Multiple Sets Research Articles

Detecting epicardial connection through intercaval bundle using intra-atrial activation sequence during right pulmonary vein pacing

Abstract Background An epicardial connection (EC) through the intercaval bundle (EC-ICB) between the right pulmonary vein (RPV) and the right atrium (RA) is one of the reasons for the need of carina ablation for PV isolation. Purpose We evaluated the intra-atrial activation sequence during RPV pacing after failure of ipsilateral RPV isolation and sought to identify specific conduction patterns in the presence of the EC-ICB. Methods This study included 223 consecutive patients undergoing initial catheter ablation of atrial fibrillation. If the RPV was not isolated by circumferential ablation or reconnected during the waiting period, an exit map during mid RPV carina pacing was created. If the earliest site of the exit map was the RA, the patient was classified into the EC-ICB group. During the exit map, intra-atrial activation sequence and RPV-HRA time were evaluated. Results First-pass isolation of the RPV was not obtained in 36 patients (16.1%), and 22 patients (9.9%) showing reconnection. Among them, 12 patients were classified into the EC-ICB group and 28 patients into the Non-EC-ICB group after excluding multiple ablation lesion sets or incomplete mapping. Intra-atrial activation sequence showed different patterns between the two groups. RPV-HRA time was significantly shorter in the EC-ICB group compared to the Non-EC-ICB group (69.2 ± 15.2 ms v.s. 148.6 ± 51.2 ms. P<0.001), and RPV-HRA time<89.0ms was highly predictive for existence of the EC-ICB (sensitivity 91.7 %, specificity 89.3 %). Conclusions The EC-ICB can be effectively detected by intra-atrial sequencing during RPV pacing and RPV-HRA time<89.0ms was highly predictive.

A Survey on Multimodal Multiobjective Optimization Algorithm

With the diversification of industrial production and daily life needs, traditional single modal multi-objective optimization algorithms are no longer able to meet complex decision-making requirements. Multimodal multi-objective optimization algorithms (MMOPAs) provide decision- makers with more options by offering multiple feasible Pareto optimal solution sets. This article provides a detailed analysis of the research background and related concepts of multimodal multi-objective optimization algorithms. It analyzes the current development status of multimodal multi-objective optimization algorithms, introduces commonly used benchmark problems and evaluation indicators, and finally explaines future research directions.

6.H. Round table: Harnessing Patient-Reported Data for Health Innovation: Insights from the OECD PaRIS Initiative

Abstract Summary This round table will disseminate knowledge and foster discussions on data reported by patients with a chronic disease in primary care settings in 20 countries for enhancing health system responsiveness and effectiveness. By delving into the OECD’s Patient-Reported Indicator Surveys (PaRIS) survey’s development, adaptation, and analytical strategies, participants will leave with a richer understanding of how patient-reported data can inform and transform health policy and practice in Europe and beyond. Rationale The round table aligns with the Conference theme by focusing on the transformative potential of patient-reported data in health systems. OECD PaRIS provide a unique lens through which to examine patient-centered care, showcasing innovative methods to capture and utilize data across diverse health system contexts. This round table will explore the development, adaptation, and analysis of this flagship survey, revealing its critical role in shaping health policies and practices that are attuned to patient needs and experiences. Objectives 1. Understanding Development and Testing Processes: To gain insights into the systematic approaches used in developing patient-reported instruments that are both robust and sensitive across multiple healthcare settings; 2. Exploring Co-development and Adaptation: To discuss the challenges and strategies involved in adapting health surveys to various national contexts while ensuring stakeholder engagement and data relevancy; 3. Analyzing Data for Policy Making: To comprehend the analytical frameworks used in processing patient-reported data for crafting effective health policies and identifying factors that influence patient outcomes. Key messages • Patient reported data on experiences and outcomes provide essential information for health systems performance. • Unique insights into the systematic collection of such information can be gathered through the proposed discussion on the experience of designing and implementing the PaRIS Survey.

Extensional settings favour initial REE enrichment in the parent granites of ion-adsorption REE deposits: implications from Late Permian to Triassic granites in South China

To understand the generation of ion-adsorption REE deposits it is important to clarify the mechanism of REE enrichment in granites. We investigated Late Permian to Triassic granites from South China to investigate these issues. Ore-related granites with high REE contents (192–467 ppm) intruded in the Late Permian have high K 2 O and Zr + Ce + Nb + Y contents, high Ga/Al and K/Na ratios and low CaO contents, and are classified as aluminous A-type granite derived from melting of felsic igneous rocks in a high-temperature extensional setting. Ore-unrelated Triassic Pingtian granite with relatively low REE contents (81.7–127 ppm) is peraluminous with occurrence of muscovite, indicating an S-type affinity. Its high Rb/Sr and low CaO/Na 2 O ratios indicate magma origin from a metapelitic source related to a compressional setting owing to continental collision. In extensional settings in South China, the high-temperature conditions promoting melting of REE-rich accessory minerals and F-rich conditions improving the solubility of REE in the melts could be the reasons for the initial REE enrichment in these granites. Given that parent rocks for ion-adsorption REE deposits are all generated in extensional settings, it is considered that multiple tectonic stages and long-term extensional setting in South China contributed to generation of the specific ion-adsorption REE deposits in South China.

Dual-space distribution metric-based evolutionary algorithm for multimodal multi-objective optimization

Multimodal multi-objective optimization problems (MMOPs) are characterized by having multiple global or local Pareto solution sets. In most of multimodal multi-objective evolutionary algorithms, the distribution of population in objective space and decision space cannot be taken into account, simultaneously. In this paper, an innovative evolutionary algorithm based on dual-space distribution metric for multimodal multi-objective optimization (DsDMEA) is designed to solve this problem. Specifically, the two enhanced performance metrics are coupled together as a single dual-space distribution metric (DsDM) serving as the criterion for environmental selection. DsDMEA explores solutions on Pareto front and Pareto solutions sets that are not associated with the reference point, which show limited contributions to the distribution in dual spaces. Then DsDMEA removes any bi-noncontributing solutions using DsDM during environmental selection until the population size is met. This achieves a balance between the distribution of solutions and convergence in decision space. At the same time, this paper introduces a novel fitness computation method, enabling the precise localization of local Pareto fronts within the population. Finally, eight state-of-the-art multimodal multi-objective evolutionary algorithms are adopted to make a comparison on two types of benchmark to verify the performance of the DsDMEA. The experimental results demonstrated the DsDMEA effectiveness in solving MMOPs with local problems and traditional MMOPs.

Short-term heating load forecasting model based on SVMD and improved informer

Accurate heat load forecasting can not only effectively reduce energy consumption, but also improve the efficiency of the heating system and user comfort. In order to improve the accuracy of heat load forecasting, we proposed a forecasting method combining SVMD algorithm and improved Informer model and applied it to the heat load forecasting of district heating system. First, SVMD algorithm is used to decompose the heat load data, and then relative position coding is introduced into Informer model, and causal convolution and jump connection mechanisms are adopted to better capture the dependence of sequence data and enhance the ability of local information extraction. In order to verify the model performance, experiments were conducted on multiple data sets. The results show that the R2 evaluation index of the model reaches 98.7 %, which is 10.8 %, 17.1 %, 6.3 %, 4.9 %, 2.3 %, 1.5 % and 16.6 % higher than SVR, RNN, LSTM, Transformer, SH-Informer, iTransformer and DLinear respectively. The validity of the model is further verified by DM test, which provides a reference for further improving the timeliness of intelligent heating.

Quantum classical hybrid convolutional neural networks for breast cancer diagnosis.

The World Health Organization states that early diagnosis is essential to increasing the cure rate for breast cancer, which poses a danger to women's health worldwide. However, the efficacy and cost limitations of conventional diagnostic techniques increase the possibility of misdiagnosis. In this work, we present a quantum hybrid classical convolutional neural network (QCCNN) based breast cancer diagnosis approach with the goal of utilizing quantum computing's high-dimensional data processing power and parallelism to increase diagnosis efficiency and accuracy. When working with large-scale and complicated datasets, classical convolutional neural network (CNN) and other machine learning techniques generally demand a large amount of computational resources and time. Their restricted capacity for generalization makes it challenging to maintain consistent performance across multiple data sets. To address these issues, this paper adds a quantum convolutional layer to the classical convolutional neural network to take advantage of quantum computing to improve learning efficiency and processing speed. Simulation experiments on three breast cancer datasets, GBSG, SEER and WDBC, validate the robustness and generalization of QCCNN and significantly outperform CNN and logistic regression models in classification accuracy. This study not only provides a novel method for breast cancer diagnosis but also achieves a breakthrough in breast cancer diagnosis and promotes the development of medical diagnostic technology.

Molecular dynamics investigation of IEPOX chemical behavior at the interface and in the bulk phase of acidic aerosols

Isoprene epoxydiol (IEPOX) is an important reactive gas-phase intermediate produced by the photooxidation of isoprene under low NOx conditions, playing a key role in the formation of secondary organic aerosols (SOA). Previous studies have mostly focused on the liquid-phase reactions of IEPOX within aerosols; however, interfacial heterogeneous chemical reactions are equally important in SOA formation. This study systematically explores the reaction mechanisms of IEPOX at the acidic aerosol interface and in the bulk phase using classical molecular dynamics (MD) and ab initio molecular dynamics simulations (AIMD). The study found that the free energy of IEPOX at the aerosol interface significantly decreases, indicating that interfacial heterogeneous chemical reactions are indispensable for the formation of IEPOX-derived SOA. The research reveals the formation pathways of 2-methyltetrols (2-MTO) and 1,3,4-trihydroxy-3-methylbutan-2-yl sulfates (2-MTOOS), finding that the protonation of the epoxy O atom and the cleavage of the C–O bond are the rate-controlling steps, while the nucleophilic addition is a spontaneous process. Through multiple sets of simulations, it was observed that the formation frequency of 2-MTO at the acidic aerosol interface and in the bulk phase reached 53.8%, significantly higher than the 30.8% of 2-MTOOS, which is consistent with field observation data. Additionally, through metadynamics (MTD) simulations, it was suggested that IEPOX could undergoes acid-catalyzed ring-opening reactions at the interface, potentially followed by the transfer of H atoms from primary alcohols into the aerosol, leading to the possible formation of the intermediate product 3-methylbut-3-ene-1,2,4-triol (one of the proposed structures of C5-alkene triols). These findings provide new insights into the formation mechanism of IEPOX-derived SOA and offer a scientific basis for future studies on their physicochemical properties and atmospheric fate.

Visible light infrared matching algorithm based on improved SuperPoint and linear converter

In order to solve the problems of high difficulty and mismatch rate in heterogeneous image matching of visible light and infrared images, a deep learning matching algorithm based on improved SuperPoint and linear transformer is proposed. The algorithm first introduces the idea of feature pyramid to construct a feature description branch based on the SuperPoint network structure, and trains it based on the hinge loss function, so as to better learn the multi-scale deep features of visible light and infrared images and increase the similarity of image point pair descriptors with the same name; in the feature matching module, the linear transformer is used to improve the SuperGlue matching algorithm and aggregate features to improve matching performance. The proposed algorithm is experimentally verified on multiple data sets. The results show that compared with the existing algorithms, the algorithm achieves better matching results and improves the matching accuracy.

The H5N1-NS1 protein affects the host cell cycle and apoptosis through interaction with the host lncRNA PIK3CD-AS2.

Long noncoding RNAs (lncRNAs) are involved in the host antiviral response, but how host lncRNAs interact with viral proteins remains unclear. The NS1 protein of avian influenza viruses can affect the interferon-dependent expression of several host lncRNAs, but the exact mechanism is unknown. To further investigate the molecular mechanism and functions of NS1 proteins and host lncRNAs, we performed RNA-immunoprecipitation sequencing assays on A549 cells transfected with the H5N1-NS1 gene. We identified multiple sets of host lncRNAs that interact with NS1. The results of the RNA pulldown assay indicated that PIK3CD-AS2 can directly interact with NS1 in vitro. Immunofluorescence confocal microscopy showed that these proteins were colocalized in the nucleus. Further studies revealed that PIK3CD-AS2 can also inhibit the transcription of NS1, which in turn affects the translation of the NS1 protein. PIK3CD-AS2 overexpression regulates NS1 protein-induced cell cycle arrest and initiates apoptosis. We hope this work will help elucidate the molecular mechanisms associated with NS1 proteins in the study of viral infections to promote the development of potential treatments for patients infected with avian influenza A viruses.

Testing spatial out-of-sample area of influence for grain forecasting models

Abstract We examine the factors that determine if a grain forecasting model fit to one region can be transferred to another region. Prior research has proposed examining the area of applicability (AoA) of a model based on structurally similar characteristics in the Earth Observation predictors and weights based on the model derived feature importance. We expand on and evaluate this approach in the context of grain yield forecasting in Sub-Saharan Africa. Specifically, we evaluate an AoA methodology established for generating raster surfaces and apply it to vector supported grain data. We fit a series of ensemble tree models both within single countries and across multiple sets of countries and then test those models in countries excluded from the training set. We then calculate and decompose AoA measures and examine several different performance metrics. We find that the spatial transfer accuracy does not vary across season but does vary by average rainfall and across high, medium, and low yielding regions. In general, areas with higher yields and medium to high average rainfall tend to have higher accuracy for both model training and transfer. Finally, we find that fitting models with multiple countries provides more accurate out-of-sample estimates when compared to models fitted to a single country.

5-hydroxymethylcytosine sequencing in plasma cell-free DNA identifies unique epigenomic features in prostate cancer patients resistant to androgen deprivation therapies.

Currently, no biomarkers are available to identify resistance to androgen-deprivation therapies (ADT) in men with hormone-naive prostate cancer. Since 5-hydroxymethylcytosines (5hmC) in gene body are associated with gene activation, in this study, we evaluated whether 5hmC signatures in cell-free DNA (cfDNA) predicts early resistance to ADT. We collected a total of 139 serial plasma samples from 55 prostate cancer patients receiving ADT at three time points including baseline (prior to initiating ADT, N=55), 3-month (after initiating ADT, N=55), and disease progression (N=15) within 24 months or 24-month if no progression was detected (N=14). To quantify 5hmC abundance across the genome, we used selective chemical labeling sequencing and mapped sequence reads to individual genes. Differential methylation analysis in baseline samples identified significant 5hmC difference in 1,642 of 23,433 genes between patients with and without progression (false discovery rate, FDR<0.1). Patients with disease progression showed significant 5hmC enrichments in multiple hallmark gene sets with androgen responses as top enriched gene set (FDR=1.19E-13). Interestingly, this enrichment was driven by a subgroup of patients featuring a significant 5hmC hypermethylation in the gene sets involving AR , FOXA1 and GRHL2 . To quantify overall activities of these gene sets, we developed a gene set activity scoring algorithm and observed significant association of high activity scores with poor progression-free survival (P<0.05). Longitudinal analysis showed that the high activity scores were significantly reduced after 3-months of initiating ADT (P<0.0001) but returned to higher levels when the disease was progressed (P<0.05). This study demonstrates that 5hmC-based activity scores from gene sets involved in AR , FOXA1 and GRHL2 may be used as biomarkers to determine early treatment resistance, monitor disease progression, and potentially identify patients who would benefit from upfront treatment intensification.

Quantum teleportation based on the elegant joint measurement

As a generalization of the well-known Bell state measurement (BSM), the elegant joint measurement (EJM) is a kind of novel two-qubit joint measurement, parameterized by a subtle phase factor θ∈[0,π/2]. We investigate here the quantum teleportation based on the EJM that conceptually goes beyond BSM. It is a probabilistic teleportation caused by nonunitary quantum evolution except for the special case of the BSM with θ=π/2. We show in detail the feasible quantum circuits to realize the present scenario, where a few unitary operations and a nonunitary quantum gate are being utilized. This work suggests that it is feasible to extend the customary (BSM-based) teleportation scenarios to the EJM-based ones, and different from the BSM being single input it may provide a continuously variable input setting or even multiple measurement settings for the sender of the quantum state.

Unrevealing the Halyomorpha halys Damage Fingerprint on Hazelnut Metabolome by Multiomic Platforms and AI-Aided Strategies.

The brown marmorated stink bug (Halyomorpha halys) poses a significant threat to hazelnut crops by affecting kernel development and causing quality defects, reducing the market value. While previous studies have identified bitter-tasting compounds in affected kernels, the impact of stink bug feeding on the hazelnut metabolome, particularly concerning aroma precursors, remains underexplored. This study aims to map the nonvolatile metabolome and volatilome of hazelnut samples obtained by caging H. halys on different cultivars in two locations to identify markers for diagnosing stink bug damage. Using a multiomic approach involving headspace solid-phase microextraction (HS-SPME), comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GC × GC-TOF MS), and liquid chromatography-high-resolution mass spectrometry (LC-HRMS), both raw and roasted hazelnuts are analyzed, with artificial intelligence (AI) and machine learning tools employed to explore data correlations. The study finds that the hazelnut metabolome and volatilome exhibit high chemical complexity with significant classes of compounds such as aldehydes, ketones, alcohols, and terpenes identified in both raw and roasted hazelnuts. Multivariate analysis indicates that the orchard location significantly impacts the metabolome, followed by damage type, with cultivar differences being less pronounced. Partial least-squares discriminant analysis (PLS-DA) models achieve high predictive accuracy for orchard location (99%) and damage type (≈80%), with the roasted volatilome showing the highest predictive accuracy. Correlation matrices reveal significant relationships between raw hazelnut metabolites and aroma compounds in roasted samples, suggesting potential markers for stink bug damage that could guide the quality assessment and mitigation strategies. Data fusion techniques further enhance classification performance, particularly in predicting damage type, underscoring the potential of integrating multiple data sets for comprehensive quality assessment.

Integrating bulk and single-cell transcriptomics to elucidate the role and potential mechanisms of autophagy in aging tissue.

Autophagy is frequently observed in tissues during the aging process, yet the tissues most strongly correlated with autophagy during aging and the underlying regulatory mechanisms remain inadequately understood. The purpose of this study is to identify the tissues with the highest correlation between autophagy and aging, and to explore the functions and mechanisms of autophagy in the aging tissue microenvironment. Integrated bulk RNA-seq from over 7000 normal tissue samples, single-cell sequencing data from blood samples of different ages, more than 2000 acute myeloid leukemia (AML) bulk RNA-seq, and multiple sets of AML single-cell data. The datasets were analysed using various bioinformatic approaches. Blood tissue exhibited the highest positive correlation between autophagy and aging among healthy tissues. Single-cell resolution analysis revealed that in aged blood, classical monocytes (C. monocytes) are most closely associated with elevated autophagy levels. Increased autophagy in these monocytes correlated with a higher proportion of C. monocytes, with hypoxia identified as a crucial contributing factor. In AML, a representative myeloid blood disease, enhanced autophagy was accompanied by an increased proportionof C. monocytes. High autophagy levels in monocytes are associated with pro-inflammatory gene upregulation and Reactive Oxygen Species (ROS) accumulation, contributing to tissue aging. This study revealed that autophagy is most strongly correlated with aging in blood tissue. Enhanced autophagy levels in C. monocytes demonstrate a positive correlation with increased secretion of pro-inflammatory factors and elevated production of ROS, which may contribute to a more rapid aging process. This discovery underscores the critical role of autophagy in blood aging and suggests potential therapeutic targets to mitigate aging-related health issues.

Cross‐domain policy feedback effects on mass publics

AbstractScholars have extensively explored policy feedback effects on mass publics. However, research exploring how one policy's implementation affects public preferences for another remains limited. This study proposes a novel cross‐domain policy feedback framework, emphasizing functional and normative interdependence as two mechanisms yielding cross‐domain resource and interpretive effects. We use multiple sets of nationally representative survey data and archival records from China, a context deeply rooted in filial piety culture. We employ a fuzzy regression discontinuity design and multilevel regressions to explore the effects of the one‐child policy on public preferences for old‐age support. The findings indicate that individuals subjected to more stringent enforcement exhibit increased support for the government assuming the responsibility for old‐age support. These changes largely stem from the reduction in their actual and desired number of children. The findings have significant implications for mass feedback research and reform efforts regarding population planning and public pension systems.

MRI recovery with self-calibrated denoisers without fully-sampled data.

Acquiring fully sampled training data is challenging for many MRI applications. We present a self-supervised image reconstruction method, termed ReSiDe, capable of recovering images solely from undersampled data. ReSiDe is inspired by plug-and-play (PnP) methods, but unlike traditional PnP approaches that utilize pre-trained denoisers, ReSiDe iteratively trains the denoiser on the image or images that are being reconstructed. We introduce two variations of our method: ReSiDe-S and ReSiDe-M. ReSiDe-S is scan-specific and works with a single set of undersampled measurements, while ReSiDe-M operates on multiple sets of undersampled measurements and provides faster inference. Studies I, II, and III compare ReSiDe-S and ReSiDe-M against other self-supervised or unsupervised methods using data from T1- and T2-weighted brain MRI, MRXCAT digital perfusion phantom, and first-pass cardiac perfusion, respectively. ReSiDe-S and ReSiDe-M outperform other methods in terms of peak signal-to-noise ratio and structural similarity index measure for Studies I and II, and in terms of expert scoring for Study III. We present a self-supervised image reconstruction method and validate it in both static and dynamic MRI applications. These developments can benefit MRI applications where the availability of fully sampled training data is limited.

Muscle Oxygen Saturation Dynamics During Upper-Body Resistance Exercise

Research examining the changes in muscle oxygen saturation across multiple sets of resistance exercise is limited. The purpose of this study was to describe the physiological response of muscle oxygenation parameters during upper-body resistance exercise and examine the differential effects of relevant participant characteristics on resistance training performance and muscle oxygen saturation dynamics. Sixty-one recreationally trained men (n = 44; 21.8 ± 2.6 years) and women (n = 17; 20.2 ± 1.8 years) completed five-repetition maximum sets of barbell bench presses at a load equal to 75% 1-RM with a 2 min rest interval. Muscle oxygen saturation (SmO2) dynamics within the anterior deltoid were monitored using a portable near-infrared spectroscopy sensor. The percent change in SmO2 (∆%SmO2), the muscle oxygen re-saturation rate (SmO2RecSlope), and the highest measured SmO2 value during recovery periods (SmO2Peak) were measured. Two-way (sex [men, women] x time [sets 1–5]) repeated measures analyses of variance (ANOVA) were performed on muscle saturation variables. To examine the effect of relevant controlling variables, separate analyses of covariance (ANCOVA) with repeated measures were also performed. No differences were seen with ∆%SmO2 across sets. The main effects for sets occurred for SmO2RecSlope, whereby a decline was noted on sets 4 and 5 (p = 0.001) compared to set 1. Additionally, SmO2Peak was the lowest on set 5 (p &lt; 0.001) compared to all other sets. Moreover, body mass (p = 0.013), diastolic blood pressure (p = 0.044), and mean arterial pressure (p = 0.033) for ∆%SmO2 were the only significant covariates noted amongst the muscle oxygenation variables. In conclusion, no sex differences and only a few set differences in muscle oxygen saturation dynamics were seen without employing any covariates. Body mass, diastolic blood pressure, and mean arterial pressure were identified as factors that could influence observed responses.

Parameterization of a Model for Wild Chickpea Flowering Time by Transferring the Knowledge Learned from Multiple Sources

Building forecasting the flowering time helps researchers to create varieties with maximum efficiency and value under a changing climate. This paper proposes an algorithm for parameterization of the wild chickpea flowering time model by using machine learning through knowledge transfer to combine multiple input-target sets. The resulting model showed high accuracy based on genetic and climatic data on only the first 20 days after sowing – the average absolute error is slightly greater than 5 days, the Pearson correlation coefficient is 0.93. It was found that maximum and minimum temperatures have the strongest effect on the timing of flowering. At the same time, all weather factors by the 7–10 day from the date of sowing affect a solution of the model.

Effectiveness of website-based education program on activities of daily living and fear of falls among sub-acute stroke survivors: a pilot study in South Gujarat Region of India

BackgroundStroke survivors and their caregivers have numerous and diverse educational needs, many of which are unmet because of inadequate training from healthcare professionals. As stroke recovery and rehabilitation can continue for years post-stroke, it is critical that stroke survivors are provided with and have access to a range of support options and evidence-based information. Website-based education programs (WEP) have been adopted in recent years to manage health problems. The present pilot study aims to evaluate the effect of WEP on activities of daily living (ADLs) and the fear of falls among sub-acute stroke survivors.MethodsA total of 16 eligible post-stroke survivors were recruited from multiple study settings across Surat City of Gujarat state in western India and were randomly assigned using a lottery method to either the experimental group (n = 8) receiving WEP or the control group (n = 8) receiving conventional physiotherapy for 4 weeks. Outcome measures were the Indian Stroke Scale (ISS), Modified Barthel Index (MBI), and Falls Efficacy Scale-International (FES-I).ResultsBoth groups showed statistically significant improvements in all outcomes but the experimental group showed more significant improvements in ADLs and fear of falls among sub-acute stroke survivors (p < 0.05).ConclusionWEP for sub-acute stroke survivors is an effective intervention. It improves the performance of ADLs for stroke survivors and helps in better fall management indicating that web-based education programs shall be implemented on a regular basis to improve patient care.