines the main components of lignocellulose and briefly summerizes their utilization in chemical raw materials and energy production.It mainly focused on cur-rent advances in component separation methods of lignocellulose by organic solvents,ionic liquids and deep eutectic solvents.The design of separation methods,understanding of sepa-ration mechanisms,and optimization of reaction systems in each method are highlighted in detail.Furthermore,the ongoing challenges and future directions based on mechanism and in-dustrialization are critically discussed.Our goal is to elucidate the separation mechanisms and principles of method design,providing guidance for the development of highly efficient com-ponent separation methods of lignocellulose.
基金The National Key Research and Development Program of China(2020YFC0862903)Supported by Jiangsu Future Membrane Technology Innovation Center(BM2021804)National Foreign Expert Program(H20240294).
文摘To develop an efficient filter for removing white blood cells from whole blood,hydrophilic large-pore blended membranes of poly(vinylidene fluoride)(PVDF),polyvinyl pyrrolidone and polyethylene glycol,with good biocompatibility,were prepared using the process of vapor-induced phase separation at various PVDF concentrations.The results demonstrated that at a PVDF mass concentration of 14%,the membrane had increased surface roughness,significantly enhanced hydrophilicity and wettability,and a wetting time of 8 s.The surface roughness of the membrane was also reduced to 31.637 nm.Furthermore,hemolysis rate and protein adsorption tests indicated that the blended membranes possessed excellent biocompatibility.They were reduced to 2.48%and 34.44μg·cm^(−2),respectively.The pore size of the fabricated membrane was relatively large,which reached approximately 8μm respectively,satisfying the filtration requirements.Lastly,the effects of different temperatures and multi-layered filters on leukocyte removal and the retention of red blood cells and platelets from whole blood were evaluated.The results revealed that the leukocyte removal rate was highest at 4℃ and with three membrane layers,the leukocyte removal rate was highest,reaching 98.36%,while the RBC and platelet content remained nearly unchanged compared with the original blood.This study provides a new approach for blood cell separation that is expected to play a significant role in medical fields such as blood transfusion demonstrating great potential for application and innovation.
文摘Objective:To analyze the impact of maternal-infant separation on the physical and mental state of high-risk pregnancy patients and explore the clinical efficacy of targeted nursing interventions.Methods:A total of 80 high-risk pregnancy patients treated in our hospital from January 2023 to January 2024 were selected as the study subjects.These patients were randomly divided into an observation group and a control group(40 cases each)using a random number table.The control group received routine high-risk pregnancy nursing care,while the observation group received specialized maternal-infant separation nursing interventions in addition to routine care.The psychological and physiological states and nursing satisfaction of the two groups were compared before and after the intervention.Results:The SAS scores,SDS scores,and sleep quality scores of the observation group were significantly lower than those of the control group,with statistically significant differences(p<0.05).The incidence of postpartum hemorrhage in the observation group was significantly lower than that in the control group,and the initiation time of lactation was significantly earlier than that in the control group,with both differences being statistically significant(p<0.05).The nursing satisfaction of the observation group was significantly higher than that of the control group(80%vs.32/40),with a statistically significant difference(p<0.05).Conclusion:Maternal-infant separation exacerbates anxiety and depression in high-risk pregnancy patients,reduces sleep quality,increases the risk of postpartum hemorrhage,and delays the initiation of lactation.Specialized nursing interventions for maternal-infant separation can improve the physical and mental state of high-risk pregnancy patients,reduce the incidence of postpartum complications,and enhance nursing satisfaction,making them worthy of clinical application and promotion.
基金the financial support from the Natural Science Foundation of Jiangsu Province(BK20231292)the Jiangsu Agricultural Science and Technology Innovation Fund(CX(24)3091)+6 种基金the Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX25_1429)the National Key R&D Program of China(2024YFE0109200)the Fundamental Research Funds for the Central Universities(No.2024300440)Guangdong Basic and Applied Basic Research Foundation(2025A1515011098)the National Natural Science Foundation of China(12464032)the Natural Science Foundation of Jiangxi Province(20232BAB201032)Ji'an Science and Technology Plan Project(2024H-100301)。
文摘Zn-I_(2) batteries have emerged as promising next-generation energy storage systems owing to their inherent safety,environmental compatibility,rapid reaction kinetics,and small voltage hysteresis.Nevertheless,two critical challenges,i.e.,zinc dendrite growth and polyiodide shuttle effect,severely impede their commercial viability.To conquer these limitations,this study develops a multifunctional separator fabricated from straw-derived carboxylated nanocellulose,with its negative charge density further reinforced by anionic polyacrylamide incorporation.This modification simultaneously improves the separator’s mechanical properties,ionic conductivity,and Zn^(2+)ion transfer number.Remarkably,despite its ultrathin 20μm profile,the engineered separator demonstrates exceptional dendrite suppression and parasitic reaction inhibition,enabling Zn//Zn symmetric cells to achieve impressive cycle life(>1800 h at 2 m A cm^(-2)/2 m Ah cm^(-2))while maintaining robust performance even at ultrahigh areal capacities(25 m Ah cm^(-2)).Additionally,the separator’s anionic characteristic effectively blocks polyiodide migration through electrostatic repulsion,yielding Zn-I_(2) batteries with outstanding rate capability(120.7 m Ah g^(-1)at 5 A g^(-1))and excellent cyclability(94.2%capacity retention after 10,000 cycles).And superior cycling stability can still be achieved under zinc-deficient condition and pouch cell configuration.This work establishes a new paradigm for designing high-performance zinc-based energy storage systems through rational separator engineering.
