Sustained release of vascular endothelial growth factor A and basic fibroblast growth factor from nanofiber membranes reduces oxygen/glucose deprivation-induced injury to neurovascular units

Upregulation of vascular endothelial growth factor A/basic fibroblast growth factor(VEGFA/b FGF)expression in the penumbra of cerebral ischemia can increase vascular volume,reduce lesion volume,and enhance neural cell proliferation and differentiation,thereby exerting neuroprotective effects.However,the beneficial effects of endogenous VEGFA/b FGF are limited as their expression is only transiently increased.In this study,we generated multilayered nanofiber membranes loaded with VEGFA/b FGF using layer-by-layer self-assembly and electrospinning techniques.We found that a membrane containing 10 layers had an ideal ultrastructure and could efficiently and stably release growth factors for more than 1 month.This 10-layered nanofiber membrane promoted brain microvascular endothelial cell tube formation and proliferation,inhibited neuronal apoptosis,upregulated the expression of tight junction proteins,and improved the viability of various cellular components of neurovascular units under conditions of oxygen/glucose deprivation.Furthermore,this nanofiber membrane decreased the expression of Janus kinase-2/signal transducer and activator of transcription-3(JAK2/STAT3),Bax/Bcl-2,and cleaved caspase-3.Therefore,this nanofiber membrane exhibits a neuroprotective effect on oxygen/glucose-deprived neurovascular units by inhibiting the JAK2/STAT3 pathway.

A gated recurrent unit model to predict Poisson’s ratio using deep learning

Static Poisson’s ratio(vs)is crucial for determining geomechanical properties in petroleum applications,namely sand production.Some models have been used to predict vs;however,the published models were limited to specific data ranges with an average absolute percentage relative error(AAPRE)of more than 10%.The published gated recurrent unit(GRU)models do not consider trend analysis to show physical behaviors.In this study,we aim to develop a GRU model using trend analysis and three inputs for predicting n s based on a broad range of data,n s(value of 0.1627-0.4492),bulk formation density(RHOB)(0.315-2.994 g/mL),compressional time(DTc)(44.43-186.9 μs/ft),and shear time(DTs)(72.9-341.2μ s/ft).The GRU model was evaluated using different approaches,including statistical error an-alyses.The GRU model showed the proper trends,and the model data ranges were wider than previous ones.The GRU model has the largest correlation coefficient(R)of 0.967 and the lowest AAPRE,average percent relative error(APRE),root mean square error(RMSE),and standard deviation(SD)of 3.228%,1.054%,4.389,and 0.013,respectively,compared to other models.The GRU model has a high accuracy for the different datasets:training,validation,testing,and the whole datasets with R and AAPRE values were 0.981 and 2.601%,0.966 and 3.274%,0.967 and 3.228%,and 0.977 and 2.861%,respectively.The group error analyses of all inputs show that the GRU model has less than 5% AAPRE for all input ranges,which is superior to other models that have different AAPRE values of more than 10% at various ranges of inputs.

Risk Factors for Mortality in Acute Kidney Injury in Intensive Care Units in Togo

Context: Acute kidney injury (AKI) in intensive care unit (ICU) is common and associated with very high mortality. In Togo, a tropical country with limited resources and only one nephrology department in the north, acute kidney injury seems to be a real tragedy with high mortality. Aims: to determine risk factors for mortality in acute kidney injury in the intensive care units. Methods and Material: We made a multicentric cross sectional study during 6 months in the four referral centers in northern Togo. Univariate and multivariate logistic regression was used to identify factors associated with mortality. Data were analyzed using RStudio 2023.04.1. Results: A total of 12.6% of patients admitted to intensive care had presented with AKI. The mean age was 49.6 ± 17.9. The sex ratio (M/F) was 2.1. Community-acquired AKI was in the majority (67.7%). Oligo anuria was the most frequent functional sign (38.4%). In our series, 81.6% of patients were in KDIGO stages 2 to 3. AKI was organic in 56.2% of cases. Mortality was 44.3%. In multivariate analysis, the main factors predictive of death were: respiratory distress (OR = 2.36;p Conclusions: Acute kidney injury in intensive care is common in northern Togo, and mortality is high. Identification of associated factors should help anticipate prognosis.

One memristor–one electrolyte-gated transistor-based high energy-efficient dropout neuronal units

Artificial neural networks(ANN) have been extensively researched due to their significant energy-saving benefits.Hardware implementations of ANN with dropout function would be able to avoid the overfitting problem. This letter reports a dropout neuronal unit(1R1T-DNU) based on one memristor–one electrolyte-gated transistor with an ultralow energy consumption of 25 p J/spike. A dropout neural network is constructed based on such a device and has been verified by MNIST dataset, demonstrating high recognition accuracies(> 90%) within a large range of dropout probabilities up to40%. The running time can be reduced by increasing dropout probability without a significant loss in accuracy. Our results indicate the great potential of introducing such 1R1T-DNUs in full-hardware neural networks to enhance energy efficiency and to solve the overfitting problem.

Hounsfield units in assessing bone mineral density in ankylosing spondylitis patients with cervical fracture-dislocation

BACKGROUND Cervical spine fracture-dislocations in patients with ankylosing spondylitis(AS)are mostly unstable and require surgery.However,osteoporosis,one of the comorbidities for AS,could lead to detrimental prognoses.There are few accurate assessments of bone mineral density in AS patients.AIM To analyze Hounsfield units(HUs)for assessing bone mineral density in AS patients with cervical fracture-dislocation.METHODS The HUs from C2 to C7 of 51 patients obtained from computed tomography(CT)scans and three-dimensional reconstruction of the cervical spine were independently assessed by two trained spinal surgeons and statistically analyzed.Inter-reader reliability and agreement were assessed by interclass correlation coefficient.RESULTS The HUs decreased gradually from C2 to C7.The mean values of the left and right levels were significantly higher than those in the middle.Among the 51 patients,25 patients(49.02%)may be diagnosed with osteoporosis,and 16 patients(31.37%)may be diagnosed with osteopenia.CONCLUSION The HUs obtained by cervical spine CT are feasible for assessing bone mineral density with excellent agreement in AS patients with cervical fracture-dislocation.

Bionic lightweight design of limb leg units for hydraulic quadruped robots by additive manufacturing and topology optimization

Galloping cheetahs,climbing mountain goats,and load hauling horses all show desirable locomotion capability,which motivates the development of quadruped robots.Among various quadruped robots,hydraulically driven quadruped robots show great potential in unstructured environments due to their discrete landing positions and large payloads.As the most critical movement unit of a quadruped robot,the limb leg unit(LLU)directly affects movement speed and reliability,and requires a compact and lightweight design.Inspired by the dexterous skeleton–muscle systems of cheetahs and humans,this paper proposes a highly integrated bionic actuator system for a better dynamic performance of an LLU.We propose that a cylinder barrel with multiple element interfaces and internal smooth channels is realized using metal additive manufacturing,and hybrid lattice structures are introduced into the lightweight design of the piston rod.In addition,additive manufacturing and topology optimization are incorporated to reduce the redundant material of the structural parts of the LLU.The mechanical properties of the actuator system are verified by numerical simulation and experiments,and the power density of the actuators is far greater than that of cheetah muscle.The mass of the optimized LLU is reduced by 24.5%,and the optimized LLU shows better response time performance when given a step signal,and presents a good trajectory tracking ability with the increase in motion frequency.

Improvement of the nutritional support management system for patients in intensive care units

BACKGROUND Nutritional support for patients hospitalized in the intensive care unit(ICU)is an important part of clinical treatment and care,but there are significant implementation difficulties.AIM To introduce a modified nutritional support management system for ICU patients based on closed-loop information management and psychological counseling.METHODS The division of functions,personnel training,system construction,development of an intelligent decision-making software system,quality control,and improvement of the whole process were carried out to systematically manage nutritional support for ICU patients.RESULTS Following the implementation of the whole process management system,the scores of ICU medical staff’s knowledge,attitudes/beliefs,and practices regarding nutritional support were comprehensively enhanced.The proportion of hospital bed-days of total enteral nutrition(EN)in ICU patients increased from 5.58%to 11.46%,and the proportion of EN plus parenteral nutrition increased from 42.71%to 47.07%.The rate of EN initiation within 48 h of ICU admission increased from 37.50%to 48.28%,and the EN compliance rate within 72 h elevated from 20.59%to 31.72%.After the implementation of the project,the Self-rating Anxiety Scale score decreased from 61.07±9.91 points to 52.03±9.02 points,the Self-rating Depression Scale score reduced from 62.47±10.50 points to 56.34±9.83 points,and the ICU stay decreased from 5.76±2.77 d to 5.10±2.12 d.CONCLUSION The nutritional support management system based on closed-loop information management and psychological counseling achieved remarkable results in clinical applications in ICU patients.

An Interpretable Light Attention-Convolution-Gate Recurrent Unit Architecture for the Highly Accurate Modeling of Actual Chemical Dynamic Processes

To equip data-driven dynamic chemical process models with strong interpretability,we develop a light attention–convolution–gate recurrent unit(LACG)architecture with three sub-modules—a basic module,a brand-new light attention module,and a residue module—that are specially designed to learn the general dynamic behavior,transient disturbances,and other input factors of chemical processes,respectively.Combined with a hyperparameter optimization framework,Optuna,the effectiveness of the proposed LACG is tested by distributed control system data-driven modeling experiments on the discharge flowrate of an actual deethanization process.The LACG model provides significant advantages in prediction accuracy and model generalization compared with other models,including the feedforward neural network,convolution neural network,long short-term memory(LSTM),and attention-LSTM.Moreover,compared with the simulation results of a deethanization model built using Aspen Plus Dynamics V12.1,the LACG parameters are demonstrated to be interpretable,and more details on the variable interactions can be observed from the model parameters in comparison with the traditional interpretable model attention-LSTM.This contribution enriches interpretable machine learning knowledge and provides a reliable method with high accuracy for actual chemical process modeling,paving a route to intelligent manufacturing.

Aerial target threat assessment based on gated recurrent unit and self-attention mechanism

Aerial threat assessment is a crucial link in modern air combat, whose result counts a great deal for commanders to make decisions. With the consideration that the existing threat assessment methods have difficulties in dealing with high dimensional time series target data, a threat assessment method based on self-attention mechanism and gated recurrent unit(SAGRU) is proposed. Firstly, a threat feature system including air combat situations and capability features is established. Moreover, a data augmentation process based on fractional Fourier transform(FRFT) is applied to extract more valuable information from time series situation features. Furthermore, aiming to capture key characteristics of battlefield evolution, a bidirectional GRU and SA mechanisms are designed for enhanced features.Subsequently, after the concatenation of the processed air combat situation and capability features, the target threat level will be predicted by fully connected neural layers and the softmax classifier. Finally, in order to validate this model, an air combat dataset generated by a combat simulation system is introduced for model training and testing. The comparison experiments show the proposed model has structural rationality and can perform threat assessment faster and more accurately than the other existing models based on deep learning.

Epidemiology of carbapenem-resistant Acinetobacter baumannii colonization in neonatal intensive care units:A systematic review and meta-analysis

BACKGROUND The rising prevalence of carbapenem-resistant Acinetobacter baumannii(CRAB)in neonatal intensive care units(NICUs)represents an escalating challenge in healthcare settings,particularly in managing hospital-acquired infections(HAIs).Studies across various World Health Organization regions have documented a significant incidence of CRAB-related HAIs,with rates as high as 41.7 cases per 1000 patients in ICUs,accounting for 13.6%of all HAIs.These infections pose a doubled mortality risk compared to infections with carbapenem-susceptible Acinetobacter baumannii.A particularly concerning aspect of CRAB colonization is its asymptomatic nature,enabling its transmission through healthcare workers(HCWs)or the NICU environment to vulnerable neonates with developing immune systems.AIM To explore the prevalence of CRAB colonization in NICUs,focusing on neonates,healthcare workers,and the environmental samples,to enhance epidemiological understanding and inform targeted interventions.METHODS We conducted according to PRISMA 2020 checklist guidelines,a comprehensive literature search across multiple databases including MEDLINE(Ovid),EMBASE(Ovid),Global Health(Ovid),Web of Science,and Global Index Me-dicus.Studies were selected based on predetermined criteria,primarily involving neonates,HCWs,and environmental swabs,using culture or molecular methods to detect CRAB colonization.We excluded studies that did not specifically focus on NICUs,were duplicates,or lacked necessary data.The study selection and quality assessment were conducted independently by two reviewers.Data extraction involved collecting comprehensive details about each study.Our statistical analysis used a random-effects model to calculate the pooled prevalence and confidence intervals,stratifying results by regional location.We assessed study heterogeneity using Cochran's Q statistic and I²statistic,with regression tests employed to evaluate potential publication bias.RESULTS We analyzed 737 records from five databases,ultimately including 13 studies from ten countries.For neonates,the pooled prevalence was 4.8%(95%CI:1.1%to 10.5%)with the highest rates observed in South-East Asia(10.5%;95%CI:2.4%to 23.3%).Among HCWs,a single Indian study reported a 3.3%prevalence.Environmental samples showed a prevalence of 2.3%(95%CI:0%to 9.3%),with the highest rates in South-East Asia(10%;95%CI:4.2%to 17.7%).Significant heterogeneity was found across studies,and no publication bias was detected.CONCLUSION This systematic review highlights a significant prevalence of CRAB colonization in neonates across various regions,particularly in South-East Asia,contrasting with lower rates in high-income countries.The study reveals a gap in research on HCWs colonization,with only a single study from India reporting moderate prevalence.Environmental samples indicate moderate levels of CRAB contamination,again higher in South-East Asia.These findings underscore the need for more extensive and focused research on CRAB colonization in NICUs,including exploring the roles of HCWs and the environment in transmission,understanding antimicrobial resistance patterns,and developing effective prevention measures.

Temperature Trends and Accumulation of Chill Hours, Chill Units, and Chill Portions in South Carolina

There is considerable concern about the potential impact of climate change on agriculture, such as the accumulation of chilling hours needed to break the dormancy of many perennial plants, like fruit trees. Therefore, this study aimed to determine if there had been a significant change in air temperatures and chill hours, chill units, and chill portion accumulation in South Carolina over the last two decades. Two decades of daily maximum (Tmax) and minimum (Tmin) air temperature records were obtained from weather stations in thirty-one counties in South Carolina. Hourly temperature data, reconstructed from the daily data, were used to calculate the daily and annual chill hours, chill units, and chill portions accumulation using four different chill models for each location and year. The chill models included the T(t) °C model, the 0°C °C model, the Utah model, and the Dynamic model. For each county, regression analyses were conducted to evaluate the historical trends. Despite year-to-year variability, the tendency was a statistically significant (α = 0.05) increase in air temperature, averaging 0.089°C per year for 20 out of 31 counties in South Carolina. The other 11 counties had no significant change in temperature. The average temperature increase in the 31 counties was 0.072°C per year. The temperature increase resulted in a decrease in annual chill accumulation during the fall to spring, averaging 17.7 chill hours, 8.6 chill hours, 17.0 chill units, and 0.40 chill portions per year calculated with the T(t) °C, 0°C °C, Utah, and Dynamic models, respectively. However, whether this decrease in chill values was statistically significant or not depended on the chill model used. This study did not investigate the cause of the observed historical trends in temperature and chill accumulation. Still, if the trends continue, they could significantly impact the future of the temperate fruit tree industry in the state.

Improved Units of Measure in Rotational Mechanics

The SI system of units in rotational mechanics yields correct numerical results, but it produces physically incorrect units of measure in many cases. SI units also violate the principle of general covariance—the general rule for defining continuous coordinates and units in mathematics and mathematical physics. After 30+ years of wrestling with these problems, the ultimate authority on units of measure has declared that Newton–meter and Joule are not equivalent in rotational mechanics, as they are in the rest of physics. This article proposes a simple modification to SI units called “Nonstandard International units” (“NI units”) until a better name is agreed upon. NI units yield correct numerical results and physically correct units of measure, and they satisfy the principle of general covariance. The main obstacle to the adoption of NI units is the consensus among users that the radius of rotation should have the unit meter because the radius can be measured with a ruler. NI units assigned to radius should have units meter/radian because the radius is a conversion factor between angular size and circumferential length, as in arclength = rθ. To manage the social consensus behind SI units, the author recommends retaining SI units as they are, and informing users who want correct units that NI units solve the technical problems of SI units.

A HybridManufacturing ProcessMonitoringMethod Using Stacked Gated Recurrent Unit and Random Forest

This study proposed a new real-time manufacturing process monitoring method to monitor and detect process shifts in manufacturing operations.Since real-time production process monitoring is critical in today’s smart manufacturing.The more robust the monitoring model,the more reliable a process is to be under control.In the past,many researchers have developed real-time monitoring methods to detect process shifts early.However,thesemethods have limitations in detecting process shifts as quickly as possible and handling various data volumes and varieties.In this paper,a robust monitoring model combining Gated Recurrent Unit(GRU)and Random Forest(RF)with Real-Time Contrast(RTC)called GRU-RF-RTC was proposed to detect process shifts rapidly.The effectiveness of the proposed GRU-RF-RTC model is first evaluated using multivariate normal and nonnormal distribution datasets.Then,to prove the applicability of the proposed model in a realmanufacturing setting,the model was evaluated using real-world normal and non-normal problems.The results demonstrate that the proposed GRU-RF-RTC outperforms other methods in detecting process shifts quickly with the lowest average out-of-control run length(ARL1)in all synthesis and real-world problems under normal and non-normal cases.The experiment results on real-world problems highlight the significance of the proposed GRU-RF-RTC model in modern manufacturing process monitoring applications.The result reveals that the proposed method improves the shift detection capability by 42.14%in normal and 43.64%in gamma distribution problems.

A Deep Learning-Based Teaching Design for High School Geography Units:Taking the Example of Landforms of the Humanistic Education Edition

The traditional teaching methods of one-way cultivation of students can no longer meet the requirements of talent cultivation at this stage.The issue of how to promote students from passive acceptance to the independent cognitive understanding stage(i.e.deep learning)has become the focus of geography teaching.Therefore,under the guidance of deep learning theory,this paper takes the“landforms”knowledge unit of the Humanistic Education Edition as an example,improves the classroom teaching means through the unit teaching mode,reconstructs the“landforms”teaching unit,and explores the specific teaching of high school geography unit based on deep learning.This study provides a good example and guidelines for high school geography teaching and learning.

Novel models for simulating maize growth based on thermal time and photothermal units: Applications under various mulching practices

Maize (Zea mays L.) is one of the three major food crops and an important source of carbohydrates for maintaining food security around the world.Plant height (H),stem diameter (SD),leaf area index (LAI) and dry matter (DM) are important growth parameters that influence maize production.However,the combined effect of temperature and light on maize growth is rarely considered in crop growth models.Ten maize growth models based on the modified logistic growth equation (Mlog) and the Mitscherlich growth equation (Mit) were proposed to simulate the H,SD,LAI and DM of maize under different mulching practices based on experimental data from 2015–2018.Either the accumulative growing degree-days (AGDD),helio thermal units (HTU),photothermal units (PTU) or photoperiod thermal units (PPTU,first proposed here) was used as a single driving factor in the models;or AGDD was combined with either accumulative actual solar hours (ASS),accumulative photoperiod response (APR,first proposed here) or accumulative maximum possible sunshine hours (ADL) as the dual driving factors in the models.The model performances were evaluated using seven statistical indicators and a global performance index.The results showed that the three mulching practices significantly increased the maize growth rates and the maximum values of the growth curves compared with non-mulching.Among the four single factor-driven models,the overall performance of the Mlog_(PTU)Model was the best,followed by the Mlog_(AGDD)Model.The Mlog_(PPTU)Model was better than the Mlog_(AGDD)Model in simulating SD and LAI.Among the 10 models,the overall performance of the Mlog_(AGDD–APR)Model was the best,followed by the Mlog_(AGDD–ASS)Model.Specifically,the Mlog_(AGDD–APR)Model performed the best in simulating H and LAI,while the Mlog_(AGDD–ADL)and Mlog_(AGDD–ASS)models performed the best in simulating SD and DM,respectively.In conclusion,the modified logistic growth equations with AGDD and either APR,ASS or ADL as the dual driving factors outperformed the commonly used modified logistic growth model with AGDD as a single driving factor in simulating maize growth.

Investigation of the Interaction Mechanism between Lignin Structural Units and Enzyme

The effect of lignin structural units on enzymatic hydrolysis of lignocellulosic biomass was investigated,especially the inhibitory role of lignin in non-productive adsorption with enzymes.Milled wood lignin(MWL)was isolated from different hardwoods of poplar,eucalyptus and acacia.The isolated lignin samples were characterized by elemental analysis,gel permeation chromatography,nitrobenzene oxidation and fourier infrared spectroscopy.The mechanism of lignin structural units on enzymatic hydrolysis of cellulose was studied by quartz crystal microbalance(QCM).The results showed that different structural units of lignin had different adsorption capacity for enzymes.The results of nitrobenzene oxidation indicated that the S/G ratio(S:syringyl-like lignin structures;G:guaiacyl-like lignin structures)of lignin of poplar was 0.99,that of eucalyptus was 1.92 and that of acacia was 1.34.According to the results of QCM,the adsorption capacity of the three lignin films was as follows:Poplar MWL(S/G ratio 0.99)<Acacia MWL(S/G ratio 1.34)<Eucalyptus MWL(S/G ratio 1.92).Eucalyptus MWL with higher degree of condensation and S/G ratio showed stronger affinity to enzymes and more non-productive adsorption with enzymes,resulting in less adsorption between enzymes and cellulose,and lower enzymatic hydrolysis efficiency.

Prediction of the instability probability for rainfall induced landslides:the effect of morphological differences in geomorphology within mapping units

Slope units is an effective mapping unit for rainfall landslides prediction at regional scale.At present,slope units extracted by hydrology and morphological method report very different morphological feature and boundaries.In order to investigate the effect of morphological difference on the prediction performance,this paper presents a general landslide probability analysis model for slope units.Monte Carlo method was used to describe the spatial uncertainties of soil mechanical parameters within slope units,and random search technique was performed to obtain the minimum safety factor;transient hydrological processes simulation was used to provide key hydrological parameters required by the model,thereby achieving landslide prediction driven by quantitative precipitation estimation and forecasting data.The prediction performance of conventional slope units(CSUs)and homogeneous slope units(HSUs)were analyzed in three case studies from Fengjie County,China.The results indicate that the mean missing alarm rate of CSUs and HSUs are 31.4% and 10.6%,respectively.Receiver Operating Characteristics(ROC)analysis also reveals that HSUs is capable of improving the overall prediction performance,and may be used further for rainfall-induced landslide prediction at regional scale.

Radar Quantitative Precipitation Estimation Based on the Gated Recurrent Unit Neural Network and Echo-Top Data

The Gated Recurrent Unit(GRU) neural network has great potential in estimating and predicting a variable. In addition to radar reflectivity(Z), radar echo-top height(ET) is also a good indicator of rainfall rate(R). In this study, we propose a new method, GRU_Z-ET, by introducing Z and ET as two independent variables into the GRU neural network to conduct the quantitative single-polarization radar precipitation estimation. The performance of GRU_Z-ET is compared with that of the other three methods in three heavy rainfall cases in China during 2018, namely, the traditional Z-R relationship(Z=300R1.4), the optimal Z-R relationship(Z=79R1.68) and the GRU neural network with only Z as the independent input variable(GRU_Z). The results indicate that the GRU_Z-ET performs the best, while the traditional Z-R relationship performs the worst. The performances of the rest two methods are similar.To further evaluate the performance of the GRU_Z-ET, 200 rainfall events with 21882 total samples during May–July of 2018 are used for statistical analysis. Results demonstrate that the spatial correlation coefficients, threat scores and probability of detection between the observed and estimated precipitation are the largest for the GRU_Z-ET and the smallest for the traditional Z-R relationship, and the root mean square error is just the opposite. In addition, these statistics of GRU_Z are similar to those of optimal Z-R relationship. Thus, it can be concluded that the performance of the GRU_ZET is the best in the four methods for the quantitative precipitation estimation.

基于改进AETG算法的车载ECU测试方法研究

对两两组合测试方法进行研究,提出了一种基于改进AETG算法的车载ECU测试方案。通过处理相邻参数和边界条件,优化了组合测试的设计和执行过程。深入研究AETG算法,通过优化参数选择策略提出了算法改进方案,提升了生成测试用例的精简度。最后,通过设计相应的测试平台实验验证了提出方法的有效性。相较于传统测试方案,该研究在测试效率上提高了15倍以上,为未来车载ECU测试提供了更高效的解决方案,对推动汽车电子系统的可靠性和性能提升具有重要意义。

The neurovascular unit-on-a-chip:modeling ischemic stroke to stem cell therapy

The neurovascular unit and stem cell therapy in ischemic stroke:Ischemic stroke,accounts for approximately 85% of all stroke incidents and is a major global health burden.It is the leading cause of disability and death worldwide,posing immense societal and economic challenges due to the long-term care required for stro ke survivors and the significant healthcare costs associated with its treatment and management(Amarenco et al.,2009).