Conventional Measurement Methods Research Articles

Virtual Sensing of Key Variables in the Hydrogen Production Process: A Comparative Study of Data-Driven Models.

Hydrogen is an ideal energy carrier manufactured mainly by the natural gas steam reforming hydrogen production process. The concentrations of CH4, CO, CO2, and H2 in this process are key variables related to product quality, which thus need to be controlled accurately in real-time. However, conventional measurement methods for these concentrations suffer from significant delays or huge acquisition and upkeep costs. Virtual sensors effectively compensate for these shortcomings. Unfortunately, previously developed virtual sensors have not fully considered the complex characteristics of the hydrogen production process. Therefore, a virtual sensor model, called "moving window-based dynamic variational Bayesian principal component analysis (MW-DVBPCA)" is developed for key gas concentration estimation. The MW-DVBPCA considers complicated characteristics of the hydrogen production process, involving dynamics, time variations, and transportation delays. Specifically, the dynamics are modeled by the finite impulse response paradigm, the transportation delays are automatically determined using the differential evolution algorithm, and the time variations are captured by the moving window method. Moreover, a comparative study of data-driven virtual sensors is carried out, which is sporadically discussed in the literature. Meanwhile, the performance of the developed MW-DVBPCA is verified by the real-life natural gas steam reforming hydrogen production process.

Algal Bed Region Segmentation Based on a ViT Adapter Using Aerial Images for Estimating CO2 Absorption Capacity

In this study, we propose a novel method for algal bed region segmentation using aerial images. Accurately determining the carbon dioxide absorption capacity of coastal algae requires measurements of algal bed regions. However, conventional manual measurement methods are resource-intensive and time-consuming, which hinders the advancement of the field. To solve these problems, we propose a novel method for automatic algal bed region segmentation using aerial images. In our method, we use an advanced semantic segmentation model, a ViT adapter, and adapt it to aerial images for algal bed region segmentation. Our method demonstrates high accuracy in identifying algal bed regions in an aerial image dataset collected from Hokkaido, Japan. The experimental results for five different ecological regions show that the mean intersection over union (mIoU) and mean F-score of our method in the validation set reach 0.787 and 0.870, the IoU and F-score for the background region are 0.957 and 0.978, and the IoU and F-score for the algal bed region are 0.616 and 0.762, respectively. In particular, the mean recognition area compared with the ground truth area annotated manually is 0.861. Our study contributes to the advancement of blue carbon assessment by introducing a novel semantic segmentation-based method for identifying algal bed regions using aerial images.

Distributed Embedded System for Air Quality Monitoring based on Long Range (LoRa) Technology

This study addresses the persistent challenge of urban air quality deterioration through the introduction of the Distributed Embedded System (DES). In response to limitations associated with conventional air quality measurement methods, the DES system offers a cost-effective and portable alternative for real-time monitoring. The study focuses on implementing a low-cost distributed system and developing effective communication protocols between sensor nodes. Equipped with Metal Oxide Semiconductor (MOS) sensors for pollutant gases, optical sensors for particulate matter, and meteorological sensors, the system strategically deploys multiple nodes within a 4 km range of urban areas. Real-time AQI and pollution severity have been measured for various locations. Implementation of Long Range (LoRa) communication technology allows the sensor nodes to efficiently transmit data to a central base station. Observations of received signal strength and signal quality indicate reliable and effective communication. To validate accuracy and reliability, acquired DES system data undergoes comparative analysis with data from a government-established meteorological station, revealing a strong correlation. This innovative approach presents a promising solution for widespread, continuous, and cost-effective real-time air quality monitoring in urban areas. The DES system addresses key challenges associated with air pollution, offering a portable and accessible tool that could revolutionize urban air quality management.

Noise power spectrum (NPS) in computed tomography: Enabling local NPS measurement without stationarity and ergodicity assumptions.

Conventional methods for estimating the noise power spectrum (NPS) often necessitate multiple computed tomography (CT) data acquisitions and are required to satisfy stringent stationarity and ergodicity conditions, which prove challenging in CT imaging systems. The aim was to revisit the conventional NPS estimation method, leading to a new framework that estimates local NPS without relying on stationarity or ergodicity, thus facilitating experimental NPSestimations. The scientific foundation of the conventional CT NPS measurement method, based on the Wiener-Khintchine theorem, was reexamined, emphasizing the critical conditions of stationarity and ergodicity. This work proposes an alternative framework, characterized by its independence from stationarity and ergodicity, and its ability to facilitate local NPS estimations. A spatial average of local NPS over a Region of Interest (ROI) yields the conventional NPS for that ROI. The connections and differences between the proposed alternative method and the conventional method are discussed. Experimental studies were conducted to validate the newmethod. (1) The NPS estimated using the conventional method was demonstrated to correspond to the spatial average of pointwise NPS from the proposed NPS estimation framework. (2) The NPS estimated over an ROI with the conventional method was shown to be the sum of the NPS estimated from the proposed method and a contribution from measurement uncertainty. (3) Local NPS estimations from the proposed method in this work elucidate the impact of surrounding image content on local NPSvariations. The NPS estimation method proposed in this work allows for the estimation of local NPS without relying on stationarity and ergodicity conditions, offering local NPS estimations with significantly improvedprecision.

Experimental measurement of local noise power spectrum (NPS) in photon counting detector-CT (PCD-CT) using a single data acquisition.

Accurate noise power spectra (NPS) measurement in clinical X-ray CT exams is challenging due to the need for repeated scans, which expose patients to high radiation risks. A reliable method for single CT acquisition NPS estimation is thus highly desirable. To develop a method for estimating local NPS from a single photon counting detector-CT (PCD-CT)acquisition. A novel nearly statistical bias-free estimator was constructed from the raw counts data of PCD-CT scan to estimate the variance of sinogram projection data. An analytical algorithm is employed to reconstruct point-wise covariance between any two image pixel/voxel locations and . A Fourier transform is applied to obtain the desired point-wise NPS for any chosen location . The method was validated using experimental data acquired from a benchtop PCD-CT system with various physical phantoms, and the results were compared with the conventional local NPS measurement method using repeated scans and statistical ensembleaveraging. The experimental results demonstrate that (1) the proposed method can achieve pointwise/local NPS measurement for a region of interest (ROI) located at any chosen position, accurately characterizing the NPS with spatial structures resulting from image content heterogeneity; (2) the local NPS measured using the proposed method show a higher precision in the measured NPS compared to the conventional measurement method; (3) spatial averaging of the local NPS yields the conventional NPS for a given localROI. A new method was developed to enable local NPS from a single PCD-CTacquisition.

Prediction of endovascular leaks after thoracic endovascular aneurysm repair though machine learning applied to pre-procedural computed tomography angiographs.

To predict endoleaks after thoracic endovascular aneurysm repair (TEVAR) we submitted patient characteristics and vessel features observed on pre- operative computed tomography angiography (CTA) to machine-learning. We evaluated 1-year follow-up CT scans (arterial and delayed phases) in patients who underwent TEVAR for the presence or absence of an endoleak. We evaluated the effect of machine learning of the patient age, sex, weight, and height, plus 22 vascular features on the ability to predict post-TEVAR endoleaks. The extreme Gradient Boosting (XGBoost) for ML system was trained on 14 patients with- and 131 without endoleaks. We calculated their importance by applying XGBoost to machine learning and compared our findings between with those of conventional vessel measurement-based methods such as the 22 vascular features by using the Pearson correlation coefficients. Pearson correlation coefficient and 95% confidence interval (CI) were r = 0.86 and 0.75 to 0.92 for the machine learning, r = -0.44 and -0.56 to -0.29 for the vascular angle, and r = -0.19 and -0.34 to -0.02 for the diameter between the subclavian artery and the aneurysm (Fig.3a-c, all: p < 0.05). With machine-learning, the univariate analysis was significant higher compared with the vascular angle and in the diameter between the subclavian artery and the aneurysm such as the conventional methods (p < 0.05). To predict the risk for post-TEVAR endoleaks, machine learning was superior to the conventional vessel measurement method when factors such as patient characteristics, and vascular features (vessel length, diameter, and angle) were evaluated on pre-TEVAR thoracic CTA images.

Artificial intelligence applications for accurate geothermal temperature prediction in the lower Friulian Plain (north-eastern Italy)

Geothermal energy as a sustainable and clean energy source depends on the accurate estimation of reservoir temperatures. Understanding aquifer temperatures is crucial for optimizing low-enthalpy geothermal system exploitation. Advances in predictive algorithms can improve geothermal efficiency, while conventional methods of indirect temperature measurement and assumptions in geochemical analysis lead to uncertainties. As a solution, this study presents a comprehensive evaluation of six machine learning algorithms including eXtreme gradient boosting (XGBoost), decision tree, generalized regression neural network, extreme randomized trees, radial basis function, and elastic net. We employed essential performance metrics including coefficient of determination (R2) score, root mean square error (RMSE), mean absolute error, mean absolute percentage error (MAPE), and variance accounted for (VAF) to elucidate their predictive accuracy and generalization potential in the lower Friulian Plain (north-eastern Italy) where a geothermal reservoir is present. Among the algorithms scrutinized, XGBoost emerges as a predictive exemplar, achieving a remarkable R2 score of 0.9930 on the test dataset, with consistently low RMSE of 0.788, MAE of 0.587, MAPE of 1.909, and high VAF of 99.30, reaffirming its exceptional precision and robustness. It is worth noting that the other four models show slightly weaker performance than XGBoost, while Elastic Net shows moderate predictive power, which illustrates the complexity of the database. The Wilcoxon signed-rank test confirmed the superior performance of XGBoost in estimating geothermal temperatures compared to other algorithms, with statistical evidence supporting its precision and reliability. A Monte Carlo simulation for uncertainty analysis underlined the importance of model selection, accuracy and uncertainty management in the planning of geothermal projects in the lower Friulian Plain. A sensitivity analysis was performed to identify the main factors influencing the temperature prediction. Among the parameters considered, hydrogen carbonate the highest significance at 0.51, which is essential for accurate temperature prediction because of its buffering capacity which directly influences water's thermal properties. Magnesium and electrical conductivity each contribute with 0.11, also play significant roles due to their impact on the water's heat retention and distribution capabilities. Water depth, with a value of 0.08, also has a significant influence on the temperature profiles in prediction models. In summary, the accurate prediction of XGBoost for the temperature of aquifer in carbonate reservoirs in the lower Friulian Plain, underline its value for optimizing geothermal resources and highlight most important influences on temperature.

Simultaneous Reconstruction of Multiple Time-Varying Thermal Properties Based on Translucent Materials.

In the realm of high-tech materials and energy applications, accurately measuring the transient heat flow at media boundaries and the internal thermal conductivity of materials in harsh heat exchange environments poses a significant challenge when using conventional direct measurement methods. Consequently, the study of photothermal parameter reconstruction in translucent media, which relies on indirect measurement techniques, has crucial practical value. Current research on reconstructing photothermal properties within participating media typically focuses on single-objective or time-invariant properties. There is a pressing need to develop effective methods for the simultaneous reconstruction of time-varying thermal flow fields and internal thermal conductivity at the boundaries of participating media. This paper introduces a computational model based on the numerical simulation theory of internal heat transfer systems in participating media, stochastic particle swarm optimization algorithms, and Kalman filter technology. The model aims to enable the simultaneous reconstruction of various thermal parameters within the target medium. Our results demonstrate that under varying levels of measurement noise, the inversion results for different target parameters exhibit slight oscillations around the true values, leading to a reduction in reconstruction accuracy. However, overall, the model demonstrates robustness and accuracy in ideal conditions, validating its effectiveness.

Three-dimensional information measurement and reconstruction for micro-hole array based on the modified shape from focus

The geometrical characteristics of the micro-holes and the consistency of spatial distribution among the micro-holes in the micro-hole array fabricated by laser beam drilling (LBD) directly influence the cooling performance, reliability, and longevity of the workpiece. It is of great significance to establish the relationship between the LBD processing parameters and the characteristics of the micro-hole array through the three-dimensional (3D) information measurement and reconstruction of the micro-hole array, as well as to guide and improve the LBD processing technology. However, due to the small size and high aspect ratio of the micro-holes in the micro-hole array, the 3D information measurement and reconstruction of the micro-hole array face challenges. The conventional micro-hole measurement methods suffer from low efficiency, high measurement costs, and potentially destructive measurement processes. Furthermore, the conventional micro-hole measurement methods face challenges in simultaneously obtaining the geometrical characteristics of the micro-holes and the spatial distribution among the micro-holes in the micro-hole array. Therefore, a non-contact and non-destructive 3D information measurement and reconstruction method for the micro-hole array based on the modified shape from focus is proposed. A novel focus measure operator based on grayscale difference and adaptive neighborhood size is proposed to accurately evaluate the focus value and obtain the initial focus volume matrix. A novel image processing operator based on the gradient information of the image sequence is proposed to enhance the initial focus volume matrix. The experimental results indicate that the proposed focus measure operator, combined with the novel image processing operator, is more suitable and reliable for detecting the micro-holes due to its better unimodality, higher contrast, and narrower peak width than the conventional focus measure operator. The optimal focus position of each image point is obtained using the modified Gaussian curve fitting method. On this basis, the 3D information measurement and reconstruction of the micro-hole array are realized. By comparing the results with the conventional micro-hole measurement method, the feasibility and accuracy of the proposed method have been confirmed to be an effective measurement method for the micro-hole array.

S1 vertebral bone quality score independently predicts pedicle screw loosening following surgery in patients with adult degenerative scoliosis

BACKGROUND CONTEXTMRI-based vertebral bone quality (VBQ) score is an effective predictor of pedicle screw loosening after lumbar fusion surgery and has been recognized as a valuable tool for assessing preoperative bone quality. Due to the lateral curvature of lumbar spine of degenerative scoliosis, accurate measurement of VBQ score by conventional measurement methods that capture MRI signal intensity at L1–4 may be limited under certain conditions. To this end, a new simplified S1 VBQ score method has been proposed, which is comparable to the previous L1–4 VBQ score or S1 Hounsfield unit (HU) value, and has high accuracy and reliability. PURPOSETo evaluate the predictive value of MRI-based S1 VBQ score for pedicle screw loosening following surgery for adult degenerative scoliosis (ADS). STUDY DESIGN/SETTINGRetrospective single-center cohort. PATIENT SAMPLEWe reviewed 92 patients who underwent posterior lumbar interbody fusion (PLIF) with pedicle screw fixation for ADS between January 2017 and January 2020. OUTCOME MEASURESAssociation between S1 VBQ score and pedicle screw loosening following surgery for ADS. METHODSWe retrospectively reviewed the records of all patients who performed PLIF between January 2017 and January 2020. The clear zone around pedicle screws≥1 mm can be identified as screw loosening. The S1 VBQ score is calculated from the S1 central-based non-contrast T1-weighted magnetic resonance imaging (MRI). The signal intensity (SI) of the cerebrospinal fluid (CSF) was then measured using ROI placed at the L3 level to normalize the results. Multivariate logistic regression analysis was performed to identify independent risk factors for screw loosening. Then, constructed a receiver operating characteristic (ROC) curve and determined the threshold (cut-off) of VBQ score with high sensitivity and specificity based on Youden Index. RESULTSA total of 92 patients were included, including 34 males and 58 females. The mean age of the patients was 61.23±1.23 years old. At 1-year follow-up, the screw loosening rate was 56.5% (52/92). The age and levels of fixation were higher in the loosening group than in the non-loosening group (p=.036, p=.025). Patients in the loosening group had a greater VBQ score compared to patients in the non-loosening group (3.31±0.41 vs 3.01±0.28, p=.001). Multivariate logistic regression analysis showed that advanced age (OR, 1.090; 95% CI 1.005–1.183; p=.039) and higher VBQ score (OR, 5.778; 95% CI 2.889–16.177; p=.001) were independent risk factors for screw loosening. In addition, the ROC curve were created to assess the role of VBQ score as predictors of screw loosening with a diagnostic accuracy of 74.6% (95% CI 64.2%–85.1%).The most suitable threshold for the VBQ score as determined by the Youden Index was 3.175 (sensitivity: 76.0%, specificity: 83.3%). CONCLUSIONSHigher S1 VBQ score was significantly associated with pedicle screw loosening following surgery for ADS. The S1 VBQ score can be used as an effective preoperative predictor, which has the potential clinical application.

Dual resistance Janus PDA/patterned PVDF membrane for membrane distillation with early wetting detection using electrochemical impedance spectroscopy

Membrane distillation (MD) is highly efficient in separating and removing water pollutants; however, it is susceptible to membrane wetting and fouling especially when treating low-surface tension pollutants such as oils and surfactants. To address these limitations, we fabricated a Janus polydopamine (PDA)/patterned polyvinylidene fluoride (PVDF) membrane with anti-wetting and anti-fouling properties via non-solvent- and vapor-induced phase separation. In the MD test with a NaCl feed solution with hourly sodium dodecyl sulfate injection, the Janus membrane with the hydrophobic patterned PVDF layer significantly delayed membrane wetting with only a slight trade-off in flux - an 8.6 % decrease in water vapor flux. Also, in the MD test using a NaCl feed solution containing 1000 mg L−1 of canola oil, the hydrophilic PDA layer of the Janus membrane effectively prevented the interaction between oil and the membrane surface. The wetting progression of the Janus membrane was systemically monitored using electrochemical impedance spectroscopy to identify the mechanism of its wetting behaviour and to detect and analyse the different wetting stages (i.e., non-wetting, partial wetting, and full wetting). Therefore, unlike conventional electrical conductivity measurement methods, the wetting front estimated using EIS enabled a more precise wetting detection during the DCMD operation.

Temperature dependence of polymer melt viscosity obtained based on DSC data and the consistency between thermodynamic and kinetic fragility

AbstractThe viscosity of the polymer melt has an impact on both the processing and properties of the product. However, conventional viscosity measurement methods have limitations such as strict testing conditions, large sample sizes, and long response times. This paper proposes a simplified method that utilizes differential scanning calorimetry to determine the viscosity–temperature relationship of the polymer melt. The research subjects consist of four noncrystalline polymers and five crystalline polymers. A new thermodynamic fragility formula of crystalline polymer is obtained. Additionally, high‐temperature limiting viscosity shows a good linear relationship with fragility. Compared with the traditional viscosity measurement, this method is a simpler and more effective way to model the viscosity of polymer melt and has broad prospects in application.

Cachexia-Affected Survival Based on Inflammatory Parameters Compared to Complex Conventional Nutritional Assessments in Patients with Pancreatic Cancer and Other Gastrointestinal Tumors-The CONKO 020 Investigation.

Pancreatic adenocarcinoma (PDAC) is still a complex, devastating disease. Cachexia symptoms frequently impair patient survival. This accompanying syndrome is commonly diagnosed late, when clinical signs become evident. Early diagnosis using conventional measurement methods is often difficult, and the discrimination of this disease from cancer progression is challenging and often overlaps. The aim of this study was to analyze whether conventional nutritional assessments or laboratory biomarkers are better predictive tools for the early detection of patients at risk of reduced survival. We analyzed a prospective predefined cohort of 182 patients with gastrointestinal cancer, 120 patients with PDAC and-as controls-62 patients with other gastrointestinal adenocarcinoma (oAC), from whom we have sufficient data of protocol-defined conventional nutritional assessments, clinical data, and specific laboratory parameters. at the time of tumor diagnosis, high inflammatory biomarkers (c-reactive protein (CRP), interleukin-6 (IL-6)) and albumin serum levels were associated with impaired OS in PDAC patients, but not in patients with oAC. Hemoglobin, body mass index (BMI), and bioelectrical assessments alone did not have a prognostic impact at the time of diagnosis. In a multivariate analysis, only CRP (HR 1.91 (1.25-2.92), p = 0.003) was found to be an independent prognostic factor in PDAC patients. Over the course of the disease in PDAC patients, inflammatory biomarkers, albumin, hemoglobin, and bioelectrical assessments were associated with impaired OS. In multivariate testing, CRP (HR 2.21 (1.38-3.55), p < 0.001) and albumin (HR 1.71 (1.05-2.77), p = 0.030) were found to be independent prognostic factors in PDAC patients. Specifically for PDAC patients, high inflammatory index and albumin serum levels potentially represent a sufficient early surrogate marker to detect patients at high risk of impaired OS better than complex conventional methods. These findings could help to identify patients who may benefit from early therapeutic interventions.

A temperature-decoupled impedance-based mass sensing using CBAM-CNN and adaptive weighted average preprocessing with high accuracy

Microcantilevers are widely adopted in various fields for micro-mass sensing due to their simple structure and high sensitivity. The conventional microcantilever-based measurement methods required ultra-precision temperature control. However, such a requirement could hardly be satisfied and the temperature shifting would deteriorate the accuracy of mass sensing. In this research, a new temperature decoupling method for mass sensing is proposed by taking advantage of the relative relationship of the peaks in the impedance signals. Besides, a CBAM-CNN network with an adaptive weighted average preprocessing method is developed for the evaluation of the relative relationship of the peaks. This method can enhance the features of the impedance signal and highlight critical information with limited samples. Experiments showed that robust mass sensing with accuracy up to 99.70% can be achieved over a wide temperature range from 25 to 55 ℃. In addition, the performance of the proposed network was compared to SVM, DNN, and CNN. This method offers a new mechanism for eliminating the influences of temperatures for precision cantilever-based mass sensing.

Pressure-dependent thermal conductivity transient measurement of graphene

The thermal properties of graphene determine the thermal damage evaluation of graphene diaphragm and the lifetime of graphene-based devices. The conventional transient measurement method tends to result in the low-precision thermal conductivity (κ) for suspended monolayer graphene and complex interferometry setup. Hence, in order to improve κ measurement of graphene, a simple, miniaturized and portable Fabry-Perot (F–P) resonator with 10-layer graphene is proposed to perform the actuation and vibration detection of the suspended graphene. Then, the κ value can be calculated by means of laser-heated graphene's thermomechanical response, along with the established model for thermal time constant (τ) of a uniformly heated circular film. A quadratic fit is applied to the measured κ at different air pressures and an average fitting curve is obtained, thus determining that the measured κ relatively decreases by 13.32% with the increasing air pressure outside the F–P cavity from ∼22 kPa to ∼101 kPa. Subsequently, the COMSOL heat transfer simulation confirms that convective heat transfer is the primary reason for the external air pressure affecting transient measurements of κ of graphene. Hence, after taking the intercept of the average fitting curve, the predicted κ at 0 Pa of 10-layer suspended graphene is calculated to be 194.48 W/(m·K), which is close to the fitting curve calculated from previously reported κ of graphene with different layers. This study contributes to accurate transient measurement of κ in graphene, and is also instructive for investigating transient thermal process in other two-dimensional materials.

Member separation and deformation recognition of spatial grid structures in-service

Bending deformation of members in spatial grid structures is a typical damage threatening structural safety. Conventional methods of deformation measurement have difficulty in detection member selection, high-altitude operation, or systematic error elimination. And reverse modeling technology for deformation detection is mostly utilized in entire structures or laboratory scenes. In this study, two kinds of member separation algorithms are developed based on point cloud registration and point cloud features respectively, both of which are suitable for engineering situations of insufficient point cloud density and multiple noise sources. The deformation recognition algorithm for members is improved accordingly with double judgment conditions. A field test has been carried out in a Sports Center Stadium in Guangzhou province. 7045 members are identified, and 29 crooked members are eventually recognized. The results demonstrate that the proposed algorithms can improve the efficiency of member separation and deformation recognition, and assist the monitoring and assessment of spatial grid structures in-service.

Measurement of serum hepcidin-25 by latex agglutination in healthy volunteers and patients with hematologic disorders.

Iron is an essential trace metal, vital for various physiologic processes, but excess levels can harm health. Maintaining iron homeostasis is critical, with hepcidin playing a key role. The isoform hepcidin-25 exerts the most significant influence on iron metabolism, making its serum levels a valuable diagnostic tool. However, mass-spectrometry and other conventional measurement methods can be difficult to perform, and some immunoassays lack reliability. In this study, we employed a recently developed latex agglutination method integrated with a readily available automated analyzer to quantify serum hepcidin-25 levels in both volunteers recruited from personnel of our hospital (n = 93) and patients with various hematological disorders (n = 112). Our findings unveiled a robust positive correlation between serum hepcidin-25 and ferritin, as well as C-reactive protein levels, in both volunteers and patients. Among the patients with hematological disorders, there was a noteworthy negative correlation between hepcidin-25 levels and hemoglobin concentrations, as well as reticulocyte counts. Interestingly, the hepcidin-25/ferritin ratio was remarkably low in patients with hemolytic anemia and myelodysplastic syndromes with ring sideroblasts. Our findings suggest that quantifying serum hepcidin-25 and the hepcidin-25/ferritin ratio using this method may be valuable for screening of hematopoietic diseases and other iron metabolism disorders.

Measuring the similarity between shapes of buildings using graph edit distance

ABSTRACT Building data play a crucial role in geographic information science, and building shape similarity can be used for spatial inquiry, cartographic generalization, map updating, data quality assessment, and other spatial data mining applications. However, conventional shape representation and measurement methods use geometric and statistical measures and may not effectively capture the subtle differences among building shapes. Geospatial artificial intelligence methods often overlook nuanced differences in building contours, posing a challenge when precise fine-grained measurements are required. Furthermore, they have low interpretability. This study proposes a graph edit distance-based method for measuring the similarity between building shapes. It uses geometrics characteristics to construct a graph, matches and associates similar regional features between two building contours, and defines edit costs based on geometric and positional differences that align with the Gestalt principle of visual similarity. It converts the optimal edit path problem to the maximum weighted cluster solution problem and utilizes a heuristic approach to achieve the exact optimal solution within a moderately suitable timeframe. The experimental results demonstrated that the proposed method identifies detailed differences between building contours, providing accurate similarity measurement with high interpretability.

The challenge of monitoring snow surface sublimation in winter could be resolved with structure-from-motion photogrammetry

Snow sublimation is one of the major water and energy balance processes for the snow water cycle, which induced a significant snow loss. The precise monitoring of snow sublimation is essential for quantifying the snow processes. However, the conventional measurement methods (e.g., pan observation with sporadic data or eddy covariance [EC]) are not feasible for long-term observations in harsh alpine environments). Here, two different one-camera time-lapse structure from motion (O-T-SfM) photogrammetry methods, were adopted to monitor daily snow surface sublimation at two different periods in the cold, harsh conditions at top of the August-one ice cap in the Qilian Mountains of China. The results revealed that the O-T-SfM-based surface sublimation rate estimates has a good agreement with two different methods such as pan observation and EC measurement in snowfall-free conditions and snow bedforms were abundant. The estimated sublimation rate varied from 0.05 to 2.19 mm/day at the study site. The total surface sublimation was about 16.3 mm snow water equivalent in 32 days which is a significant loss of moisture from glacier in a short period. Our results suggest that by apply appropriate estimation methods, remote O-T-SfM photogrammetry derived DEM sequence can be successfully used to monitor the daily snow surface sublimation in winter.

Development of non-contact ultrasonic IoT system for monitoring hardening process of concrete

The objectives of this study are to develop a non-contact ultrasonic IoT (Internet of Things) system and to investigate its applicability in monitoring the hardening process of cementitious materials. The conventional measurement methods, which include contact or embedded sensors with wires, have limited applicability on complex construction sites. Therefore, in this study, we propose the non-contact ultrasonic method for identifying the properties of cementitious materials using the characteristics of the leaky Rayleigh waves and demonstrate the effectiveness of an IoT system in improving the limitations of wired systems. The apparatus developed includes an ultrasonic transmitter, sensor board and operating software. First, we demonstrated that the non-contact ultrasonic IoT system delivers commands up to a distance of 20 m at a Wi-fi strength greater than −70 dBm. Furthermore, experimental validation was performed on three different cementitious materials (cement paste, mortar, and concrete) to investigate the applicability of the system.