Background:The traditional method of heterotopic abdominal heart transplantation(HTx)involves crossclamping the inferior vena cava,which inevitably leads to bilateral lower limb ischemia(LI).This study first aimed to ...Background:The traditional method of heterotopic abdominal heart transplantation(HTx)involves crossclamping the inferior vena cava,which inevitably leads to bilateral lower limb ischemia(LI).This study first aimed to investigate the impact of LI on renal function in rats subjected to unilateral nephrectomy(UNx).Second,a modified method utilizing renal vessel-assisted anastomosis in rats with left UNx was compared with the traditional method for abdominal HTx.Methods:Male Sprague-Dawley rats were utilized as subjects for both experimental phases.In experiment 1,the animals were divided into four groups:sham operation group;LI group-rats undergoing occlusion of the abdominal aorta and vena cava below the renal vessels;UNx group-rats with left UNx;and LI+UNx group.All operated animals were monitored for up to 7 days for biochemical markers,renal histopathology,and survival rates.In experiment 2,we introduced the renal vessel-assisted method as the experimental group and compared it against the traditional method as the control within rat heterotopic HTx models.We assessed operative characteristics,echocardiography results,histological findings,and graft survival.Results:First,LI resulted in acute kidney dysfunction characterized by a decrease in 7day survival rates and creatinine clearance rates in both the LI and LI+UNx groups compared to the sham operation and UNx groups.Particularly,histopathological damage in the kidney and liver did not exhibit significant effects during this period.Second,the implementation of the renal vessel-assisted method significantly reduced bleeding volume at suture sites and enhanced the 7day survival rate compared to the traditional method.Conclusion:Acute kidney injury was induced by LI postoperation in treated rats.The renal vessel-assisted method demonstrated its effectiveness as a superior alternative that mitigates complications associated with the traditional method.展开更多
Objectives This study aimed to evaluate the effectiveness of a Health Belief Model(HBM)-based electronic education program combined with individualized supervised exercise in improving exercise adherence and pregnancy...Objectives This study aimed to evaluate the effectiveness of a Health Belief Model(HBM)-based electronic education program combined with individualized supervised exercise in improving exercise adherence and pregnancy outcomes among women with gestational hypertension.Methods A randomized controlled trial was conducted from June 2024 to February 2025 at a tertiary hospital in Shenzhen,China.A total of 142 pregnant women diagnosed with gestational hypertension were randomly assigned to either an experimental group or a control group.The experimental group received routine antenatal care plus a 6-week HBM-based e-education intervention delivered via a mobile application and short messaging service(SMS)reminders,complemented by individualized in-person exercise guidance.The control group received routine antenatal care only.After the 6-week intervention,outcomes were assessed using the 6-min walk test,a disease knowledge and attitudes questionnaire,and the Pregnancy Exercise Self-Efficacy Scale.Primary outcomes included exercise adherence,blood pressure control,incidence of preeclampsia,and other pregnancy-related outcomes.Results A total of 129 participants completed the study(the intervention group[n=65],the control group[n=64]).At 6 weeks post-intervention,the experimental group demonstrated significantly greater improvements than the control group in exercise adherence,blood pressure control,preeclampsia incidence,disease-related knowledge and attitudes,and exercise self-efficacy(all P<0.05).Specifically,participants in the experimental group engaged in more frequent and longer-duration exercise sessions(P<0.05).Their blood pressure was maintained within a more stable and clinically optimal range(systolic:135.2±4.7 mmHg;diastolic:85.4±4.5 mmHg),which was significantly better than that of the control group(systolic:138.4±10.4 mmHg;diastolic:90.9±6.9 mmHg;P<0.05).The incidence of preeclampsia was also significantly lower in the experimental group(P<0.05).Additionally,scores for disease knowledge,attitudes,and exercise self-efficacy were higher in the experimental group(P<0.05).Within-group comparisons revealed that the experimental group showed significant improvements from baseline in exercise frequency,duration,total physical activity,and knowledge/attitude scores(P<0.05),whereas the control group showed no significant changes(P>0.05).Conclusion By embedding video-based education,real-time monitoring,and personalized support into routine prenatal care,this intervention facilitated positive behavioral changes in physical activity among pregnant women.The approach offers a scalable model for clinical nurses to delivering tailored remote exercise support for women with other pregnancy-related complications.展开更多
The rapid melting of Arctic sea ice poses significant risks to the safety of shipping routes.Accurate remote sensing data on sea ice concentration(SIC)is crucial for effective route planning of ships and ensuring navi...The rapid melting of Arctic sea ice poses significant risks to the safety of shipping routes.Accurate remote sensing data on sea ice concentration(SIC)is crucial for effective route planning of ships and ensuring navigational safety.Despite the availability of numerous SIC products in China,these datasets still lag behind mainstream international products in terms of data accuracy,spatiotemporal resolution,and time span.To enhance the accuracy of China's domestic SIC remote sensing data,this study used the SIC data derived from the passive microwave remote sensing dataset provided by the University of Bremen(BRM-SIC)as a reference to conduct a comprehensive evaluation and analysis of two additional SIC datasets:the dataset derived from the microwave radiation imager(MWRI)aboard the FY-3D satellite,provided by the National Satellite Meteorological Center(FY-SIC),and the dataset obtained through the DT-ASI algorithm from the microwave imager of the FY-3D satellite,provided by Ocean University of China(OUC-SIC).Based on the evaluation results,a TransUnet fusion correction model was developed.The performance of this model was then compared against Ordinary Least Squares(OLS),Random Forest(RF),and UNet correction models,through spatial and temporal analyses.Results indicate that,compared to FY-SIC data,the RMSE of the OUC-SIC data and the standard data is reduced by24.245%,while the R is increased by 12.516%.Overall,the accuracy of OUC-SIC data is superior to that of FY-SIC data.During the research period(2020–2022),the standard deviation(SD)and coefficient of variation(CV)of OUC-SIC were 3.877%and 10.582%,respectively,while those for FY-SIC were 7.836%and 7.982%,respectively.In the study area,compared with OUC-SIC data,FYSIC data exhibited a larger standard deviation of deviation and a smaller coefficient of variation of deviation across most sea areas.These results indicate that the OUC-SIC data exhibit better temporal and spatial stability,whereas the FY-SIC data show stronger relative dimensionless stability.Among the four correction models,all showed improvements over the original,unfused corrected data.The fusion corrections using the OLS,RF,UNet,and TransUnet models reduced RMSE by 5.563%,14.601%,42.927%,and48.316%,respectively.Correspondingly,R increased by 0.463%,1.176%,3.951%,and 4.342%,respectively.Among these models,TransUnet performed the best,effectively integrating the advantages of FY-SIC and OUC-SIC data and notably improving the overall accuracy and spatiotemporal stability of SIC data.展开更多
Rural domestic sewage treatment is critical for environmental protection.This study defines the spatial pattern of villages from the perspective of rural sewage treatment and develops an integrated decision-making sys...Rural domestic sewage treatment is critical for environmental protection.This study defines the spatial pattern of villages from the perspective of rural sewage treatment and develops an integrated decision-making system to propose a sewage treatment mode and scheme suitable for local conditions.By considering the village spatial layout and terrain factors,a decision tree model of residential density and terrain type was constructed with accuracies of 76.47%and 96.00%,respectively.Combined with binary classification probability unit regression,an appropriate sewage treatment mode for the village was determined with 87.00%accuracy.The Analytic Hierarchy Process(AHP),combined with the Technique for Order Preference(TOPSIS)by Similarity to an Ideal Solution model,formed the basis for optimal treatment process selection under different emission standards.Verification was conducted in 542 villages across three counties of the Inner Mongolia Autonomous Region,focusing on the standard effluent effect(0.3773),low investment cost(0.3196),and high standard effluent effect(0.5115)to determine the best treatment process for the same emission standard under different needs.The annual environmental and carbon emission benefits of sewage treatment in these villages were estimated.This model matches village density,geographic feature,and social development level,and provides scientific support and a theoretical basis for rural sewage treatment decision-making.展开更多
Slope units are divided according to the real topography and have clear geological characteristics,making them ideal units for evaluating the susceptibility to geological disasters.Based on the results of automaticall...Slope units are divided according to the real topography and have clear geological characteristics,making them ideal units for evaluating the susceptibility to geological disasters.Based on the results of automatically and manually corrected hydrological slope unit division,the Longhua District,Shenzhen City,Guangdong Province,was selected as the study area.A total of 15 influencing factors,namely Fluctuation,slope,slope aspect,curvature,topographic witness index(TWI),stream power index(SPI),topographic roughness index(TRI),annual average rainfall,distance to water system,engineering rock group,distance to fault,land use,normalized difference vegetation index(NDVI),nighttime light,and distance to road,were selected as evaluation indicators.The information volume model(IV)and random points were used to select non-geological disaster units,and then the random forest model(RF)was used to evaluate the susceptibility to geological disasters.The automatic slope unit and the hydrological slope unit were compared and analyzed in the random forest and information volume random forest models.The results show that the area under the curve(AUC)values of the automatic slope unit evaluation results are 0.931 for the IV-RF model and 0.716 for the RF model,which are 0.6%(IV-RF model)and 1.9%(RF model)higher than those for the hydrological slope unit.Based on a comparison of the evaluation methods based on the two types of slope units,the hydrological slope unit evaluation method based on manual correction is highly subjective,is complicated to operate,and has a low evaluation accuracy,whereas the evaluation method based on automatic slope unit division is efficient and accurate,is suitable for large-scale efficient geological disaster evaluation,and can better deal with the problem of geological disaster susceptibility evaluation.展开更多
Human motion modeling is a core technology in computer animation,game development,and humancomputer interaction.In particular,generating natural and coherent in-between motion using only the initial and terminal frame...Human motion modeling is a core technology in computer animation,game development,and humancomputer interaction.In particular,generating natural and coherent in-between motion using only the initial and terminal frames remains a fundamental yet unresolved challenge.Existing methods typically rely on dense keyframe inputs or complex prior structures,making it difficult to balance motion quality and plausibility under conditions such as sparse constraints,long-term dependencies,and diverse motion styles.To address this,we propose a motion generation framework based on a frequency-domain diffusion model,which aims to better model complex motion distributions and enhance generation stability under sparse conditions.Our method maps motion sequences to the frequency domain via the Discrete Cosine Transform(DCT),enabling more effective modeling of low-frequency motion structures while suppressing high-frequency noise.A denoising network based on self-attention is introduced to capture long-range temporal dependencies and improve global structural awareness.Additionally,a multi-objective loss function is employed to jointly optimize motion smoothness,pose diversity,and anatomical consistency,enhancing the realism and physical plausibility of the generated sequences.Comparative experiments on the Human3.6M and LaFAN1 datasets demonstrate that our method outperforms state-of-the-art approaches across multiple performance metrics,showing stronger capabilities in generating intermediate motion frames.This research offers a new perspective and methodology for human motion generation and holds promise for applications in character animation,game development,and virtual interaction.展开更多
Alzheimer’s disease is the most common cause of dementia.Although increasing evidence suggests that disruptions in lipid metabolism are closely associated with the disease,the overall profile of lipid and sterol chan...Alzheimer’s disease is the most common cause of dementia.Although increasing evidence suggests that disruptions in lipid metabolism are closely associated with the disease,the overall profile of lipid and sterol changes that occur in the brain during Alzheimer’s disease remains unclear.In this study,we compared brain tissues extracted from 32-week-old male wild-type mice and 5×FAD transgenic Alzheimer’s disease model mice,which carry mutations in the amyloid precursor protein(APP)and presenilin 1(PS1)genes.Using untargeted lipidomics and sterolomics techniques,we investigated the metabolic profiles of lipids,with a focus on sterols specifically,in three brain regions:cerebellum,hippocampus,and olfactory bulb.Our results revealed significant alterations in various lipids,particularly in the hippocampus and olfactory bulb,suggesting changes in energy levels in these regions.Further pathway analysis indicated notable disruptions in key metabolic processes,particularly those related to fatty acids and cell membrane components.Additionally,we observed decreased expression of 15 genes involved in lipid and sterol regulation.Collectively,these findings provide new insights into how imbalances in lipid and sterol metabolism may contribute to the progression of Alzheimer’s disease,highlighting potential metabolic pathways involved in the development of this debilitating disease.展开更多
The BOPPPS teaching model is a student-centered teaching model that has been widely applied in various teaching fields.This paper summarizes the overview of the BOPPPS teaching model,its application in emergency teach...The BOPPPS teaching model is a student-centered teaching model that has been widely applied in various teaching fields.This paper summarizes the overview of the BOPPPS teaching model,its application in emergency teaching and training,as well as its advantages and disadvantages,aiming to provide references for the further promotion and application of the BOPPPS teaching model in emergency education.展开更多
In the variance component estimation(VCE)of geodetic data,the problem of negative VCE is likely to occur.In the ordinary additive error model,there have been related studies to solve the problem of negative variance c...In the variance component estimation(VCE)of geodetic data,the problem of negative VCE is likely to occur.In the ordinary additive error model,there have been related studies to solve the problem of negative variance components.However,there is still no related research in the mixed additive and multiplicative random error model(MAMREM).Based on the MAMREM,this paper applies the nonnegative least squares variance component estimation(NNLS-VCE)algorithm to this model.The correlation formula and iterative algorithm of NNLS-VCE for MAMREM are derived.The problem of negative variance in VCE for MAMREM is solved.This paper uses the digital simulation example and the Digital Terrain Mode(DTM)to prove the proposed algorithm's validity.The experimental results demonstrated that the proposed algorithm can effectively correct the VCE in MAMREM when there is a negative VCE.展开更多
Large language models cross-domain named entity recognition task in the face of the scarcity of large language labeled data in a specific domain,due to the entity bias arising from the variation of entity information ...Large language models cross-domain named entity recognition task in the face of the scarcity of large language labeled data in a specific domain,due to the entity bias arising from the variation of entity information between different domains,which makes large language models prone to spurious correlations problems when dealing with specific domains and entities.In order to solve this problem,this paper proposes a cross-domain named entity recognition method based on causal graph structure enhancement,which captures the cross-domain invariant causal structural representations between feature representations of text sequences and annotation sequences by establishing a causal learning and intervention module,so as to improve the utilization of causal structural features by the large languagemodels in the target domains,and thus effectively alleviate the false entity bias triggered by the false relevance problem;meanwhile,through the semantic feature fusion module,the semantic information of the source and target domains is effectively combined.The results show an improvement of 2.47%and 4.12%in the political and medical domains,respectively,compared with the benchmark model,and an excellent performance in small-sample scenarios,which proves the effectiveness of causal graph structural enhancement in improving the accuracy of cross-domain entity recognition and reducing false correlations.展开更多
Systems with quenched disorder possess complex energy landscapes that are challenging to explore under conventional Monte Carlo methods.In this work,we implement an efficient entropy sampling scheme for accurate compu...Systems with quenched disorder possess complex energy landscapes that are challenging to explore under conventional Monte Carlo methods.In this work,we implement an efficient entropy sampling scheme for accurate computation of the entropy function in low-energy regions.The method is applied to the two-dimensional±J random-bond Ising model,where frustration is controlled by the fraction p of ferromagnetic bonds.We investigate the low-temperature paramagnetic–ferromagnetic phase boundary below the multicritical point at T_(N)=0.9530(4),P_(N)=0.89078(8),as well as the zerotemperature ferromagnetic–spin-glass transition.Finite-size scaling analysis reveals that the phase boundary for T<T_(N) exhibits reentrant behavior.By analyzing the evolution of the magnetizationresolved density of states g(E,M)and ground-state spin configurations against increasing frustration,we provide strong evidence that the zero-temperature transition is a mixed-order.Finite-size scaling conducted on the spin-glass side supports the validity of β=0,whereβis the magnetization exponent,with a correlation length exponentν=1.50(8).Our results provide new insights into the nature of the ferromagnetic-to-spin-glass phase transition in an extensively degenerate ground state.展开更多
Processing police incident data in public security involves complex natural language processing(NLP)tasks,including information extraction.This data contains extensive entity information—such as people,locations,and ...Processing police incident data in public security involves complex natural language processing(NLP)tasks,including information extraction.This data contains extensive entity information—such as people,locations,and events—while also involving reasoning tasks like personnel classification,relationship judgment,and implicit inference.Moreover,utilizing models for extracting information from police incident data poses a significant challenge—data scarcity,which limits the effectiveness of traditional rule-based and machine-learning methods.To address these,we propose TIPS.In collaboration with public security experts,we used de-identified police incident data to create templates that enable large language models(LLMs)to populate data slots and generate simulated data,enhancing data density and diversity.We then designed schemas to efficiently manage complex extraction and reasoning tasks,constructing a high-quality dataset and fine-tuning multiple open-source LLMs.Experiments showed that the fine-tuned ChatGLM-4-9B model achieved an F1 score of 87.14%,nearly 30%higher than the base model,significantly reducing error rates.Manual corrections further improved performance by 9.39%.This study demonstrates that combining largescale pre-trained models with limited high-quality domain-specific data can greatly enhance information extraction in low-resource environments,offering a new approach for intelligent public security applications.展开更多
Missiles provide long-range precision strike capabilities and have become a cornerstone of modern warfare.The contrail clouds formed by missile during their active flight phase present significant chal-lenges to high-...Missiles provide long-range precision strike capabilities and have become a cornerstone of modern warfare.The contrail clouds formed by missile during their active flight phase present significant chal-lenges to high-altitude environmental observation and target detection and tracking.Existing studies primarily focus on specific airspace regions,leaving critical gaps in understanding the effects of long dispersion times,wide altitude ranges,and variable atmospheric conditions on missile contrail clouds.To address these gaps,this article develops a numerical method based on the Lagrangian random walk model,which incorporates various velocity variation terms,including particle velocity caused by the difference of wind field,by the thermal motion of local gas molecules and by random collisions between contrail cloud particles to capture the influence of environmental wind fields,atmospheric conditions,and particle concentrations on the motion of contrail cloud particles.A general coordinate system aligned with the missile's flight trajectory is employed to represent particle distribution characteristics.The proposed method is in good agreement with the conducted experiments as well as with the available numerical simulations.The results demonstrate that the proposed model effectively simulates the dispersion state of contrail clouds,accurately reflecting the impact of large-scale wind field variations and altitude changes with high computational efficiency.Additionally,simulation results indicate that the increased distance between gas molecules in rarefied environments facilitates enhanced particle dispersion,while larger particles exhibit a faster dispersion rate due to their greater mass.展开更多
Border Gateway Protocol(BGP),as the standard inter-domain routing protocol,is a distance-vector dynamic routing protocol used for exchanging routing information between distributed Autonomous Systems(AS).BGP nodes,com...Border Gateway Protocol(BGP),as the standard inter-domain routing protocol,is a distance-vector dynamic routing protocol used for exchanging routing information between distributed Autonomous Systems(AS).BGP nodes,communicating in a distributed dynamic environment,face several security challenges,with trust being one of the most important issues in inter-domain routing.Existing research,which performs trust evaluation when exchanging routing information to suppress malicious routing behavior,cannot meet the scalability requirements of BGP nodes.In this paper,we propose a blockchain-based trust model for inter-domain routing.Our model achieves scalability by allowing the master node of an AS alliance to transmit the trust evaluation data of its member nodes to the blockchain.The BGP nodes can expedite the trust evaluation process by accessing a global view of other BGP nodes through the master node of their respective alliance.We incorporate security service evaluation before direct evaluation and indirect recommendations to assess the security services that BGP nodes provide for themselves and prioritize to guarantee their security of routing service.We forward the trust evaluation for neighbor discovery and prioritize the nodes with high trust as neighbor nodes to reduce the malicious exchange routing behavior.We use simulation software to simulate a real BGP environments and employ a comparative experimental research approach to demonstrate the performance evaluation of our trust model.Compared with the classical trust model,our trust model not only saves more storage overhead,but also provides higher security,especially reducing the impact of collusion attacks.展开更多
Detecting cyber attacks in networks connected to the Internet of Things(IoT)is of utmost importance because of the growing vulnerabilities in the smart environment.Conventional models,such as Naive Bayes and support v...Detecting cyber attacks in networks connected to the Internet of Things(IoT)is of utmost importance because of the growing vulnerabilities in the smart environment.Conventional models,such as Naive Bayes and support vector machine(SVM),as well as ensemble methods,such as Gradient Boosting and eXtreme gradient boosting(XGBoost),are often plagued by high computational costs,which makes it challenging for them to perform real-time detection.In this regard,we suggested an attack detection approach that integrates Visual Geometry Group 16(VGG16),Artificial Rabbits Optimizer(ARO),and Random Forest Model to increase detection accuracy and operational efficiency in Internet of Things(IoT)networks.In the suggested model,the extraction of features from malware pictures was accomplished with the help of VGG16.The prediction process is carried out by the random forest model using the extracted features from the VGG16.Additionally,ARO is used to improve the hyper-parameters of the random forest model of the random forest.With an accuracy of 96.36%,the suggested model outperforms the standard models in terms of accuracy,F1-score,precision,and recall.The comparative research highlights our strategy’s success,which improves performance while maintaining a lower computational cost.This method is ideal for real-time applications,but it is effective.展开更多
BACKGROUND Kidney transplantation is one of the most effective treatments for patients with end-stage renal disease.However,many regions face low deceased donor rates and limited ABO-compatible transplant availability...BACKGROUND Kidney transplantation is one of the most effective treatments for patients with end-stage renal disease.However,many regions face low deceased donor rates and limited ABO-compatible transplant availability,which increases reliance on living donors.These regional challenges necessitate the implementation of kidney paired donation(KPD)programs to overcome incompatibilities such as ABO mismatch or positive cross-matching,even when suitable and willing donors are available.AIM To evaluate the effectiveness of a single-center domino KPD model in both operational planning and clinical management processes and to assess its impact on clinical outcomes.METHODS Between April 2020 and January 2024,we retrospectively evaluated patients enrolled in our center’s domino kidney transplantation program.Donor-recipient pairs unable to proceed due to ABO incompatibility or positive cross-matching with their own living donors were included.Donors and recipients were assessed based on blood group compatibility,HLA tissue typing,and negative cross-match results.A specialized computer algorithm grouped patients into three-way,fourway,and five-way chains.All surgical procedures were performed on the same day at a single center.RESULTS A total of 169 kidney transplants were performed,forming 52 domino chains.These domino KPD transplants accounted for a notable proportion of our center’s overall transplant activity,which included both living donor kidney transplants and deceased donor transplants.Among these chains,the primary reasons for participation were ABO incompatibility(74%),positive cross-matching(10%),and the desire to improve HLA mismatch(16%).Improved HLA mismatch profiles and high graft survival(96%at 1 year,92%at 3 years)and patient survival(98%at 1 year,94%at 3 years)rates were observed,as well as low acute rejection episodes.CONCLUSION The single-center domino KPD model enhanced transplant opportunities for incompatible donor-recipient pairs while maintaining excellent clinical outcomes.By providing a framework that addresses regional challenges,improves operational efficiency,and optimizes clinical management,this model offers actionable insights to reduce waiting lists and improve patient outcomes.展开更多
The mortality rate of patients with abdominal aortic aneurysm(AAA) after rupture is extremely high,and this disease has become an important disease endangering the health of the Chinese population.Methods used to mode...The mortality rate of patients with abdominal aortic aneurysm(AAA) after rupture is extremely high,and this disease has become an important disease endangering the health of the Chinese population.Methods used to model AAA include intraluminal pressurized elastase infusion,chronic infusion of angiotensin Ⅱ(Ang Ⅱ) via an osmotic pump,periarterial application of calcium chloride,vascular grafting,and gene modification.AAA models induced by elastase and Ang Ⅱ are the two most widely used animal models.In the elastase-induced model,because intraluminal infusion is transient,with the cessation of initial stimulation,the aneurysm lesion tends to be stable and rarely ruptures.The model induced by Ang Ⅱ infusion often presents with a typical aortic dissection with a false lumen,whereas clinical AAA patients do not necessarily have dissection.Currently,the treatment of AAA in clinical practice remains endovascular,and there is a lack of pharmacological therapy,which is also related to the fact that the pathogenic mechanism has not been fully elucidated.Smoking,old age,male sex,and hypertension are the main risk factors for AAA,but these risk factors have not been fully investigated in the current modeling methods,which may affect the clinical translational application of research results based on animal models.Therefore,this article reviews the most commonly used AAA modeling methods,comments on their applications and limitations,and provides a perspective on the development of novel animal models.展开更多
基金The Youth Project of Tianjin Natural Science Foundation,Grant/Award Number:23JCQNJC01380。
文摘Background:The traditional method of heterotopic abdominal heart transplantation(HTx)involves crossclamping the inferior vena cava,which inevitably leads to bilateral lower limb ischemia(LI).This study first aimed to investigate the impact of LI on renal function in rats subjected to unilateral nephrectomy(UNx).Second,a modified method utilizing renal vessel-assisted anastomosis in rats with left UNx was compared with the traditional method for abdominal HTx.Methods:Male Sprague-Dawley rats were utilized as subjects for both experimental phases.In experiment 1,the animals were divided into four groups:sham operation group;LI group-rats undergoing occlusion of the abdominal aorta and vena cava below the renal vessels;UNx group-rats with left UNx;and LI+UNx group.All operated animals were monitored for up to 7 days for biochemical markers,renal histopathology,and survival rates.In experiment 2,we introduced the renal vessel-assisted method as the experimental group and compared it against the traditional method as the control within rat heterotopic HTx models.We assessed operative characteristics,echocardiography results,histological findings,and graft survival.Results:First,LI resulted in acute kidney dysfunction characterized by a decrease in 7day survival rates and creatinine clearance rates in both the LI and LI+UNx groups compared to the sham operation and UNx groups.Particularly,histopathological damage in the kidney and liver did not exhibit significant effects during this period.Second,the implementation of the renal vessel-assisted method significantly reduced bleeding volume at suture sites and enhanced the 7day survival rate compared to the traditional method.Conclusion:Acute kidney injury was induced by LI postoperation in treated rats.The renal vessel-assisted method demonstrated its effectiveness as a superior alternative that mitigates complications associated with the traditional method.
基金supported by the Guangdong Basic and Applied Basic Research Foundation(2025A1515011703).
文摘Objectives This study aimed to evaluate the effectiveness of a Health Belief Model(HBM)-based electronic education program combined with individualized supervised exercise in improving exercise adherence and pregnancy outcomes among women with gestational hypertension.Methods A randomized controlled trial was conducted from June 2024 to February 2025 at a tertiary hospital in Shenzhen,China.A total of 142 pregnant women diagnosed with gestational hypertension were randomly assigned to either an experimental group or a control group.The experimental group received routine antenatal care plus a 6-week HBM-based e-education intervention delivered via a mobile application and short messaging service(SMS)reminders,complemented by individualized in-person exercise guidance.The control group received routine antenatal care only.After the 6-week intervention,outcomes were assessed using the 6-min walk test,a disease knowledge and attitudes questionnaire,and the Pregnancy Exercise Self-Efficacy Scale.Primary outcomes included exercise adherence,blood pressure control,incidence of preeclampsia,and other pregnancy-related outcomes.Results A total of 129 participants completed the study(the intervention group[n=65],the control group[n=64]).At 6 weeks post-intervention,the experimental group demonstrated significantly greater improvements than the control group in exercise adherence,blood pressure control,preeclampsia incidence,disease-related knowledge and attitudes,and exercise self-efficacy(all P<0.05).Specifically,participants in the experimental group engaged in more frequent and longer-duration exercise sessions(P<0.05).Their blood pressure was maintained within a more stable and clinically optimal range(systolic:135.2±4.7 mmHg;diastolic:85.4±4.5 mmHg),which was significantly better than that of the control group(systolic:138.4±10.4 mmHg;diastolic:90.9±6.9 mmHg;P<0.05).The incidence of preeclampsia was also significantly lower in the experimental group(P<0.05).Additionally,scores for disease knowledge,attitudes,and exercise self-efficacy were higher in the experimental group(P<0.05).Within-group comparisons revealed that the experimental group showed significant improvements from baseline in exercise frequency,duration,total physical activity,and knowledge/attitude scores(P<0.05),whereas the control group showed no significant changes(P>0.05).Conclusion By embedding video-based education,real-time monitoring,and personalized support into routine prenatal care,this intervention facilitated positive behavioral changes in physical activity among pregnant women.The approach offers a scalable model for clinical nurses to delivering tailored remote exercise support for women with other pregnancy-related complications.
基金supported by the National Natural Science Foundation of China(No.41971339)the SDUST Research Fund(No.2019TDJH103)。
文摘The rapid melting of Arctic sea ice poses significant risks to the safety of shipping routes.Accurate remote sensing data on sea ice concentration(SIC)is crucial for effective route planning of ships and ensuring navigational safety.Despite the availability of numerous SIC products in China,these datasets still lag behind mainstream international products in terms of data accuracy,spatiotemporal resolution,and time span.To enhance the accuracy of China's domestic SIC remote sensing data,this study used the SIC data derived from the passive microwave remote sensing dataset provided by the University of Bremen(BRM-SIC)as a reference to conduct a comprehensive evaluation and analysis of two additional SIC datasets:the dataset derived from the microwave radiation imager(MWRI)aboard the FY-3D satellite,provided by the National Satellite Meteorological Center(FY-SIC),and the dataset obtained through the DT-ASI algorithm from the microwave imager of the FY-3D satellite,provided by Ocean University of China(OUC-SIC).Based on the evaluation results,a TransUnet fusion correction model was developed.The performance of this model was then compared against Ordinary Least Squares(OLS),Random Forest(RF),and UNet correction models,through spatial and temporal analyses.Results indicate that,compared to FY-SIC data,the RMSE of the OUC-SIC data and the standard data is reduced by24.245%,while the R is increased by 12.516%.Overall,the accuracy of OUC-SIC data is superior to that of FY-SIC data.During the research period(2020–2022),the standard deviation(SD)and coefficient of variation(CV)of OUC-SIC were 3.877%and 10.582%,respectively,while those for FY-SIC were 7.836%and 7.982%,respectively.In the study area,compared with OUC-SIC data,FYSIC data exhibited a larger standard deviation of deviation and a smaller coefficient of variation of deviation across most sea areas.These results indicate that the OUC-SIC data exhibit better temporal and spatial stability,whereas the FY-SIC data show stronger relative dimensionless stability.Among the four correction models,all showed improvements over the original,unfused corrected data.The fusion corrections using the OLS,RF,UNet,and TransUnet models reduced RMSE by 5.563%,14.601%,42.927%,and48.316%,respectively.Correspondingly,R increased by 0.463%,1.176%,3.951%,and 4.342%,respectively.Among these models,TransUnet performed the best,effectively integrating the advantages of FY-SIC and OUC-SIC data and notably improving the overall accuracy and spatiotemporal stability of SIC data.
基金supported by the Central Government Guiding Local Science and Technology Development Fund Project(No.2024SZY0343)the Joint Research Program for Ecological Conservation and High Quality Development of the Yellow River Basin(No.2022-YRUC-01-050205)+2 种基金the Higher Education Scientific Research Project of Inner Mongolia Autonomous Region(No.NJZZ23078)the project of Inner Mongolia"Prairie Talents"Engineering Innovation Entrepreneurship Talent Team,the Major Projects of Erdos Science and Technology(No.2022EEDSKJZDZX015)the Innovation Team of the Inner Mongolia Academy of Science and Technology(No.CXTD2023-01-016).
文摘Rural domestic sewage treatment is critical for environmental protection.This study defines the spatial pattern of villages from the perspective of rural sewage treatment and develops an integrated decision-making system to propose a sewage treatment mode and scheme suitable for local conditions.By considering the village spatial layout and terrain factors,a decision tree model of residential density and terrain type was constructed with accuracies of 76.47%and 96.00%,respectively.Combined with binary classification probability unit regression,an appropriate sewage treatment mode for the village was determined with 87.00%accuracy.The Analytic Hierarchy Process(AHP),combined with the Technique for Order Preference(TOPSIS)by Similarity to an Ideal Solution model,formed the basis for optimal treatment process selection under different emission standards.Verification was conducted in 542 villages across three counties of the Inner Mongolia Autonomous Region,focusing on the standard effluent effect(0.3773),low investment cost(0.3196),and high standard effluent effect(0.5115)to determine the best treatment process for the same emission standard under different needs.The annual environmental and carbon emission benefits of sewage treatment in these villages were estimated.This model matches village density,geographic feature,and social development level,and provides scientific support and a theoretical basis for rural sewage treatment decision-making.
文摘Slope units are divided according to the real topography and have clear geological characteristics,making them ideal units for evaluating the susceptibility to geological disasters.Based on the results of automatically and manually corrected hydrological slope unit division,the Longhua District,Shenzhen City,Guangdong Province,was selected as the study area.A total of 15 influencing factors,namely Fluctuation,slope,slope aspect,curvature,topographic witness index(TWI),stream power index(SPI),topographic roughness index(TRI),annual average rainfall,distance to water system,engineering rock group,distance to fault,land use,normalized difference vegetation index(NDVI),nighttime light,and distance to road,were selected as evaluation indicators.The information volume model(IV)and random points were used to select non-geological disaster units,and then the random forest model(RF)was used to evaluate the susceptibility to geological disasters.The automatic slope unit and the hydrological slope unit were compared and analyzed in the random forest and information volume random forest models.The results show that the area under the curve(AUC)values of the automatic slope unit evaluation results are 0.931 for the IV-RF model and 0.716 for the RF model,which are 0.6%(IV-RF model)and 1.9%(RF model)higher than those for the hydrological slope unit.Based on a comparison of the evaluation methods based on the two types of slope units,the hydrological slope unit evaluation method based on manual correction is highly subjective,is complicated to operate,and has a low evaluation accuracy,whereas the evaluation method based on automatic slope unit division is efficient and accurate,is suitable for large-scale efficient geological disaster evaluation,and can better deal with the problem of geological disaster susceptibility evaluation.
基金supported by the National Natural Science Foundation of China(Grant No.72161034).
文摘Human motion modeling is a core technology in computer animation,game development,and humancomputer interaction.In particular,generating natural and coherent in-between motion using only the initial and terminal frames remains a fundamental yet unresolved challenge.Existing methods typically rely on dense keyframe inputs or complex prior structures,making it difficult to balance motion quality and plausibility under conditions such as sparse constraints,long-term dependencies,and diverse motion styles.To address this,we propose a motion generation framework based on a frequency-domain diffusion model,which aims to better model complex motion distributions and enhance generation stability under sparse conditions.Our method maps motion sequences to the frequency domain via the Discrete Cosine Transform(DCT),enabling more effective modeling of low-frequency motion structures while suppressing high-frequency noise.A denoising network based on self-attention is introduced to capture long-range temporal dependencies and improve global structural awareness.Additionally,a multi-objective loss function is employed to jointly optimize motion smoothness,pose diversity,and anatomical consistency,enhancing the realism and physical plausibility of the generated sequences.Comparative experiments on the Human3.6M and LaFAN1 datasets demonstrate that our method outperforms state-of-the-art approaches across multiple performance metrics,showing stronger capabilities in generating intermediate motion frames.This research offers a new perspective and methodology for human motion generation and holds promise for applications in character animation,game development,and virtual interaction.
基金supported by the National Natural Science Foundation of China,Nos.82200784,32271311Qizhen Foundation,No.226‐2023‐00008(all to LH).
文摘Alzheimer’s disease is the most common cause of dementia.Although increasing evidence suggests that disruptions in lipid metabolism are closely associated with the disease,the overall profile of lipid and sterol changes that occur in the brain during Alzheimer’s disease remains unclear.In this study,we compared brain tissues extracted from 32-week-old male wild-type mice and 5×FAD transgenic Alzheimer’s disease model mice,which carry mutations in the amyloid precursor protein(APP)and presenilin 1(PS1)genes.Using untargeted lipidomics and sterolomics techniques,we investigated the metabolic profiles of lipids,with a focus on sterols specifically,in three brain regions:cerebellum,hippocampus,and olfactory bulb.Our results revealed significant alterations in various lipids,particularly in the hippocampus and olfactory bulb,suggesting changes in energy levels in these regions.Further pathway analysis indicated notable disruptions in key metabolic processes,particularly those related to fatty acids and cell membrane components.Additionally,we observed decreased expression of 15 genes involved in lipid and sterol regulation.Collectively,these findings provide new insights into how imbalances in lipid and sterol metabolism may contribute to the progression of Alzheimer’s disease,highlighting potential metabolic pathways involved in the development of this debilitating disease.
基金Scientific Research Program of Tianjin Municipal Education Commission(2023SK011)。
文摘The BOPPPS teaching model is a student-centered teaching model that has been widely applied in various teaching fields.This paper summarizes the overview of the BOPPPS teaching model,its application in emergency teaching and training,as well as its advantages and disadvantages,aiming to provide references for the further promotion and application of the BOPPPS teaching model in emergency education.
基金supported by the National Natural Science Foundation of China(No.42174011)。
文摘In the variance component estimation(VCE)of geodetic data,the problem of negative VCE is likely to occur.In the ordinary additive error model,there have been related studies to solve the problem of negative variance components.However,there is still no related research in the mixed additive and multiplicative random error model(MAMREM).Based on the MAMREM,this paper applies the nonnegative least squares variance component estimation(NNLS-VCE)algorithm to this model.The correlation formula and iterative algorithm of NNLS-VCE for MAMREM are derived.The problem of negative variance in VCE for MAMREM is solved.This paper uses the digital simulation example and the Digital Terrain Mode(DTM)to prove the proposed algorithm's validity.The experimental results demonstrated that the proposed algorithm can effectively correct the VCE in MAMREM when there is a negative VCE.
基金supported by National Natural Science Foundation of China Joint Fund for Enterprise Innovation Development(U23B2029)National Natural Science Foundation of China(62076167,61772020)+1 种基金Key Scientific Research Project of Higher Education Institutions in Henan Province(24A520058,24A520060,23A520022)Postgraduate Education Reform and Quality Improvement Project of Henan Province(YJS2024AL053).
文摘Large language models cross-domain named entity recognition task in the face of the scarcity of large language labeled data in a specific domain,due to the entity bias arising from the variation of entity information between different domains,which makes large language models prone to spurious correlations problems when dealing with specific domains and entities.In order to solve this problem,this paper proposes a cross-domain named entity recognition method based on causal graph structure enhancement,which captures the cross-domain invariant causal structural representations between feature representations of text sequences and annotation sequences by establishing a causal learning and intervention module,so as to improve the utilization of causal structural features by the large languagemodels in the target domains,and thus effectively alleviate the false entity bias triggered by the false relevance problem;meanwhile,through the semantic feature fusion module,the semantic information of the source and target domains is effectively combined.The results show an improvement of 2.47%and 4.12%in the political and medical domains,respectively,compared with the benchmark model,and an excellent performance in small-sample scenarios,which proves the effectiveness of causal graph structural enhancement in improving the accuracy of cross-domain entity recognition and reducing false correlations.
基金supported by NKRDPC-2022YFA1402802,NSFC-92165204the Research Grants Council of the HKSAR under Grant Nos.12304020 and 12301723+2 种基金Guangdong Provincial Key Laboratory of Magnetoelectric Physics and Devices under Grant No.2022B1212010008Guangdong Fundamental Research Center for Magnetoelectric Physics under Grant No.2024B0303390001Guangdong Provincial Quantum Science Strategic Initiative under Grant No.GDZX2401010。
文摘Systems with quenched disorder possess complex energy landscapes that are challenging to explore under conventional Monte Carlo methods.In this work,we implement an efficient entropy sampling scheme for accurate computation of the entropy function in low-energy regions.The method is applied to the two-dimensional±J random-bond Ising model,where frustration is controlled by the fraction p of ferromagnetic bonds.We investigate the low-temperature paramagnetic–ferromagnetic phase boundary below the multicritical point at T_(N)=0.9530(4),P_(N)=0.89078(8),as well as the zerotemperature ferromagnetic–spin-glass transition.Finite-size scaling analysis reveals that the phase boundary for T<T_(N) exhibits reentrant behavior.By analyzing the evolution of the magnetizationresolved density of states g(E,M)and ground-state spin configurations against increasing frustration,we provide strong evidence that the zero-temperature transition is a mixed-order.Finite-size scaling conducted on the spin-glass side supports the validity of β=0,whereβis the magnetization exponent,with a correlation length exponentν=1.50(8).Our results provide new insights into the nature of the ferromagnetic-to-spin-glass phase transition in an extensively degenerate ground state.
文摘Processing police incident data in public security involves complex natural language processing(NLP)tasks,including information extraction.This data contains extensive entity information—such as people,locations,and events—while also involving reasoning tasks like personnel classification,relationship judgment,and implicit inference.Moreover,utilizing models for extracting information from police incident data poses a significant challenge—data scarcity,which limits the effectiveness of traditional rule-based and machine-learning methods.To address these,we propose TIPS.In collaboration with public security experts,we used de-identified police incident data to create templates that enable large language models(LLMs)to populate data slots and generate simulated data,enhancing data density and diversity.We then designed schemas to efficiently manage complex extraction and reasoning tasks,constructing a high-quality dataset and fine-tuning multiple open-source LLMs.Experiments showed that the fine-tuned ChatGLM-4-9B model achieved an F1 score of 87.14%,nearly 30%higher than the base model,significantly reducing error rates.Manual corrections further improved performance by 9.39%.This study demonstrates that combining largescale pre-trained models with limited high-quality domain-specific data can greatly enhance information extraction in low-resource environments,offering a new approach for intelligent public security applications.
文摘Missiles provide long-range precision strike capabilities and have become a cornerstone of modern warfare.The contrail clouds formed by missile during their active flight phase present significant chal-lenges to high-altitude environmental observation and target detection and tracking.Existing studies primarily focus on specific airspace regions,leaving critical gaps in understanding the effects of long dispersion times,wide altitude ranges,and variable atmospheric conditions on missile contrail clouds.To address these gaps,this article develops a numerical method based on the Lagrangian random walk model,which incorporates various velocity variation terms,including particle velocity caused by the difference of wind field,by the thermal motion of local gas molecules and by random collisions between contrail cloud particles to capture the influence of environmental wind fields,atmospheric conditions,and particle concentrations on the motion of contrail cloud particles.A general coordinate system aligned with the missile's flight trajectory is employed to represent particle distribution characteristics.The proposed method is in good agreement with the conducted experiments as well as with the available numerical simulations.The results demonstrate that the proposed model effectively simulates the dispersion state of contrail clouds,accurately reflecting the impact of large-scale wind field variations and altitude changes with high computational efficiency.Additionally,simulation results indicate that the increased distance between gas molecules in rarefied environments facilitates enhanced particle dispersion,while larger particles exhibit a faster dispersion rate due to their greater mass.
基金funded by the National Natural Science Foundation of China,grant numbers(62272007,62001007)the Natural Science Foundation of Beijing,grant numbers(4234083,4212018)The authors also extend their appreciation to King Khalid University for funding this work through the Large Group Project under grant number RGP.2/373/45.
文摘Border Gateway Protocol(BGP),as the standard inter-domain routing protocol,is a distance-vector dynamic routing protocol used for exchanging routing information between distributed Autonomous Systems(AS).BGP nodes,communicating in a distributed dynamic environment,face several security challenges,with trust being one of the most important issues in inter-domain routing.Existing research,which performs trust evaluation when exchanging routing information to suppress malicious routing behavior,cannot meet the scalability requirements of BGP nodes.In this paper,we propose a blockchain-based trust model for inter-domain routing.Our model achieves scalability by allowing the master node of an AS alliance to transmit the trust evaluation data of its member nodes to the blockchain.The BGP nodes can expedite the trust evaluation process by accessing a global view of other BGP nodes through the master node of their respective alliance.We incorporate security service evaluation before direct evaluation and indirect recommendations to assess the security services that BGP nodes provide for themselves and prioritize to guarantee their security of routing service.We forward the trust evaluation for neighbor discovery and prioritize the nodes with high trust as neighbor nodes to reduce the malicious exchange routing behavior.We use simulation software to simulate a real BGP environments and employ a comparative experimental research approach to demonstrate the performance evaluation of our trust model.Compared with the classical trust model,our trust model not only saves more storage overhead,but also provides higher security,especially reducing the impact of collusion attacks.
基金funded by Institutional Fund Projects under grant no.(IFPDP-261-22)。
文摘Detecting cyber attacks in networks connected to the Internet of Things(IoT)is of utmost importance because of the growing vulnerabilities in the smart environment.Conventional models,such as Naive Bayes and support vector machine(SVM),as well as ensemble methods,such as Gradient Boosting and eXtreme gradient boosting(XGBoost),are often plagued by high computational costs,which makes it challenging for them to perform real-time detection.In this regard,we suggested an attack detection approach that integrates Visual Geometry Group 16(VGG16),Artificial Rabbits Optimizer(ARO),and Random Forest Model to increase detection accuracy and operational efficiency in Internet of Things(IoT)networks.In the suggested model,the extraction of features from malware pictures was accomplished with the help of VGG16.The prediction process is carried out by the random forest model using the extracted features from the VGG16.Additionally,ARO is used to improve the hyper-parameters of the random forest model of the random forest.With an accuracy of 96.36%,the suggested model outperforms the standard models in terms of accuracy,F1-score,precision,and recall.The comparative research highlights our strategy’s success,which improves performance while maintaining a lower computational cost.This method is ideal for real-time applications,but it is effective.
文摘BACKGROUND Kidney transplantation is one of the most effective treatments for patients with end-stage renal disease.However,many regions face low deceased donor rates and limited ABO-compatible transplant availability,which increases reliance on living donors.These regional challenges necessitate the implementation of kidney paired donation(KPD)programs to overcome incompatibilities such as ABO mismatch or positive cross-matching,even when suitable and willing donors are available.AIM To evaluate the effectiveness of a single-center domino KPD model in both operational planning and clinical management processes and to assess its impact on clinical outcomes.METHODS Between April 2020 and January 2024,we retrospectively evaluated patients enrolled in our center’s domino kidney transplantation program.Donor-recipient pairs unable to proceed due to ABO incompatibility or positive cross-matching with their own living donors were included.Donors and recipients were assessed based on blood group compatibility,HLA tissue typing,and negative cross-match results.A specialized computer algorithm grouped patients into three-way,fourway,and five-way chains.All surgical procedures were performed on the same day at a single center.RESULTS A total of 169 kidney transplants were performed,forming 52 domino chains.These domino KPD transplants accounted for a notable proportion of our center’s overall transplant activity,which included both living donor kidney transplants and deceased donor transplants.Among these chains,the primary reasons for participation were ABO incompatibility(74%),positive cross-matching(10%),and the desire to improve HLA mismatch(16%).Improved HLA mismatch profiles and high graft survival(96%at 1 year,92%at 3 years)and patient survival(98%at 1 year,94%at 3 years)rates were observed,as well as low acute rejection episodes.CONCLUSION The single-center domino KPD model enhanced transplant opportunities for incompatible donor-recipient pairs while maintaining excellent clinical outcomes.By providing a framework that addresses regional challenges,improves operational efficiency,and optimizes clinical management,this model offers actionable insights to reduce waiting lists and improve patient outcomes.
基金Natural Science Foundation of Shaanxi Province,Grant/Award Number:2023-CX-PT-17General Project of Natural Science Research in Luoyang Polytechnic College,Grant/Award Number:2024B01。
文摘The mortality rate of patients with abdominal aortic aneurysm(AAA) after rupture is extremely high,and this disease has become an important disease endangering the health of the Chinese population.Methods used to model AAA include intraluminal pressurized elastase infusion,chronic infusion of angiotensin Ⅱ(Ang Ⅱ) via an osmotic pump,periarterial application of calcium chloride,vascular grafting,and gene modification.AAA models induced by elastase and Ang Ⅱ are the two most widely used animal models.In the elastase-induced model,because intraluminal infusion is transient,with the cessation of initial stimulation,the aneurysm lesion tends to be stable and rarely ruptures.The model induced by Ang Ⅱ infusion often presents with a typical aortic dissection with a false lumen,whereas clinical AAA patients do not necessarily have dissection.Currently,the treatment of AAA in clinical practice remains endovascular,and there is a lack of pharmacological therapy,which is also related to the fact that the pathogenic mechanism has not been fully elucidated.Smoking,old age,male sex,and hypertension are the main risk factors for AAA,but these risk factors have not been fully investigated in the current modeling methods,which may affect the clinical translational application of research results based on animal models.Therefore,this article reviews the most commonly used AAA modeling methods,comments on their applications and limitations,and provides a perspective on the development of novel animal models.
