Measurement Points Research Articles

Effects of Surface Crack Shape on Fracture Behavior of Oil Pipelines Based on the MMC Criterion.

This study employs a hybrid numerical-experimental calibration method based on phenomena to determine the fracture parameters of the Modified Mohr-Coulomb (MMC) model. Using a self-developed VUMAT subroutine and the element deletion technique, the fracture process of a wide plate pipeline is thoroughly analyzed. This study investigates the impact of various crack shapes on the fracture response under tensile loading and the influence of surface crack size on the initiation location of a wide plate. These results demonstrate the calibrated MMC fracture model's accurate prediction of the toughness fracture behavior of X80 pipeline steel. Under equal area conditions of the dangerous section, circular cracks exhibit lower bearing capacity compared to elliptical cracks. Elliptical cracks predominantly propagate in the thickness direction, whereas circular cracks show nearly uniform growth in all directions. Furthermore, when the crack depth is less than half of the wall thickness, the damage accumulation value at the midpoint of the crack front is maximized; conversely, when the crack front is closer to the internal measurement point of the wide plate, the damage accumulation value is maximized.

Contaminant mass discharge estimation of a sulfonamide plume by use of hydraulic profiling tool (HPT) and fluorescence techniques

The contaminant mass discharge is a relevant metric to evaluate the risk that a groundwater plume poses to water resources. However, this assessment is often vitiated by a high uncertainty inherent to the assessment method and often limited number of measurement points to carry out the assessment. Direct-Push techniques in combination with profiling tools and dedicated sampling can be an interesting alternative to increase the measurement point density and hence reduce the mass discharge uncertainty. The main objective of our study was to assess if DP logging and sampling could be employed to get a reasonable estimate of contaminant mass discharge in a large sulfonamide contaminant plume (> 1500 m wide), compared to a more traditional approach based on monitoring wells. To do so, an Hydraulic Profiling Tool (HPT) logging with a dedicated site calibration was used to estimate the hydraulic conductivity field. The sulfonamide concentrations were inferred from the compound fluorescence properties measured by laboratory spectrofluorometry (λEx / λEm = 255/340 nm) and a dedicated log-log linear regression model. Our results show that HPT-derived hydraulic conductivity values are in good agreement with the monitoring well results, and within the order of magnitude reported in similar studies or indirect geophysical techniques. Fluorescence appears as a powerful proxy for the sulfonamide concentration levels. Ultimately, the contaminant mass discharge estimate from HPT and fluorescence techniques lies within a factor 2 from the estimate by monitoring wells, with 549 [274–668] and 776 [695–879] kg/yr respectively. Overall, this study highlights that DP logging tools combined with indirect methods (correlation with fluorescence) could provide a relevant contaminant mass discharge estimate for some optically active substances, given that a proper calibration phase is carried out.

Exploring the footprint representation of microwave radiance observations in an Arctic limited-area data assimilation system

Abstract. The microwave radiances are key observations, especially over data-sparse regions, for operational data assimilation in numerical weather prediction (NWP). An often applied simplification is that these observations are used as point measurements; however, the satellite field of view may cover many grid points of high-resolution models. Therefore, we examine a solution in high-resolution data assimilation to better account for the spatial representation of the radiance observations. This solution is based on a footprint operator implemented and tested in the variational assimilation scheme of the AROME-Arctic (Application of Research to Operations at MEsoscale – Arctic) limited-area model. In this paper, the design and technical challenges of the microwave radiance footprint operator are presented. In particular, implementation strategies, the representation of satellite field-of-view ellipses, and the emissivity retrieval inside the footprint area are discussed. Furthermore, the simulated brightness temperatures and the sub-footprint variability are analysed in a case study, indicating particular areas where the use of the footprint operator is expected to provide significant added value. For radiances measured by the Advanced Microwave Sounding Unit-A (AMSU-A) and Microwave Humidity Sounder (MHS) sensors, the standard deviation of the observation minus background (OmB) departures is computed over a short period in order to compare the statistics of the default and the implemented footprint observation operator. For all operationally used AMSU-A and MHS tropospheric channels, it is shown that the standard deviation of OmB departures is reduced when the footprint operator is applied. For AMSU-A radiances, the reduction is around 1 % for high-peaking channels and about 4 % for low-peaking channels. For MHS data, this reduction is somewhere between 1 %–2 % by the footprint observation operator.

Experimental study using phantom models of cerebral aneurysms and 4D-DSA to measure blood flow on 3D-color-coded images.

The current 3D-iFlow application can only measure the arrival time of contrast media through intensity values. If the flow rate could be estimated by 3D-iFlow, patient-specific hemodynamics could be determined within the scope of normal diagnostic management, eliminating the need for additional resources for blood flow rate estimation. The aim of this study is to develop and validate a method for measuring the flow rate by data obtained from 3D-iFlow images - a prototype application in Four-dimensional digital subtraction angiography (4D-DSA). Using phantom model and experimental circuit with circulating glycerin solution, an equation for the relationship between contrast media intensity and flow rate was developed. Applying the equation to the aneurysm phantom models, the derived flow rate was evaluated. The average errors between the derived flow rate and setting flow rate became larger when the glycerin flow and the X-rays from the X-ray tube of the angiography system were parallel to each other or when the measurement point included overlaps with other contrast enhanced areas. Although the error increases dependent on the imaging direction and overlap of contrast enhanced area, the developed equation can estimate the flow rate using the image intensity value measured on 3D-iFlow based on 4D-DSA.

Traveling wave network location method based on adaptive waveform similarity

High-voltage transmission technology can effectively address the issue of uneven spatial and temporal energy distribution, leading to its rapid development in recent years. To address the challenge of accurately identifying the source of the second traveling wave head in complex transmission network scenarios with existing single-ended fault location methods, a traveling wave network fault location method based on adaptive waveform similarity is proposed. The paper analyzes the propagation process of traveling waves in transmission lines and quantitatively derives the time-domain expression of the traveling wave waveform. The BFS algorithm is enhanced by incorporating the propagation characteristics of traveling waves, allowing for the determination of all paths from any location in the topological network to the measurement points. Based on the path information and the derived expression, the traveling wave waveform at the measurement points for the fault location is calculated. An optimization algorithm is used to iteratively solve for unknown parameters such as fault location, traveling wave speed, and fault point information, with the objective of maximizing the similarity between the adaptive waveform and the real waveform by adaptively adjusting the waveform shape. When the similarity between the adaptive waveform and the real waveform is maximized, the adaptive fault location is identified as the actual fault location. Verified through the PSCAD simulation platform, this method can achieve accurate location under different fault conditions.

Changes in health-related quality of life, depression, and fear of progression during oncological inpatient rehabilitation and beyond: a longitudinal study

PurposeStudies evaluating oncological inpatient rehabilitation rarely include follow-up intervals beyond 6 months and larger proportions of patients other than those with breast cancer. Therefore, this study investigated changes in health-related quality of life (HRQoL), depression, and fear of progression of patients with breast, colorectal, or prostate cancer from the beginning to the end of oncological rehabilitation and a 9-month follow-up.MethodsThree hundred seventy-seven patients with breast, colorectal, or prostate cancer undergoing oncological inpatient rehabilitation (median age 61 years, 49% female) completed the EORTC QLQ-C30, the PHQ-9, and the FoP-Q-SF at each measurement point. Data analysis used 3 (tumor site) × 3 (time of measurement) repeated measures ANCOVAs with patient age and time since diagnosis as covariates. At each time point, we also compared our sample to the general population on the measures used.ResultsHaving controlled for the covariates, we found significant effects of tumor site, which were small except for Diarrhea. Effects of time of measurement were often significant and in part at least medium in size indicating improvement of HRQoL and depression during rehabilitation. At follow-up, some HRQoL domains and depression deteriorated. Women with breast cancer, in particular, showed a greater decrease in emotional functioning then. Compared to the general population, the sample’s HRQoL and depression were significantly worse on most occasions.ConclusionOncological inpatient rehabilitation may improve HRQoL. The subsequent and in part differential deterioration in some HRQoL domains suggests a need for further follow-up care within survivorship programs.

Multicohort study testing the generalisability of the SASKit-ML stroke and PDAC prognostic model pipeline to other chronic diseases

ObjectivesTo validate and test the generalisability of the SASKit-ML pipeline, a prepublished feature selection and machine learning pipeline for the prediction of health deterioration after a stroke or pancreatic adenocarcinoma...

The use of infrared thermography as an indicator of methane production in hair sheep.

Infrared thermography may be an alternative technology for measuring the amount of CH4 produced and has the advantages of low cost, speed and efficiency in obtaining results. The study's objective was to determine if the infrared thermography is adequate for predicting the emission of CH4 in hair sheep and the best time after feeding to carry out the measurement. Twelve Santa Inês lambs (females, non-pregnant, with twelve months old and mean body weight of 39.3 ± 2.1kg) remained for two days in respirometric chambers, in a semi-closed system, to determine the CH4 production. The animals were divided into two treatments, according to the diet provided. During this period, seven thermographic photographs were taken, at times - 1h, -0.5h, 0h, 0.5h, 1h, 2h, 3h, 5h, and 7h, according to the feeding time, defined as 0h. CH4 production was measured over 24h. Thermographic images measured the maximum, minimum, average and point temperatures at the left and right flanks. The temperature difference between the left and right flanks (left minus right) was calculated each time. Pearson correlation coefficients, multiple regression and principal component analysis were carried out in SAS®. The best prediction of emission intensity of CH4 (kg of CH4 per dry matter intake) was obtained at 3h after feeding: CH4/DMI = 13.9016-0,38673 * DifP2 + 3.39089 * DifMed2 (R² = 0.48), using the difference between left and right flanks for point and average temperature measures. Therefore, infrared thermography can be used as an indicator of CH4 production in hair sheep three hours after feeding.

Intensity-dependence of auditory-evoked potentials might present an early surrogate marker for post-stroke depression

ObjectivePost-stroke depression (PSD) is a common stroke complication, associated with severe physical and cognitive impairment. Low central serotonergic tone, associated with depression, inversely correlates with the intensity-dependence of auditory-evoked potentials (IDAP). Aim of this study was to investigate IDAP’s usability as early surrogate marker for PSD development by assessing the correlation between IDAP early after stroke and the occurrence of PSD from 4 weeks after stroke. MethodsWe assessed auditory-evoked potentials (AEP) and depressive symptoms using the Montgomery-Åsberg Depression Rating Scale (MADRS) at day 1 – 3 and > 30 after stroke onset. IDAP was calculated as the linear slope of the N1-P2 amplitude/stimulus intensity function (ASF). Results37 patients completed the study. We diagnosed 7 patients with PSD, defined as MADRS-score≥ 7 at follow-up. The PSD group showed significantly steeper ASF slopes at admission compared with the non-depressed group (p = 0.007). We also found a positive correlation between ASF slopes on first and MADRS-scores on last measurement point for all stroke patients as a group (p = 0.007). ConclusionsThe study findings support the hypothesis that IDAP can predict the development of depressive symptoms following stroke and may therefore serve as an early surrogate marker for PSD. SignificanceThis is the first longitudinal study to assess the relationship between IDAP and PSD.

Large deformation problems arising from deep excavation in silt strata: A case study in Shenzhen, China

Deep excavations in silt strata can lead to large deformation problems, posing risks to both the excavation and adjacent structures. This study combines field monitoring with numerical simulation to investigate the underlying mechanisms and key aspects associated with large deformation problems induced by deep excavation in silt strata in Shenzhen, China. The monitoring results reveal that, due to the weak property and creep effect of the silt strata, the maximum wall deflection in the first excavated section (Section 1) exceeds its controlled value at more than 93% of measurement points, reaching a peak value of 137.46 mm. Notably, the deformation exhibits prolonged development characteristics, with the diaphragm wall deflections contributing to 39% of the overall deformation magnitude during the construction of the base slab. Subsequently, numerical simulations are carried out to analyze and assess the primary factors influencing excavation-induced deformations, following the observation of large deformations. The simulations indicate that the low strength of the silt soil is a pivotal factor that results in significant deformations. Furthermore, the flexural stiffness of the diaphragm walls exerts a notable influence on the development of deformations. To address these concerns, an optimization study of potential treatment measures was performed during the subsequent excavation of Section 2. The combined treatment approach, which comprises the reinforcement of the silt layer within the excavation and the increase in the thickness of the diaphragm walls, has been demonstrated to offer an economically superior solution for the handling of thick silt strata. This approach has the effect of reducing the lateral wall displacement by 83.1% and the ground settlement by 70.8%, thereby ensuring the safe construction of the deep excavation.

Comparison of the reverse scan technique with an intraoral scanner and the traditional impression technique

Intraoral scanners have many advantages but have limited applicability for extensive tooth-supported or implant-supported prostheses because of merging errors. The purpose of this study was to compare the reverse scan technique (RST) with an intraoral scanner using the traditional impression technique both in vitro and in vivo. A participant was scanned 10 times with an intraoral scanner. Then, the RST was performed 3 times in the oral cavity. One of the intraoral scans was chosen to make a 3-dimensionally (3D) printed cast. The printed cast was scanned 10 times with a laboratory scanner (Control cast) and 10 times with an intraoral scanner (IOS cast). The RST was performed 10 times (RST cast), and 10 dental impressions were made using the traditional splinted technique on the same cast (Impression cast). A laboratory scanner was used to obtain standard tessellation language (STL) files of the tested methods. A newly developed pyramid replacement method was used to evaluate accuracy. This method uses modified virtual abutments with integrated pyramids that determine the exact measurement points. The obtained data were processed using Procrustes analysis and statistically analyzed (α=.05). The Procrustes distances were Control cast 0 to 0.4µm (median 0.3µm), RST cast 5.6 to 6.9µm (median 6.2µm), Impression cast 5.4 to 7.1µm (median 6.5µm), and IOS cast group 4.5 to 41.2µm (median 5.8µm). In the participant, the Procrustes distance values were RST intraoral 9.5 to 9.6µm (median 9.5µm) and IOS intraoral 5.7 to 18.3µm (median 10.9µm). The reverse scan technique is an acceptable fully digital replacement for traditional impression making. Using an IOS to obtain a 3D cast of an implant-supported interim prosthesis with scannable implant analogs is not recommended. It is more beneficial to use laboratory scanners rather than intraoral scanners for the digitization of precision improvement devices such as bonded interim implant-supported prostheses with a scannable implant analog or solid index.

Low constraint fracture toughness testing for master curve reference temperature determination using 10 mm-thick SE(B) and SE(T) specimens

The constraint correction methodology for defect assessment is not widely applied in fitness-for-service codes in the nuclear industry and there are a limited number of testing standards to account for low-constraint testing. In this work, fracture toughness testing was performed with two different types of alloy steels. SE(B), C(T), and SE(T) specimen configurations were used, along with varying a/W ratios and specimen sizes, allowing for varying levels of constraint. New quality assurance measures related to selection of testing temperature, crack front straightness, and compliance for low constraint specimens are suggested. The results show that the SE(B) specimens are sensitive to CMOD measurement point location. Future work should be focused on improving and validating the instrumentation and analysis practices for low constraint specimens. Yet, the obtained SE(T) T0 values follow models predicting the effect of constraint loss developed using larger specimens. The work impacts the development of quality criteria for future low constraint testing standards.

Estimating the Ebro river discharge at 1 km/daily resolution using indirect satellite observations

Estimating river discharge Q at global scale from satellite observations is not yet fully satisfactory in part because of limited space/time resolution. Furthermore, on highly anthropized basins, it is essential to anchor the analysis to reliable Q measurements. Gauge networks are however very sparse and limited in time, and SWOT (Surface Water Ocean Topography) river discharge estimates at global scale are not yet available. The method proposed here is able to obtain continuous daily Q estimates at 1 km/daily resolution, using indirect satellite data and ground-based estimates. We focus here on the Ebro. Over such an anthropized basin (e.g. change of land use, irrigation), the exploitation of 205 available gauges at their nominal resolution (i.e., daily point measurements) is a necessity. The hydrological Continuum model is used to help interpolate spatially and temporally the observations into our optimal interpolation scheme. The proposed Q-mapping is similar to an assimilation scheme were Earth observations (precipitation, evapotranspiration and total water storage change) and model simulations are constrained by in situ gauge measurements. The Q estimates are evaluated using a rigorous leave-one-out experiment, showing a good agreement with the in situ data: a correlation of 0.72 (median), and a 75th percentile of Nash-Sutcliffe Efficiency up to 0.62. Our spatio-temporal continuous Q estimates at high spatial/temporal resolution can describe complex continental water dynamics, including extreme events. SWOT estimates will soon be available, at the global scale but with irregular space/time sampling: our method should help exploit them to obtain a regular space-temporal description of the water cycle at high resolution.

Failure mode and load prediction of steel bridge girders through 3D laser scanning and machine learning methods

AbstractCorrosion poses a significant threat to the longevity of steel bridges, impacting overall structural integrity. To effectively assess the structural condition of corroded steel bridges, conventional methods rely on visual inspections or single point measurements. To enhance and modernize this approach, this study introduces a novel framework integrating laser scanning data, computational models, and convolutional neural networks (CNNs). The CNN models are trained on a data set consisting of more than 1400 artificial corrosion scenarios generated by parameterizing real scan data from naturally corroded girders. This innovative method predicts the residual capacity and failure mode of corroded beam ends, achieving a low error rate of up to 3.3%. Unlike established evaluation procedures, the proposed evaluation framework directly utilizes post‐processed laser scanner output, eliminating the need for feature extraction and calculations.

Learning styles unmasked: Conceptual change among pre-service teachers using podcasts and texts

BackgroundThe learning styles myth is prevalent among (pre-service) teachers. Research shows that texts and podcasts refuting misconceptions and contrasting them with scientific concepts can effectively dissolve such misconceptions through cognitive conflicts, encouraging the reflection of one's beliefs. However, it is unclear if texts and podcasts differ in refuting pre-service teachers' misconceptions. AimsWe investigated if conceptual change texts and podcasts have different potential for inducing conceptual change among pre-service teachers regarding their beliefs in the learning styles myth, compared to an expository information presentation immediately and up to eight weeks after the intervention. Sample116 pre-service teachers participated in the short term, and 100 in the long term. MethodsThe experimental study used a 2 x 2 x 4 mixed-design (manner of information presentation: conceptual change vs expository; medium: podcast vs text; time: immediately before and after the treatment as well as four and eight weeks later). Participants were randomly assigned to one of four conditions. Students' beliefs were measured at all four measurement points. ResultsThe conceptual change conditions led to a stronger decrease in students' beliefs than the expository conditions in the short term (p < 0.01, η2 = 0.08) and the long term (p = 0.03, η2 = 0.04). Text and podcast were equally effective in the short term (p = 0.62, η2 = 0.00, BF01 = 9.13) and the long term (p = 0.67, η2 = 0.00, BF01 = 39.94). ConclusionPodcasts and texts using conceptual change strategies can effectively dispel pedagogical misconceptions among pre-service teachers up to eight weeks post-intervention.

One-Dimensional Electrical Resistivity Prospecting for Small Dam Projects: A case Study Smaquli Dam, East Erbil City, Kurdistan Region of Iraq

Abstract: This study employs the One-Dimensional Vertical Electrical Resistivity technique to elucidate the subsurface stratigraphy and structures beneath the axis and abutments of the suggested Smaquli dam, situated to the east of Erbil City in Kurdistan Region of Iraq (KRI). Three traverses were conducted using a Schlumberger array. Data interpretation was performed using the Russian commercial software, IPI2WIN. Each measurement point featured a maximum total electrode spread ranging from 400-600m, reaching an investigation depth of 130m to 150m. The inter-distance between successive measurement points varied from 16m to 17.5m. Geo-electrical sections were generated by interpreting the data, revealing three distinct lithological groups: alluvial deposits (upper first group) with a thickness ranging from 3 to 10 meters, marl and marly limestone of the Shiranish Formation (second group) with thickness ranging from 18 up to 110 meters, and dolomitic limestone of the Bekhme Formation (third group) which its thickness is not defined. These lithological groups exhibited significant resistivity contrasts, rendering the resistivity method effective for delineating their interfaces. The resistivity of the upper horizon ranged from 18 to 136 Ω·m, the second horizon exhibited values between 19 and 304 Ω·m, while the lower third horizon showed high resistivity values ranging from 97 to over 50000 Ω.m.. The relatively low resistivity values of the lower horizon (Bekhme Formation) are interpreted as fractured and water-saturated limestones, whereas the high values are indicative of bituminous material. A correlation was established between the geo-electrical sections and borehole geologic columns, demonstrating consistency in indicating the presence of two rock units within the uppermost 20m, which corresponds to the borehole depth.

Comprehensive numerical modeling analysis and experimental validation of a multi-turn pulsating heat pipe

The aim of this paper is to present the results of the experimentally validated numerical code designed for thermal-fluid computations in pulsating heat pipes using CFD techniques. The analysis was carried out using the OpenFOAM software, where an algorithm was developed to simulate multiphase flow with phase change and coupled heat transfer between the fluid and solid domains. The article meticulously delineates the steps involved in implementing and validating the model, commencing from basic comparative tests like the Stefan problem, progressing through Scriven’'s bubble growth, and culminating in an experiment with single- and multi-turn pulsating heat pipes. The algorithm has been refined by including a convergence criterion based on the Galusinski-Vigneaux number, which allows faster results and reduces the likelihood of numerical anomalies. Finally, an experiment was carried out to evaluate the thermal efficiency of a two-loop heat pipe on a specially designed experimental set-up constructed by the authors. In the experiment, ethanol was utilized as the working fluid, operating within a temperature range of 20 to 90 °C. The experiment was carried out with three distinct filling ratios: 50 %, 62.5 %, and 75 %. The validated parameters included pressure and temperature at the measurement points and the experimentally derived thermal efficiency results were compared with the numerical results. The model showed an error of 7.06 ± 3.73 %.

Anthropometric Measures, Cardiometabolic and Hepatic Indices, and Cut-off Points for Predicting Type 2 Diabetes Mellitus in Southwest Iran: A Cross-Sectional Study from the Enrolment Phase of the Hoveyzeh Cohort Study

Background: Cut-off points for anthropometric measures associated with diabetes may vary across different ethnic groups. Objectives: This study aimed to identify the optimal cut-off values for these measures and their relationship with cardiometabolic and hepatic indices to predict type 2 diabetes mellitus (T2DM) in southwest Iran. Methods: This cross-sectional study was conducted in Hoveyzeh, Iran, and included a total of 7,300 individuals (1,607 with T2DM and 5,693 healthy participants). Anthropometric, cardiometabolic, and hepatic indices were calculated. Results: The visceral adiposity index (VAI) showed the highest correlation with cardiometabolic indices, such as the cardiometabolic index and lipid accumulation product. The conicity index (CI) had the strongest correlation with hepatic indices, including the hepatic steatosis index and the Alcoholic Liver Disease/Non-alcoholic Fatty Liver Disease (ALD/NAFLD) index. The atherogenic index of plasma was the most significant predictor of T2DM in the Hoveyzeh population for both men (OR: 3.39; CI: 2.38 - 4.81) and women (OR: 5.68; CI: 4.20 - 7.68). The optimal cut-off values for several anthropometric measures were higher in men than in women: BMI (28.0 vs. 25.48), Waist/Height ratio (0.64 vs. 0.56), Weight-adjusted waist index (11.83 vs. 10.76), CI (1.35 vs. 1.29), body roundness index (8.43 vs. 7.33), relative fat mass (44.63 vs. 28.5), and lipid accumulation product (67.23 vs. 67.04). Conversely, the cut-off values for optimal waist circumference (99.45 vs. 98.35), Waist/Hip ratio (0.99 vs. 0.96), VAI (2.22 vs. 2.19), and abdominal volume index (20 vs. 19.6) were higher in women than in men. Conclusions: The atherogenic index of plasma is a highly predictive indicator of T2DM. Our results suggest that novel anthropometric and cardiometabolic indices could serve as accessible and cost-effective strategies for assessing health outcomes related to T2DM.

A novel method for tomato stem diameter measurement based on improved YOLOv8-seg and RGB-D data

The automatic acquisition of crop information can promote the rapid development of precision agriculture. Tomatoes, as a representative greenhouse crop, can demonstrate their growth status and overall health could be exhibited by measuring the diameters of their main stems. Automated measurement of the diameters of the trunk stems can not only reduce labor costs, but also enhance efficiency, thereby facilitating the improvement of tomato cultivation and planting management. Therefore, this study proposes a novel method based on an instance segmentation algorithm combined with RGB-D data to measure the diameter of a tomato trunk. First, we utilize the improved YOLOv8-seg to acquire the masks and bounding boxes of the buds and stems. Namely, we replace the SPPF module with Soft-SPPF to enhance the model’s capability to extract multi-scale features. Additionally, we improve the neck layer using cross-stage and weighted feature fusion to enhance the feature fusion effect. Then, we design a method to calculate the diameter of the tomato trunk by utilizing the instance segmentation results and depth information. Specifically, we first obtain the measurement point and the ROI (Region of Interest) to be measured by identifying the intersection between the bounding box of the bud and the straight line fitted by the stem mask in an image. We then filter out irrelevant depth information using the mask within the ROI, optimize the coordinate values of the measurement point, and calculate the main stem diameter. The results demonstrate that the measurements obtained in this study have a RMSE (Root Mean Square Error) of 1.5 mm and a MAPE (Mean Absolute Percentage Error) of 12.37 % compared with the manual measurements. Compared with the method based on object detection, the direct acquisition of contour information for the target instances by the instance segmentation algorithm reduces the algorithmic complexity of the subsequent processing. The proposed method can offer precise and reliable information on the diameter of the main stem of a tomato in real-world scenarios. It can be extended to other types of crops in greenhouses.

A missing data processing method for dam deformation monitoring data using spatiotemporal clustering and support vector machine model

Deformation monitoring is a critical measure for intuitively reflecting the operational behavior of a dam. However, the deformation monitoring data are often incomplete due to environmental changes, monitoring instrument faults, and human operational errors, thereby often hindering the accurate assessment of actual deformation patterns. This study proposed a method for quantifying deformation similarity between measurement points by recognizing the spatiotemporal characteristics of concrete dam deformation monitoring data. It introduces a spatiotemporal clustering analysis of the concrete dam deformation behavior and employs the support vector machine model to address the missing data in concrete dam deformation monitoring. The proposed method was validated in a concrete dam project, with the model error maintaining within 5%, demonstrating its effectiveness in processing missing deformation data. This approach enhances the capability of early-warning systems and contributes to enhanced dam safety management.