Important Measure Research Articles

Projected decline in the strength of vegetation carbon sequestration under climate change in India

Tropical vegetation plays a critical role in terrestrial carbon budget and supply many ecological functions such as carbon sequestration. In recent decades, India has witnessed an increase in net primary productivity (NPP), an important measure of carbon sequestration. However, uncertainties persist regarding the sustainability of these land carbon sinks in the face of climate change. The enhanced NPP is driven by the strong CO2 fertilization effect (CFE), but the temporal patterns of this feedback remain unclear. Using the carbon flux data from the Earth System Models (ESMs), an increasing trend in NPP was observed, with projections of NPP to 2.00 ± 0.12 PgCyr-1 (25 % increase) during 2021-2049, 2.36 ± 0.12 PgCyr-1 (18 % increase) during 2050-2079, and 2.67 ± 0.07 PgCyr-1 (13 % increase) during 2080-2099 in Indian vegetation under SSP585 scenario. This suggests a significant decline in the NPP growth rate. To understand the feedback mechanisms driving NPP, the relative effects of CFE and warming were analyzed. Comparing simulations from the biogeochemically coupled model (BGC) with the fully coupled model, the BGC model projected a 74.7 % increase in NPP, significantly higher than the 55.9 % increase projected by the fully coupled model by the end of the century. This indicates that the consistent increase in NPP was associated with CO2 fertilization. More importantly, results reveal that the decrease in the NPP growth rate was due to the declining contribution of CFE at a rate of -0.62 % per 100 ppm CO2 increase. This decline could be attributed to factors such as nutrient limitations and high temperatures. Additionally, significant shifts in the strength of carbon sinks in offsetting the CO2 emissions were identified, decreasing at a rate of -1.15 % per decade. This decline in the strength of vegetation carbon sequestration may increase the societal dependence on mitigation measures to address climate change.

Discussion on the status quo and problems of health risk management of hand-transmitted vibration in the workplace

The risk management in workplace is an important measure to effectively prevent and control the harm of hand-transmitted vibration. Based on the relevant developments at home and abroad, this paper expounds the risk of manual vibration operation in workplace by taking contact assessment and hazard assessment as an example. On this basis, the limit management and hierarchical management related to risk management are discussed, and the existing problems are analyzed.

Research on the Stability and Water Isolation of Waterproof Coal Pillars between Adjacent Working Faces under the Influence of Water Ponding Goaf—A Case Study

Retaining a waterproof coal pillar is an important measure to defend against water inrush accidents in mining areas and guarantee the safe mining of the next working face. In this paper, the mechanical model of the coal pillar is established and the calculation formula of the waterproof coal pillar width is derived. Then, the development of the water-conducting fracture zone of the overlying rock layer under different coal pillar widths is analyzed using numerical simulation and finally, the integrity of the coal pillar is detected using the geophysical survey method. The main conclusions are as follows: (1) According to the mechanical failure characteristics of the coal pillar, it can be divided into the plastic zone, elastic zone, and water pressure damage zone. The mechanical calculation model for each zone was established, and the formula for calculating the width of the waterproof coal pillar was obtained. (2) Numerical simulation was employed to investigate the development condition of the water conducting fracture zone in the overlying rock strata under the actual width of the waterproof coal pillar; the simulation results indicated that the water conducting fracture zone of two working faces was not connected, which can effectively prevent the accumulation of water in the 2303 goaf. (3) On-site geophysical surveys determined that the influence of water-logged goaf on the coal pillar is between 5 to 15 m; the integrity of the waterproof coal pillar is good, which effectively prevents water accumulation in the previous working face goaf and ensures safe mining in the next working face.

Surgical Antibiotic Prophylaxis: A Proposal for a Global Evidence-Based Bundle.

In the multimodal strategy context, to implement healthcare-associated infection prevention, bundles are one of the most commonly used methods to adapt guidelines in the local context and transfer best practices into routine clinical care. One of the most important measures to prevent surgical site infections is surgical antibiotic prophylaxis (SAP). This narrative review aims to present a bundle for the correct SAP administration and evaluate the evidence supporting it. Surgical site infection (SSI) prevention guidelines published by the WHO, CDC, NICE, and SHEA/IDSA/APIC/AHA, and the clinical practice guidelines for SAP by ASHP/IDSA/SIS/SHEA, were reviewed. Subsequently, comprehensive searches were also conducted using the PubMed®/MEDLINE and Google Scholar databases, in order to identify further supporting evidence-based documentation. The bundle includes five different measures that may affect proper SAP administration. The measures included may be easily implemented in all hospitals worldwide and are based on minimal drug pharmacokinetics and pharmacodynamics knowledge, which all surgeons should know. Antibiotics for SAP should be prescribed for surgical procedures at high risk for SSIs, such as clean-contaminated and contaminated surgical procedures or for clean surgical procedures where SSIs, even if unlikely, may have devastating consequences, such as in procedures with prosthetic implants. SAP should generally be administered within 60 min before the surgical incision for most antibiotics (including cefazolin). SAP redosing is indicated for surgical procedures exceeding two antibiotic half-lives or for procedures significantly associated with blood loss. In principle, SAP should be discontinued after the surgical procedure. Hospital-based antimicrobial stewardship programmes can optimise the treatment of infections and reduce adverse events associated with antibiotics. In the context of a collaborative and interdisciplinary approach, it is essential to encourage an institutional safety culture in which surgeons are persuaded, rather than compelled, to respect antibiotic prescribing practices. In that context, the proposed bundle contains a set of evidence-based interventions for SAP administration. It is easy to apply, promotes collaboration, and includes measures that can be adequately followed and evaluated in all hospitals worldwide.

Accelerated enhanced recovery after colon cancer surgery with discharge within one day after surgery: a systematic review

BackgroundRecent studies have demonstrated that accelerated enhanced recovery after colorectal surgery is feasible for specific patient populations. The accelerated enhanced recovery protocols (ERP) tend to vary, and the majority of studies included a small study population. This hampers defining the optimal protocol and establishing the potential benefits. This systematic review aimed to determine the effect of accelerated ERPs with intended discharge within one day after surgery.MethodsPubMed (MEDLINE), Embase, Cochrane and Web of Science databases were searched using the following search terms: colon cancer, colon surgery, accelerated recovery, fast track recovery, enhanced recovery after surgery. Clinical trials published between January 2005 – February 2023, written in English or Dutch comparing accelerated ERPs to Enhanced Recovery After Surgery (ERAS) care for adult patients undergoing elective laparoscopic or robotic surgery for colon cancer were eligible for inclusion.ResultsThirteen studies, including one RCT were included. Accelerated ERPs after colorectal surgery was possible as LOS was shorter; 14 h to 3.4 days, and complication rate varied from 0–35.7% and readmission rate was 0–17% in the accelerated ERP groups. Risk of bias was serious or critical in most of the included studies.ConclusionsAccelerated ERPs may not yet be considered the new standard of care as the current data is heterogenous, and data on important outcome measures is scarce. Nonetheless, the decreased LOS suggests that accelerated recovery is possible for selected patients. In addition, the complication and readmission rates were comparable to ERAS care, suggesting that accelerated recovery could be safe.

Calculation of the System Unavailability Measures of Component Importance Using the D2T2 Methodology of Fault Tree Analysis

A recent development in Fault Tree Analysis (FTA), known as Dynamic and Dependent Tree Theory (D2T2), accounts for dependencies between the basic events, making FTA more powerful. The method uses an integrated combination of Binary Decision Diagrams (BDDs), Stochastic Petri Nets (SPN) and Markov models. Current algorithms enable the prediction of the system failure probability and failure frequency. This paper proposes methods which extend the current capability of the D2T2 framework to calculate component importance measures. Birnbaum’s measure of importance, the Criticality measure of importance, the Risk Achievement Worth (RAW) measure of importance and the Risk Reduction Worth (RRW) measure of importance are considered. This adds a vital ability to the framework, enabling the influence that components have on system failure to be determined and the most effective means of improving system performance to be identified. The algorithms for calculating each measure of importance are described and demonstrated using a pressure vessel cooling system.

Psychometric Properties of the Subhealth Measurement Scale V1.0 for Assessing Suboptimal Health Status of Midwives: A Multicentre Cross-Sectional Study

Aim. Midwives are prone to suboptimal health status (SHS), while there is a lack of unified measurement standard of SHS for them to early detect and prevent SHS. This study aimed to evaluate validity and reliability of Subhealth Measurement Scale V1.0 (SHMS V1.0) in midwives. Design and Methods. The cross-sectional study had 842 midwives from 46 midwifery institutions in southeastern China completing the SHMS V1.0 with 39 items. Internal consistency, test-retest reliability, and concurrent validity of SHMS V1.0 were assessed. Project analysis and confirmatory factor analysis (CFA) were performed to assess construct validity. Results. SHMS V1.0 had acceptable reliability with Cronbach’s alpha and a test-retest reliability coefficient of 0.908 and 0.804, respectively. The correlation coefficients between each item and corresponding dimension and those between dimension and corresponding subscale were all greater than 0.4. The CFA supported the structure of SHMS V1.0 with strong factor loadings and excellent fit indexes in the first-order and modified second-order factor model, illustrating that SHMS V1.0 had good construct validity among midwives. The concurrent validity for SHMS V1.0 showed large correlation with FS-14, but the social subscale showed medium correlations with fatigue, inferring that SHMS V1.0 can more comprehensively reflect SHS of midwives than FS-14, especially for social health symptoms. Practice Implications. SHMS V1.0 with excellent psychometric properties can accurately and reliably evaluate the SHS of midwives. These findings of our study provide an important measurement tool and inspiration for midwives to early detect and prevent SHS.

Deep Learning and YOLOv8 Utilized in an Accurate Face Mask Detection System

Face mask detection is a technological application that employs computer vision methodologies to ascertain the presence or absence of a face mask on an individual depicted in an image or video. This technology gained significant attention and adoption during the COVID-19 pandemic, as wearing face masks became an important measure to prevent the spread of the virus. Face mask detection helps to enforce mask-wearing guidelines, which can significantly reduce the spread of respiratory illnesses, including COVID-19. Wearing masks in densely populated areas provides individuals with protection and hinders the spread of airborne particles that transmit viruses. The application of deep learning models in object recognition has shown significant progress, leading to promising outcomes in the identification and localization of objects within images. The primary aim of this study is to annotate and classify face mask entities depicted in authentic images. To mitigate the spread of COVID-19 within public settings, individuals can employ the use of face masks created from materials specifically designed for medical purposes. This study utilizes YOLOv8, a state-of-the-art object detection algorithm, to accurately detect and identify face masks. To analyze this study, we conducted an experiment in which we combined the Face Mask Dataset (FMD) and the Medical Mask Dataset (MMD) into a single dataset. The detection performance of an earlier research study using the FMD and MMD was improved by the suggested model to a “Good” level of 99.1%, up from 98.6%. Our study demonstrates that the model scheme we have provided is a reliable method for detecting faces that are obscured by medical masks. Additionally, after the completion of the study, a comparative analysis was conducted to examine the findings in conjunction with those of related research. The proposed detector demonstrated superior performance compared to previous research in terms of both accuracy and precision.

Construction Quality Control for Rutting Resistance of Asphalt Pavement Using BIM Technology

During the course of building asphalt pavement, a lack of quality control will lead to the abandonment of the asphalt mixtures. One of the most common problems with asphalt pavement is rutting. Improving the construction’s quality is an important measure to reduce rutting. The purpose is to ensure the high-temperature durability of asphalt mixtures during the construction workflow to reduce the waste of asphalt mixtures, as well as to provide a methodology for the current monitoring of the quality based on the building information modeling (BIM). Rutting resistance was appraised utilizing the static uniaxial creep examination. Oblique photography technology was used to obtain terrain data. The software of Revit 2016 was used to build the spatial model of highways and bridges. The results show that the size distribution of particles, the asphalt proportion, and the forming specimen’s temperature are the vital elements influencing the high-temperature behavior. The gradation was identified as the most important factor. The second was the asphalt binder content. Gradation variation should be given more consideration during paving using asphalt mixtures. Furthermore, the developed BIM platform can also monitor rutting resistance to reduce rework during construction.

Do Clean Toilets Help Improve Farmers’ Mental Health? Empirical Evidence from China’s Rural Toilet Revolution

The mental health crisis poses a major challenge to global sustainable development. In response, the United Nations has launched an ambitious plan to “reshape the environment that affects mental health”. Clean toilets are an important measure of civilization, yet it is unclear whether they are helping to alleviate the global mental health crisis. Therefore, using data from a large sample of rural Chinese households and introducing an instrumental variables approach based on the IV-Tobit model to address endogeneity, this study quantitatively discusses that clean latrines have a positive effect on the mental health of farm households. The results showed the following: (1) 89.8% of Chinese rural households had clean toilets, while the average farm household depression index was 12.568; and (2) clean toilets helped to improve the mental health of farmers, which means that the depression index of farmers with clean toilets decreased by 66.9% compared to farmers without clean toilets. The findings of this study can inform the development of policies to mitigate the global mental health crisis and contribute to the resilience of global development.

Superhydrophobic and anticorrosion Ni-B/WC@MoS2 composite coatings deposited via jet electrodeposition assisted with ultrasonics

BackgroundElectrodeposited surface coatings are used as an important protective measure for metal surfaces such as bearings. Enhancing the corrosion resistance of the coating is the key to practical application. MethodsNi-B/WC@MoS2 composite coatings deposited on 45-steels were prepared by ultrasound-assisted jet electrodeposition combined with hydrothermal modification of nanoparticles. The impact of ultrasonic power on the composite coatings’ physical phase structure, micro-morphology, surface roughness, surface hydrophobicity and corrosion mechanism were evaluated. Significant FindingsUltrasound-assisted power helped to enhance the deposition of WC@MoS2 powder for the composite coatings. The coating surface prepared with the assistance of 270 W ultrasound showed a dense "cauliflower" shape with a superhydrophobic surface contact angle of 153.7° The composite coating assisted by 270 W ultrasonic power was found to have the largest corrosion potential (-218 mV) and the smallest corrosion current (6.8E-07 A/cm2) after the electrochemical corrosion test. The polarization resistance of the coating was 35,568 Ω·cm2, and the measured corrosion rate was 8.0E-3 mm/a. It was three orders of magnitude lower than that of the composite coating without ultrasonic power assistance. The Ni-B/WC@MoS2 coatings obtained with jet electrodeposition assisted by 270 W ultrasonic wave showed good corrosion resistance.

Analgesic Efficacy of Suprascapular Nerve Block versus Combined Suprascapular and Axillary Nerve Block in Patients Undergoing Shoulder Arthroscopy: A Prospective, Randomized, Comparative Study

Abstract Background: Immediate post-operative pain can be severe and is an important outcome measure for patients in shoulder arthroscopic surgeries. Interscalene block is the most widely used and studied technique for post shoulder arthroscopy pain relief and is associated with certain unpleasant adverse effects. Suprascapular nerve block (SSNB) has been suggested as an alternative and safer block. As the suprascapular nerve contributes to 70% of the sensory nerve supply to the shoulder joint, and the axillary circumflex nerve is the other major nerve contributing to 25%; therefore, a combined block of these two nerves should provide more effective pain relief than SSNB alone. Aim: This study aimed to compare the efficacy of combined SSNB and axillary nerve block (ANB) with SSNB alone for analgesia after shoulder arthroscopy in terms of level of pain scores, total analgesic consumption, and patient satisfaction in 24 h. Methods: Forty-one patients posted for shoulder arthroscopy were randomly allocated into two groups, Group S (n = 20) receiving SSNB and Group C (n = 21) receiving combined SSNB and ANB. Visual Analog Scale (VAS) both at rest and during movement at 1, 3, 6, 12, 24 h, and at the time of first demand, total consumption of analgesics in 24 h and patient satisfaction score at 12 and 24 h were noted. Statistical Analysis: The data was analyzed by Student’s t-test, Mann-Whitney U test, and Fisher’s exact test using Graph Pad PRISM 9, a p<0.05 was considered significant. Results: The VAS scores both at rest and during movement were significantly less in Group C at 1, 6, 12 h, and at the time of the first demand of analgesic (p<0.05). Compared to Group S, Group C had a longer pain-free duration (154.3 ± 11.41 min vs. 36.60 ± 6.553 min; P < 0.001) and lesser cumulative consumption of pethidine (300.0 ± 8.894 mg vs. 336.8 ± 13.27 mg; P = 0.0256). The satisfaction level was better in Group C at 12 h (P = 0.002) but comparable to the Group S at 24 h (P = 0.0673). Conclusion: Combined SSNB and ANB provided better pain relief in first 12 h, better patient satisfaction in the first 12 h and decreased overall consumption of analgesics in the first 24 h compared to SSNB alone.

Spatial patterns of β-diversity under cumulative pressures in the Western Mediterranean Sea

Understanding the spatial dynamics of biodiversity is an essential issue in marine ecology and requires combining information at local and regional scales. β-diversity is an important measure of biodiversity that informs on the differences in community composition between sites and, thus, in the species turnover in the community structure. In this study, we analysed and predicted the spatial patterns of β-diversity for fishes, invertebrates and the demersal assemblage along the Iberian Mediterranean coast. We used Bayesian Bootstrap Generalized Dissimilarity Models (BBGDMs) to study the effects of environment and human pressures on the β-diversity of invertebrate, fishes and the entire demersal assemblage from 1994 to 2015 using different time windows to account for temporal variability. Then, we used these relationships to predict the spatial patterns of β-diversity in the whole Iberian Mediterranean coast. Our results highlighted that the regional spatial patterns of β-diversity were best described by bathymetry and a cumulative index of coastal impacts. We identified specific regions with the highest β-diversity in the study area, which were complementary to hotspots of species richness and presented different degree of overlapping with existent marine protected areas. Overall, our study illustrates that by modelling spatial turnover using β-diversity we can better understand and predict spatial variation of biodiversity and the effects of particular variables, providing relevant information to end-users and policy makers for designing specific spatial conservation and management strategies.

Constitutive models for confined elastomeric layers: Effects of nonlinearity and compressibility

Elastomers tend to undergo large deformations accompanied by small volumetric changes. For elastomeric layers sandwiched between rigid plates, large deformations can be significantly limited by the constraints imposed by the plates. Further, those constraints can be enhanced by material’s inability to undergo large volumetric changes. From this perspective, it is appropriate to examine the validity of constitutive models, in which an elastomer is treated as incompressible, for analysis of the confined layers. Here, this issue is addressed by considering the mechanical response of sandwiched elastomeric layers using three constitutive models. The first one, referred to as compressible neo-Hookean, is regarded as exact. The other two models are regarded as approximations. Of those two, the first one neglects nonlinearity and the second one neglects compressibility. Accordingly, the modeling errors associated with the former are treated as measures of importance of nonlinearity, and the modeling errors associated with the latter are treated as measures of importance of compressibility. The modeling errors are evaluated using the force–displacement curve and the mean stress at the layer center as the quantities of interest. Numerical results are presented for rubber and polydimethylsiloxane (PDMS), characterized by Poisson’s ratios ν=0.4999 and ν=0.49, respectively. It is shown that, even when the forces applied to the plates are large, considering nonlinearity is important for thick but not for thin layers. In contrast, considering compressibility is important for thin layers. The need for considering compressibility is further assessed by introducing a competition parameter, which reinforces the notion that compressibility is important for modeling PDMS layers and thin rubber layers.

Monitoring Lubrication and Wear in-situ by triboelectrification Under Vacuum Conditions

Maintaining the condition of the friction pair is an important measure to reduce mechanical failure. This paper integrates a vacuum elastohydrodynamic oil film measurement device and a current amplifier to simultaneously observe the optical interference image, friction coefficient and triboelectric signal during the friction process. In a high vacuum environment, the discharge phenomenon of steel balls corresponds to the time when the friction pair wears. Changes in the friction state in high vacuum environments have a significant impact on triboelectricity. Based on the relationship between the two, a sensor for real-time monitoring of the friction and wear status of the friction pair is designed, which provides a reliable real-time monitoring method for friction pair monitoring in vacuum machinery.

The Development Path and Challenges of Digital Transformation of Small and Medium Sized-Enterprises-Taking Fat East and FAW-Volkswagen as Examples

In recent years, the digital economy and artificial intelligence have relied on some digital technologies to develop rapidly. The world is also in the post-pandemic era of gradual economic recovery, and many companies want to achieve their own transformation through unique pathways. Being a significant driver of China's employment and economy, small and medium-sized enterprises should take the lead in completing digital transformation. This paper analyzes in detail from the three dimensions of development status, development path, obstacles and challenges, and selects some examples and real data queried from China Metrology and Testing Network for investigation. The survey results show that small and medium-sized businesses are still in the first half of the digital transformation road. In the meantime, they will encounter many problems in the process of transformation, such as weak digital technology foundation, talent shortage, capital chain obstacles, and low general awareness. In addition, the study finds that the government's collaboration with enterprises is an important measure to encourage the digital transformation of small and medium-sized companies. With the goal to encourage the digital transformation of small and medium-sized enterprises, the government and relevant departments should issue and implement corresponding policies to pave the way further effectively for enterprises. Enterprises should also actively exert their subjective initiative and take every step down-to-earth.

Study on the Response of Vegetation Water Use Efficiency to Drought in the Manas River Basin, Xinjiang, China

Ecosystem water use efficiency (WUE) is an important measure of the degree of water–hydrogen coupling and an important indicator for assessing ecosystem responses to climate change. Drought adversely affects ecosystem security, particularly in irrigated agricultural areas; therefore, understanding the relationship between WUE and drought is important. This study revealed the spatial and temporal characteristics of drought in the Manas River Basin, Xinjiang, China, from 2001 to 2020 through multi-source data using standardised anomaly indices and mutation detection. It also quantitatively analysed the hysteresis effect and resilience characteristics of drought for different vegetation types in the study area. The results showed that droughts at a severe level occurred less frequently in most of the study area on average from 2001 to 2020, and that droughts in the vegetation growing season occurred more frequently, particularly in grasslands; the frequency of droughts in woodlands was low. Furthermore, the lag in WUE to drought occurred on a 3-month scale and accounted for 64.0% of the total watershed area. Finally, 38.16% of the regional vegetation ecosystems in the Manas River Basin exhibited drought resistance. In conclusion, our results provide novel insights into the water-use strategies of plants in the study area and will help facilitate WUE optimisation.

A Novel Approach for Automatic Detection of Driver Fatigue Using EEG Signals Based on Graph Convolutional Networks.

Nowadays, the automatic detection of driver fatigue has become one of the important measures to prevent traffic accidents. For this purpose, a lot of research has been conducted in this field in recent years. However, the diagnosis of fatigue in recent research is binary and has no operational capability. This research presents a multi-class driver fatigue detection system based on electroencephalography (EEG) signals using deep learning networks. In the proposed system, a standard driving simulator has been designed, and a database has been collected based on the recording of EEG signals from 20 participants in five different classes of fatigue. In addition to self-report questionnaires, changes in physiological patterns are used to confirm the various stages of weariness in the suggested model. To pre-process and process the signal, a combination of generative adversarial networks (GAN) and graph convolutional networks (GCN) has been used. The proposed deep model includes five convolutional graph layers, one dense layer, and one fully connected layer. The accuracy obtained for the proposed model is 99%, 97%, 96%, and 91%, respectively, for the four different considered practical cases. The proposed model is compared to one developed through recent methods and research and has a promising performance.

Does teachers’ motivation have an impact on students’ scientific literacy and motivation? An empirical study in Colombia with data from PISA 2015

In this study we use data from the Programme for International Student Assessment (PISA) to investigate the effect of teachers’ motivation on students’ scientific literacy and motivation in Colombia. These relationships are explored using a multilevel modeling framework and through the lens of Self-Determination Theory. Although difficulties in achieving education quality in developing and emerging economies are commonly attributed to teacher motivation issues, and important policy measures are implemented based on this assumption, this topic remains largely empirically unexplored. The purpose of the study is to contribute to fill this gap and provide empirically based insights for a broader and more informed dialogue regarding the effect of motivation in the development of scientific literacy, and to the design and implementation of evidence-based policies, instructional practices, and interventions. In this analysis, we did not find a significant relationship between teacher motivation and either students’ scientific literacy or motivation. However, students’ interest in science and sense of self-efficacy were significantly associated with their own achievement. The results also show that teacher-directed instruction is the strongest predictor of scientific literacy as opposed to inquiry-based teaching. However, inquiry-based teaching was found to be a positive predictor of increased students’ motivation.

An Improved Filtering Method and Application of Landslide Deformation Monitoring

Real-time early warning based on deformation monitoring is an important measure to reduce the threat related to landslides. However, due to the complexity of landslide deformation, equipment accuracy, and data transmission interference, the displacement obtained by existing monitoring equipment has random errors, resulting in inaccurate landslide warning. In this paper, a tangent angle model based on the improved filtering method is proposed for the early warning of landslides. An improved filtering method is proposed based on the least-square method to smooth the measurement errors of monitoring equipment, considering both “data smoothing effect” and “accelerated deformation real-time determination”. By comparing the least-squares method and the improved filtering method, the results demonstrate that the improved filtering method can more effectively smooth the fluctuation of the deformation rate. The amplitude of the error-induced tangent angle warning value fluctuation can be reduced, which provides correct early warning. The improved method provides a new approach for the real-time filtering of subsequent landslide deformation data.