Average Map Research Articles

Effect of time-variant NDVI on landside susceptibility: A case study in Quang Ngai province, Vietnam

Abstract Normalized difference vegetation index (NDVI) is a conditioning factor that significantly affects slope stabilization, as the low vegetation coverage can create conducive conditions for landslide occurrence. In previous studies, NDVI was often calculated from long-term average NDVI maps or specific yearly NDVI maps. However, this approach is unsuitable due to the time-varying nature of these data, influenced by numerous factors, including human activities. To solve this problem, this study uses NDVI as a time-varying factor. NDVI maps are generated from Sentinel 2 and Landsat_8 imagery at the onset of each rainy season between 2015 and 2020 in the mountainous region of Quang Ngai Province. Moreover, the landslide events that occurred within this 5-year period (2016–2020), along with a set of conditioning factors, are utilized to develop landslide susceptibility models based on three algorithms: logistic regression, support vector machine, and extreme gradient boosting (XGBoost). The obtained results demonstrate that using time-varying NDVI shows superior performance compared to using only NDVI from 2015. The outcomes also indicate that XGBoost is the most effective model. Selecting suitable NDVI maps can improve the predictive accuracy of landslide susceptibility mapping.

Wind Turbine Surface Defect Detection Method Based on YOLOv5s-L

In order to solve the problems of low efficiency, time consumption and high costs in the detection of defects on wind turbine surfaces in industrial scenarios, an improved YOLOv5 algorithm for wind turbine surface defect detection is proposed, named YOLOv5s-L. Firstly, the C3 module of YOLOv5s is replaced with the C2f module, which is more abundant in gradient flow, to enhance the ability of feature extraction and feature fusion. Secondly, the Squeeze and Excitation (SE) module is embedded in the YOLOv5 Backbone network to filter out redundant feature information and retain important feature information. Thirdly, the weighted Bidirectional Feature Pyramid Network (BiFPN) is introduced to replace the FPN + PAN, which can achieve a higher level of feature fusion while keeping the weight light. Finally, the Focal Loss function is used to replace the CIOU Loss function of the YOLOv5 algorithm to optimize the training model and improve the accuracy of the algorithm. The experimental results show that, compared with the traditional YOLOv5 algorithm, the average precision mAP is improved by 1.9%, and the frame rate FPS can reach 145 F/s without increasing the model parameters; it can satisfy the requirements for real-time, accurate detection on mobile devices. This method provides effective support for surface defect detection of wind turbines and provides reference for intelligent wind farm operation and maintenance.

A Scientific Document Retrieval and Reordering Method by Incorporating HFS and LSD

Achieving scientific document retrieval by considering the wealth of mathematical expressions and the semantic text they contain has become an inescapable trend. Current scientific document matching models focus solely on the textual features of expressions and frequently encounter hurdles like proliferative parameters and sluggish reasoning speeds in the pursuit of improved performance. To solve this problem, this paper proposes a scientific document retrieval method founded upon hesitant fuzzy sets (HFS) and local semantic distillation (LSD). Concretely, in order to extract both spatial and semantic features for each symbol within a mathematical expression, this paper introduces an expression analysis module that leverages HFS to establish feature indices. Secondly, to enhance contextual semantic alignment, the method of knowledge distillation is employed to refine the pretrained language model and establish a twin network for semantic matching. Lastly, by amalgamating mathematical expressions with contextual semantic features, the retrieval results can be made more efficient and rational. Experiments were implemented on the NTCIR dataset and the expanded Chinese dataset. The average MAP for mathematical expression retrieval results was 83.0%, and the average nDCG for sorting scientific documents was 85.8%.

Transforming Estonian health data to the Observational Medical Outcomes Partnership (OMOP) Common Data Model: lessons learned.

To describe the reusable transformation process of electronic health records (EHR), claims, and prescriptions data into Observational Medical Outcome Partnership (OMOP) Common Data Model (CDM), together with challenges faced and solutions implemented. We used Estonian national health databases that store almost all residents' claims, prescriptions, and EHR records. To develop and demonstrate the transformation process of Estonian health data to OMOP CDM, we used a 10% random sample of the Estonian population (n = 150824 patients) from 2012 to 2019 (MAITT dataset). For the sample, complete information from all 3 databases was converted to OMOP CDM version 5.3. The validation was performed using open-source tools. In total, we transformed over 100 million entries to standard concepts using standard OMOP vocabularies with the average mapping rate 95%. For conditions, observations, drugs, and measurements, the mapping rate was over 90%. In most cases, SNOMED Clinical Terms were used as the target vocabulary. During the transformation process, we encountered several challenges, which are described in detail with concrete examples and solutions. For a representative 10% random sample, we successfully transferred complete records from 3 national health databases to OMOP CDM and created a reusable transformation process. Our work helps future researchers to transform linked databases into OMOP CDM more efficiently, ultimately leading to better real-world evidence.

Electronic Product Recommendation System Using the Cosine Similarity Algorithm and VGG-16

The recommendation system is a mechanism for filtering a batch of data into numerous data sets based on what the user wants. Cosine similarity is one of the algorithms used in creating recommendation model. This algorithm employs a calculation approach between two things by measuring the cosine between the two objects to be compared. Image-based recommendation systems were recently introduced since word processing to generate recommendations had the issue of duplicating product descriptions for different types of items. Before processing with cosine similarity, image feature extraction requires the use of a deep learning algorithm, VGG16. The purpose of this research is to make it easier for customers to select the desired electronic goods by providing product recommendations based on product visual similarity. This model is able to recommend 10 products that are similar to the selected product. The presented product has a cosine value near one, and the discrepancy with the selected product's cosine value is modest. The mAP technique was used for model testing, and the smartwatch category received the greatest mAP value of 94.38%, while the headphone category had the lowest value of 70.84%. The average mAP attained is 81.50%. These findings show that mAP accuracy varies by category. This disparity is due to the unequal dataset in each category.

Comparative Root Transcriptome Profiling and Gene Regulatory Network Analysis between Eastern and Western Carrot (Daucus carota L.) Cultivars Reveals Candidate Genes for Vascular Tissue Patterning.

Carrot (Daucus carota L.) is a highly consumed vegetable rich in carotenoids, known for their potent antioxidant, anti-inflammatory, and immune-protecting properties. While genetic and molecular studies have largely focused on wild and Western carrot cultivars (cvs), little is known about the evolutionary interactions between closely related Eastern and Western cvs. In this study, we conducted comparative transcriptome profiling of root tissues from Eastern (UHSBC-23-1) and Western (UHSBC-100) carrot cv. to better understand differentially expressed genes (DEGs) associated with storage root development and vascular cambium (VC) tissue patterning. Through reference-guided TopHat mapping, we achieved an average mapping rate of 73.87% and identified a total of 3544 DEGs (p < 0.05). Functional annotation and gene ontology classification revealed 97 functional categories, including 33 biological processes, 19 cellular components, 45 metabolic processes, and 26 KEGG pathways. Notably, Eastern cv. exhibited enrichment in cell wall, plant-pathogen interaction, and signal transduction terms, while Western cv. showed dominance in photosynthesis, metabolic process, and carbon metabolism terms. Moreover, constructed gene regulatory network (GRN) for both cvs. obtained orthologs with 1222 VC-responsive genes of Arabidopsis thaliana. In Western cv, GRN revealed VC-responsive gene clusters primarily associated with photosynthetic processes and carbon metabolism. In contrast, Eastern cv. exhibited a higher number of stress-responsive genes, and transcription factors (e.g., MYB15, WRKY46, AP2/ERF TF connected via signaling pathways with NAC036) were identified as master regulators of xylem vessel differentiation and secondary cell wall thickening. By elucidating the comparative transcriptome profiles of Eastern and Western cvs. for the first time, our study provides valuable insights into the differentially expressed genes involved in root development and VC tissue patterning. The identification of key regulatory genes and their roles in these processes represents a significant advancement in our understanding of the evolutionary relations and molecular mechanisms underlying secondary growth of carrot and regulation by vascular cambium.

Defect Detection Method of Phosphor in Glass Based on Improved YOLO5 Algorithm

Phosphor in Glass (PiG) is easy to be stirred unevenly during production and processing, and improper use of instruments and other factors lead to defective products. In this paper, we propose an improved YOLOv5 target detection algorithm. Firstly, the Coordinate Attention (CA) is introduced into the backbone network to enable the network to notice detect targets in a larger range. Secondly, the Bidirectional Feature Pyramid Network (BiFPN) is used to fuse different scale information in the neck part to obtain the output feature map with rich semantic information. At the same time, the weighted bidirectional feature fusion pyramid structure adjusts the contribution of different scale input feature maps to the output by introducing weights. This optimization enhances the feature fusion effect, reduces the loss of feature information in the convolution process, and improves detection accuracy. Then, the GIOU_Loss function is replaced with the EIOU_Loss function to speed up the convergence. Finally, the comparative experiment is carried out with the self-made PiG dataset. The experimental results show that the average accuracy mAP of this method is 12.35% higher than that of the original method (YOLOv5s), with a detection speed is 53.92 FPS, aligning with the actual needs of industrial detection.

Microstructural Evolution, Intermetallic Formation, and Mechanical Performance of Dissimilar Al6061–Ti6Al4V Static Shoulder Friction Stir Welds

The present study focuses on achieving microstructural uniformity in dissimilar Al–Ti joints through static shoulder friction stir welding (SSFSW). This method restricts material mixing at the Al–Ti interface due to reduced shoulder action, resulting in peak temperatures of 317 °C (SSFSW) and 491 °C in conventional friction stir welding (CFSW). This reduction in heat input during SSFSW leads to decreased mechanical mixing, resulting in a thinner 2.169 μm intermetallic layer at interface of SSFSW compared to 5.485 μm in CFSW. The resulting weld nugget (WN) microstructure reveals the presence of Ti particles, intermetallic compounds, and fine Al‐associated grains. Rearrangement of dislocations through slip‐and‐climb mechanisms, along with distinct recrystallization processes in SSFSW, significantly reduces grain size within WN. The kernel average misorientation map highlights the presence of nonuniform dislocation concentrations at the interface of CFSW, attributed to the large Ti particles that impede the mobility of dislocations. As a result, the interface of CFSW is found to be the weakest among all the zones, eventually leading to the failure of the CFSW joint near the Ti interface after reaching an ultimate tensile strength of 259 MPa. In contrast, SSFSW welds achieve a higher tensile strength of 289 MPa.

Singular Points of the Tremor of the Earth’s Surface

A method for studying properties of the Earth’s surface tremor, measured by means of GPS, is proposed. The following tremor characteristics are considered: the entropy of wavelet coefficients, the Donoho–Johnston wavelet index, and two estimates of the spectral slope. The anomalous areas of tremor are determined by estimating the probability densities of extreme values of the studied properties. The criteria for abnormal tremor behavior are based on the proximity to, or the difference between, tremor properties and white noise. The greatest deviation from the properties of white noise is characterized by entropy minima and spectral slope and DJ index maxima. This behavior of the tremor is called “active”. The “passive” tremor behavior is characterized by the maximum proximity to the properties of white noise. The principal components approach provides weighted averaged density maps of these two variants of extreme distributions of parameters in a moving time window of 3 years. Singular points are the points of maximum average densities. The method is applied to the analysis of daily time series from a GPS network in California during the period 2009–2022. Singular points of tremor form well-defined clusters were found. The passive tremor could be caused by the activation of movement in fragments of the San Andreas fault.

Research on Improved Method based on YOLOV5s Target Detection Model

Aiming at the problem of low detection accuracy of small targets, an object detection method based on average pooling improved YOLOV5s model is proposed. The algorithm introduces the Squeeze Excitation attention module and the Efficient Intersection Over Union loss function to comprehensively improve the detection calculation efficiency and accurate deployment ability. With the development of deep learning technology, which is of great significance to improve the detection accuracy and detection rate. YOLO greatly improves detection performance, three times faster than retinanet and 2 times faster than faster-rcnn. YOLO has strong generalization ability, can be applied to different application scenarios, and is also easy to deploy. The steel surface defect public dataset was selected for verification. The results show that the improved YOLOV5s model is better than the original YOLOV5s model, the test average accuracy mAP can reach 81.8%, the average accuracy mAP of the model is increased by 7.4%, and the overall performance of the improved model is better than other conventional models.

UAV Target Detection Algorithm with Improved YOLOv7

The wide application of UAV technology in various fields makes UAV target detection crucial. In this study, we propose an improved algorithm based on YOLOv7 to enhance the performance and robustness of UAV target detection. We utilize YOLOv7 as the infrastructure and introduce BiFPN (Bi-directional Feature Pyramid Network) to enhance the feature fusion, while adding the GAM attention mechanism to the model, which is trained and evaluated using the VisDrone2019 dataset. The experimental results of this study show that the improved model achieves an average accuracy mAP value of 45.6%, which is 2.7% higher than the traditional model, and is able to detect and localize UAV targets more accurately.

MCMNET: Multi-Scale Context Modeling Network for Temporal Action Detection.

Temporal action detection is a very important and challenging task in the field of video understanding, especially for datasets with significant differences in action duration. The temporal relationships between the action instances contained in these datasets are very complex. For such videos, it is necessary to capture information with a richer temporal distribution as much as possible. In this paper, we propose a dual-stream model that can model contextual information at multiple temporal scales. First, the input video is divided into two resolution streams, followed by a Multi-Resolution Context Aggregation module to capture multi-scale temporal information. Additionally, an Information Enhancement module is added after the high-resolution input stream to model both long-range and short-range contexts. Finally, the outputs of the two modules are merged to obtain features with rich temporal information for action localization and classification. We conducted experiments on three datasets to evaluate the proposed approach. On ActivityNet-v1.3, an average mAP (mean Average Precision) of 32.83% was obtained. On Charades, the best performance was obtained, with an average mAP of 27.3%. On TSU (Toyota Smarthome Untrimmed), an average mAP of 33.1% was achieved.

Predictive Value of Doppler Renal Resistive Index for Prediction of Acute Kidney Injury in Septic Patients in Intensive Care Unit

Abstract Background Acute kidney injury (AKI) occurs as a serious complication in septic patients. In its severe form, AKI requires renal replacement therapy, which is applied in 5–13% of intensive care units (ICU) patients. AKI is common in patients confined in ICU and remains to be associated with poor outcomes. According to the current diagnostic criteria, serum creatinine (SCr) and urine volume are used as diagnostic and staging criteria for AKI. Aim of the Work To evaluate predictive value of renal resistive index in detection of acute kidney injury. Measuring flow resistance in the renal circulation, Renal Resistive Index (RRI) could become part of vital organ function assessment using Doppler ultrasound. Patients and Methods This was a prospective descriptive study conducted on 150 ICU patients with sepsis; to evaluate prediction value of renal resistive index in detection of acute kidney injury. Measuring flow resistance in the renal circulation, RRI could become part of vital organ function assessment using Doppler ultrasound. Results We found that; the mean age of all patients was (38.4 ± 13.5) years. Regarding gender of the patients, the majority (62.7%) of patients were males; while (37.3%) were females. Regarding basic clinical data; the average MAP was (57.6 ± 14.8) mmHg, the average APACHE score was (14.6 ± 3.7), and the average SOFA score was (4.2 ± 0.92). Regarding comorbidities; (24.7%) of patients had septic shock, (20%) had DM, (34%) had HTN, and (14.7%) had IHD. Conclusion High RRI on ICU admission was a significant predictor for development of AKI stage 2 ± 3 during the first week. High RRI can be used as an early warning signal RRI, because of its high specificity. A combined score including RRI, APACHE III and fluid balance improved AKI prediction, suggesting that vasoconstriction or poor vascular compliance, severity of disease and positive fluid balance independently contribute to AKI development.

A Lightweight Cherry Tomato Maturity Real-Time Detection Algorithm Based on Improved YOLOV5n

To enhance the efficiency of mechanical automatic picking of cherry tomatoes in a precision agriculture environment, this study proposes an improved target detection algorithm based on YOLOv5n. The improvement steps are as follows: First, the K-means++ clustering algorithm is utilized to update the scale and aspect ratio of the anchor box, adapting it to the shape characteristics of cherry tomatoes. Secondly, the coordinate attention (CA) mechanism is introduced to expand the receptive field range and reduce interference from branches, dead leaves, and other backgrounds in the recognition of cherry tomato maturity. Next, the traditional loss function is replaced by the bounding box regression loss with dynamic focusing mechanism (WIoU) loss function. The outlier degree and dynamic nonmonotonic focusing mechanism are introduced to address the boundary box regression balance problem between high-quality and low-quality data. This research employs a self-built cherry tomato dataset to train the target detection algorithms before and after the improvements. Comparative experiments are conducted with YOLO series algorithms. The experimental results indicate that the improved model has achieved a 1.4% increase in both precision and recall compared to the previous model. It achieves an average accuracy mAP of 95.2%, an average detection time of 5.3 ms, and a weight file size of only 4.4 MB. These results demonstrate that the model fulfills the requirements for real-time detection and lightweight applications. It is highly suitable for deployment in embedded systems and mobile devices. The improved model presented in this paper enables real-time target recognition and maturity detection for cherry tomatoes. It provides rapid and accurate target recognition guidance for achieving mechanical automatic picking of cherry tomatoes.

Cerebral autoregulation and cerebral blood flow response to mean arterial pressure challenge following induction of general anaesthesia for neuroradiology procedure

IntroductionIntraoperative hypotension is common following general anaesthesia induction with propofol, but its impact on cerebral autoregulation (CA) remains unclear. We investigate the incidence and risk factors of impaired CApost-propofol induction and its recovery after a mean arterial pressure (mAP) challenge.MethodsWe included 40 non-emergency neuroradiology surgery patients [58 (47, 58)years old., 57% women]. We recorded mAP, mean blood flow velocity in the mean cerebral artery (MCAvmean), and regional cerebral oxygen saturation (rSO2). We computed the mean flow index (Mxa) pre and post mAP challenge. Mxa &amp;gt; 0.3 defined poor CA.ResultsAfter anaesthesia induction, 21 (53%) had impaired CBF autoregulation (CA−, Mxa &amp;gt; 0.3). The average mAP was 66 ± 9 mmHg, average MCAv was 39 ± 12 cm.s−1, and rSO2 was 63 ± 7%. We found no significant difference in age, norepinephrine infusion rate, and cardiovascular risks factors were similar between CA− and CA+ (Mxa ≤ 0.3) patients. Among the 22 patients (CA−: n = 14; CA+: n = 8) undergoing mAP challenge, there was a significant Mxa improvement and MCAv increase among CA− patients, (CA−: 0.63 ± 0.18 vs. 0.28 ± 0.20, p &amp;lt; 0.001), and [absolute variation: 1 (0.7–1.5) vs. 7 (3–9) cm.sec−1], respectively.ConclusionAfter induction of general anaesthesia for neuroradiology procedure, 53% of the patients had an impaired CA, regardless of age or medical history. Importantly, a mAP challenge effectively restored CA and improved CBF.Clinical Trial Registrationidentifier, NCT04288869

An efficient method of pavement distress detection based on improved YOLOv7

Pavements play a pivotal role in infrastructure construction, so pavement distress detection (PDD) will greatly affect pavement service life and vehicle operation safety. Traditional manual detection and computer detection methods have such disadvantages as low efficiency, high cost and error-proneness. Thus, they are not suitable for high-speed detection tasks due to a large number of defects. Defect detection methods based on deep learning can achieve end-to-end target detection, generalize and monitor targets in real time. On such a basis, this paper has proposed an efficient method of PDD based on improved YOLOv7. YOLOv7, which is the best-performing object detection model in the YOLO series, is known for its high efficiency, strong scalability, and support for panoramic detection. It lays a solid foundation for enhancing PDD models. In this paper, this model will be improved based on model speed and accuracy. Firstly, SimAM attention module is employed to weight feature images, which has greatly improved model accuracy. Secondly, Ghost module in place of a partial deep convolution module is used to improve model running speed. Then, SIoU, instead of the original localization loss function, is performed to optimize the model training process. Finally, the proposed improved YOLOv7 model is applied to different road defect datasets and compared with other methods, such as Faster R-CNN, CenterNet, DETR, YOLOv6 and the original YOLOv7 model. The results show that the proposed method has ubiquitous advantages over the above-mentioned methods, with the average mAP, F1 value and FPS value of 85.8%, 0.697 and 62.13 fps respectively. Furthermore, the values of the parameters Params and FLOPs also decrease to some degree.

Early Visual Deprivation Impairs Functional Development of the Visual Ventral Stream.

To probe the dynamic alternations of neural networks in real-time visual processing after visual deprivation (VD) removal. A prospective cross-sectional study was conducted. Twenty children with a history of early binocular VD caused by congenital cataracts and 20 matched typically developing (TD) children were enrolled. The event-related potential (ERP) data were obtained via high-density electroencephalography. ERP data were analyzed based on three components (P1, N170, and P2), three test conditions (objects, human faces, and Chinese characters), and peak time and region of interest (ROI) chosen on a grand average head map collapsed from the averaged waveform of each group. Source localization and alpha power spectrum density were applied to define the functional pattern of brain areas and evaluate the attention function. The VD group showed significantly lower P1 amplitudes than the TD group under all conditions in peak ROIs, which were situated in the left occipito-temporal region. For both VD and TD groups, there were strong N170 effects in the character and human face conditions in the component's peak ROIs. Furthermore, source mapping indicated that the VD group generally showed significantly lower activation in the visual cortex and ventral stream, whereas the beyond network areas (mostly frontal areas) intensively participated in functional compensation in the VD group. The VD group showed significant poststimulus alpha desynchronization in object recognition. Our research described the mechanisms of visual networks after early binocular VD removal. Our findings may provide a new basis for the poor visual recovery after early binocular VD removal and offer clues for visual recovery strategies.

Self-attention-based long temporal sequence modeling method for temporal action detection

Temporal Action Detection (TAD) is a basic and complex task in video understanding. It aims at detecting both the localization and category of actions in a video. The anchor-free TAD methods directly predict the action classes at each location and regress the distances to the boundaries. However, current anchor-free models based on convolutional neural network encode spatiotemporal sequences by 3D convolution networks. Due to the limited receptive field and the basic prior of the translation invariance, effective long temporal sequence modeling cannot be achieved. As a result, these methods cannot effectively detect temporal boundaries. To solve this problem, we design a novel end-to-end self-attention temporal enhancement TAD model, which introduces the Temporal Enhancement module to enhance the temporal feature encoding of the videos and expand the receptive field. Extensive experiments demonstrate that the self-attention Temporal Enhancement model yields an effective improvement on previous work, which improves the performance on THUMOS14 by 1.2%, reaching 53.2% on average mAP. Meanwhile, a competitive result of 34.7% average mAP is achieved on ActivityNet-1.3.

Genome assembly and resequencing analyses provide new insights into the evolution, domestication and ornamental traits of crape myrtle.

Crape myrtle (Lagerstroemia indica) is a globally used ornamental woody plant and is the representative species of Lagerstroemia. However, studies on the evolution and genomic breeding of L. indica have been hindered by the lack of a reference genome. Here we assembled the first high-quality genome of L. indica using PacBio combined with Hi-C scaffolding to anchor the 329.14-Mb genome assembly into 24 pseudochromosomes. We detected a previously undescribed independent whole-genome triplication event occurring 35.5 million years ago in L. indica following its divergence from Punica granatum. After resequencing 73 accessions of Lagerstroemia, the main parents of modern crape myrtle cultivars were found to be L. indica and L. fauriei. During the process of domestication, genetic diversity tended to decrease in many plants, but this was not observed in L. indica. We constructed a high-density genetic linkage map with an average map distance of 0.33cM. Furthermore, we integrated the results of quantitative trait locus (QTL) using genetic mapping and bulk segregant analysis (BSA), revealing that the major-effect interval controlling internode length (IL) is located on chr1, which contains CDL15, CRG98, and GID1b1 associated with the phytohormone pathways. Analysis of gene expression of the red, purple, and white flower-colour flavonoid pathways revealed that differential expression of multiple genes determined the flower colour of L. indica, with white flowers having the lowest gene expression. In addition, BSA of purple- and green-leaved individuals of populations of L. indica was performed, and the leaf colour loci were mapped to chr12 and chr17. Within these intervals, we identified MYB35, NCED, and KAS1. Our genome assembly provided a foundation for investigating the evolution, population structure, and differentiation of Myrtaceae species and accelerating the molecular breeding of L. indica.

PENGARUH MASSAGE TEKNIK EFFLUAGE TERHADAP PENURUNAN TEKANAN DARAH PADA LANSIA DENGAN HIPERTENSI

Hypertension in the elderly is high blood pressure with a medical condition where blood pressure increases above normal, namely 140/90 mmHg which is at risk of morbidity and mortality. The purpose of this study was to determine the effect of effluage technique massage on reducing blood pressure in the elderly with hypertension in Rejosari Village RT.001 RW.003. This type of quantitative research with the method of pre-experimental design one group pretest-posttest. The research sample was determined by purposive sampling technique and obtained a sample of 55 people. Statistical test using the Wilcoxon signed rank test. The results of the study found that before the intervention was given the average MAP value was 133 mmHg and after the intervention was given, the average MAP value was 120 mmHg with a Pvalue of 0.000, because the Pvalue &lt;0.05 means that there is an effect of Effluage Massage Techniques on Reducing Blood Pressure in Elderly With Hypertension. In reducing blood pressure, non-pharmacological therapy can be done, one of which is relaxation therapy, massage with the effleurage technique, which is useful for reducing peripheral resistance and increasing the elasticity of blood vessels. There is a need for health education for people with hypertension to take advantage of massage effleurage relaxation therapy as an alternative therapy that can be used to control blood pressure so that it is within normal limits.