Spatial Error Research Articles

The population percentile allowance method for determining systematic spatial error tolerances for temporary intensity modulated brachytherapy.

Multiple approaches are under development for delivering temporary intensity modulated brachytherapy (IMBT) using partially shielded applicators wherein the delivered dose distributions are sensitive to spatial uncertainties in both the applicator position and shield orientation, rather than only applicator position as with conventional high-dose-rate brachytherapy (HDR-BT). Sensitivity analyses to spatial uncertainties have been reported as components of publications on these emerging technologies, however, a generalized framework for the rigorous determination of the spatial uncertainty tolerances of dose-volume parameters is needed. To derive and present the population percentile allowance (PPA) method, a generalized mathematical and statistical framework to evaluate the tolerance of temporary IMBT approaches to spatial uncertainties in applicator position and shield orientation. A mathematical formalism describing geometric applicator position and shield orientation shifts was derived that supports straight and curved applicators and applies to serial and helical rotating shield brachytherapy (RSBT) and direction modulated brachytherapy (DMBT). The PPA method entails defining the percentage of a patient population receiving a given therapy that is, allowed to receive dose-volume errors in the target volume and specified organs at risk of a defined percentage or less, then determining what combinations of applicator position and shield orientation systematic errors would be expected to produce that outcome in the population. The PPA method was applied to the use case of multi-shield helical 169 Yb-based RSBT for cervical cancer, with 45° and 180° shield emission angles. A total of 37 cervical cancer patients were considered in the population, with average (±1standarddeviation) HR-CTV volumes of 79 cm3 ±37 cm3 and optimized baseline treatment plans (no spatial uncertainties applied) created for each patient to meet dose-volume requirements of 85 GyEQD2 (equivalent uniform dose in 2Gy fraction), with D2cc tolerance doses of 90GyEQD2 , 75GyEQD2 , and 75GyEQD2 for bladder, rectum, and sigmoid colon, respectively. For the PPA requirement that 90% of cervical cancer patients receiving multi-shield helical RSBT could have a maximum dose-volume uncertainty of 10% for high-risk clinical target volume (HR-CTV) D90 (minimum dose to hottest 90%) and bladder, rectum, and sigmoid colon D2cc (minimum dose to hottest 2 cm3 ), the tolerance systematic applicator position and shield orientation uncertainties were approximately±1.0mm and±4.25°, respectively. For±1.5mm and±5° systematic applicator position and shield orientation tolerances, 90% of the patients considered would have a maximum dose-volume uncertainty of 12.8% or less. The PPA method was formalized to determine the temporary IMBT spatial uncertainty tolerances that would be expected to result in an allowed percentage of a population of patients receiving relative dose-volume errors above a defined percentage. Multi-shield, helical 169 Yb-based RSBT for cervical cancer was evaluated and tolerances determined, which, if applied on each treatment fraction, would represent an extreme situation. The PPA method is applicable to a variety of temporary IMBT approaches and can be used to rigorously determine the design parameters for the delivery systems such as mechanical driver motor accuracy, shield angle backlash, applicator rotation, and applicator fixation stability.

Econometric Modeling of Creative Industries Concentration Process in the Siberian and the Urals Single-Industry Towns

Creative industry is considered the driver of modern urban development. It raises the new wave of issues of re-industrialization policy in single-industry towns. Nevertheless, the algorithms of current mathematical modeling in regional economies are not complex enough, leaving out spatial errors and variety in models used. We present eight steps of econometric analysis, considering local-level data. For the research, a balanced data panel was formed for 38 single-industry towns in Siberia and the Urals in Russia, for the period of 2013–2017. For mathematical modeling of the process of concentration of creative industries in single-industry towns, first, we present the specific indices of concentration, variety, and spatial disparities. Then, we test the full list of possible models for the variables. Then, we evaluate the regional offset. We describe the sufficient F-test, Hausman test and Breusch–Pagan Lagrange multiplier tests, choosing the most appropriate model. Finally, we evaluate the spatial autorepression of residuals. This algorithm allows us prove the data period and identify the tendency of spatial heterogeneity growth. We assume it to be the growing spillover effect in creative industries. At the same time, despite the positive trend of decreasing concentration of creative industries in single-industry towns, mono-industry continues to have a meaningful impact on their development, which forms the basis of path dependence. In this regard, the main actor of development in towns is city-forming enterprise, through the tools of corporate social responsibility. In view of the latter, it is proposed to develop tools for corporate creative responsibility in single-industry organizations within cities and regions. Finally, the general concern about the growth of spatial differentiation at the level of cities and regions is not yet significant.

Assessment of UAS Photogrammetry and Planet Imagery for Monitoring Water Levels around Railway Tracks

High water levels near railway tracks can be a major factor affecting the safety of train passage. Water conditions near the tracks are normally monitored through visual inspections. However, this method is limited in spatial coverage and may not provide comparable information over time. We evaluated the utility of satellite imagery (Planet Dove constellation at 3 m pixel size) at the landscape level to assess overall water surface area along railway tracks. Comparatively, we evaluated the use of Structure- from-Motion 3D point clouds and high spatial detail orthomosaics (3 cm) generated from a commercial off-the-shelf Unmanned Aerial System (UAS) (DJI M300 RTK) for measuring vertical water level changes and extent of surface water, respectively, within the right-of-way of a railway line in Ontario, Canada, in areas prone to high water level and flooding. Test sites of varied lengths (~180 m to 500 m), were assessed four times between June and October 2021. Our results indicate that the satellite imagery provides a large-scale overview regarding the extent of open water in wetlands at long distances from the railway tracks. Analysis of the UAS derived 3D point cloud indicates that changes in water level can be determined at the centimeter scale. Furthermore, the spatial error (horizontal and vertical alignments) between the multi-temporal UAS data collections between sites was less than 3 cm. Our research highlights the importance of using consistent UAS data collection protocols, and the significant potential of commercial off-the-shelf UAS systems for water level monitoring along railway tracks.

A Method for Spatiotemporally Merging Multi-Source Precipitation Based on Deep Learning

Reliable precipitation data are essential for studying water cycle patterns and climate change. However, there are always temporal or spatial errors in precipitation data from various sources. Most precipitation fusion methods are influenced by high-dimensional input features and do not make good use of the spatial correlation between precipitation and environmental variables. Thus, this study proposed a novel multi-source precipitation spatiotemporal fusion method for improving the spatiotemporal accuracy of precipitation. Specifically, the attention mechanism was used to first select critical input information to dimensionalize the inputs, and the Convolutional long-short-term memory network (ConvLSTM) was used to merge precipitation products and environmental variables spatiotemporally. The Yalong River in the southeastern part of the Tibetan Plateau was used as the case study area. The results show that: (1) Compared with the original precipitation products (IMERG, ERA5 and CHIRPS), the proposed method has optimal accuracy and good robustness, and its correlation coefficient (CC) reaches 0.853, its root mean square coefficient (RMSE) decreases to 3.53 mm/d and its mean absolute error (MAE) decreases to 1.33 mm/d. (2) The proposed method can reduce errors under different precipitation intensities and greatly improve the detection capability for strong precipitation. (3) The merged precipitation generated by the proposed method can be used to describe the rainfall–runoff relationship and has good applicability. The proposed method may greatly improve the spatiotemporal accuracy of precipitation in complex terrain areas, which is important for scientific management and the allocation of water resources.

Publishing Australian Marine Data to OBIS: Twenty Years of Lessons Learnt

In 2003, the Australian Antarctic Data Centre published the first Australian dataset of seabirds from the Southern Ocean to OBIS (Ocean Biodiversity Information System) via DiGIR (Distributed Generic Information Retrieval). The dataset initially had 17 fields with an emphasis on counts of individuals. Standards evolved and with the development of the IPT (Integrated Publishing Toolkit) by GBIF (Global Biodiversity Information Facility) around 2008, large datasets could be published. OBIS subsequently adopted the IPT as the preferred publishing tool for providers to use. In 2016, the Darwin Core Event core with the OBIS Extended Measurements and Facts extension was released (De Pooter et al. 2017), meaning that richer and more comprehensive datasets could be published via the IPT. It is only recently that the biological aggregators (e.g., OBIS, GBIF) are looking at enhancing functionality to report this data. The Australian OBIS Node (OBIS-AU), hosted by CSIRO NCMI (the Commonwealth Science and Industrial Research Organisation National Collections and Marine Infrastructure Business Unit) now manages an Australian region marine biodiversity IPT with 30 million records from over 450 datasets. In the last 12 months, using the GBIF DNA Derived Data Extention, the OBIS-AU Node has published extensive eDNA datasets to OBIS with sequences and DNA related metadata. OBIS-AU has developed tools and procedures to ensure that data is of the best possible quality before it is published. Issues covered include preventing the duplication of data, preserving context, enhancing data once published with improvements in publication schemas, matching taxa, and identification of temporal or spatial errors.

Spatial econometric modeling of malaria distribution in Burkina Faso

This study focuses on the spatial distribution of malaria rate in Burkina Faso. For this purpose, we used 2020 geographical and confirmed malaria cases data from the National Institute of Statistics and Demography of Burkina Faso. We also accessed climate data on the French weather history website. We deployed relevant spatial statistical tools to address the notions of neighborhood matrix, spatial autocorrelation, and spatial heterogeneity between geographical observations. Ordinary Least Squares (OLS) regression model, Spatial Error Model (SEM), Spatial Autoregressive model (SAR), and the spatial Durbin Model (SDM) were computed using cross-validation to ensure the reliability of our findings. The Akaike information criterion (AIC) was used to select the most appropriate model for our study. The specification tests conclude that there is a spatial dependence between the observations. The SDM was chosen as the best-fitting data for modeling geolocated malaria rates. This outcome reveals that environmental indicators, population literacy rate, and rural population size by region significantly affect the country’s geolocated malaria rate. Policymakers can use these findings to make informed decisions related to malaria spread.

SCE-SLAM: a real-time semantic RGBD SLAM system in dynamic scenes based on spatial coordinate error

Simultaneous localization and mapping (SLAM) is one of the prerequisite technologies for intelligent mobile robots to accomplish various tasks in unknown environments. In recent years, many excellent SLAM systems have emerged, but most of them have a basic assumption that the environment is static, which results in their poor performance in dynamic environments. To solve this problem, this paper presents SCE-SLAM: a novel real-time semantic RGB-D SLAM system that is built on the RGB-D mode of ORB-SLAM3. SCE-SLAM tightly combines semantic and geometric information. Considering the real-time requirements, the semantic module provides semantic prior knowledge for the geometric module using the latest and fastest object detection network YOLOv7. Then, a new geometric constraint method is proposed to filter dynamic feature points. This method takes full advantage of depth images and semantic information to recover three-dimensional (3D) feature points and the initial camera pose. A 3D coordinate error is used as a threshold, and SCE-SLAM removes dynamic points using the K-means clustering algorithm. In this way, SCE-SLAM effectively reduces the impact of dynamic points. Furthermore, we validate SCE-SLAM with challenging dynamic sequences of the TUM dataset. The results demonstrate that SCE-SLAM significantly improves the localization accuracy and system robustness in all kinds of dynamic environments.

Socioeconomic and environmental determinants of foot and mouth disease incidence: an ecological, cross-sectional study across Iran using spatial modeling

Foot-and-mouth disease (FMD) is a highly contagious animal disease caused by a ribonucleic acid (RNA) virus, with significant economic costs and uneven distribution across Asia, Africa, and South America. While spatial analysis and modeling of FMD are still in their early stages, this research aimed to identify socio-environmental determinants of FMD incidence in Iran at the provincial level by studying 135 outbreaks reported between March 21, 2017, and March 21, 2018. We obtained 46 potential socio-environmental determinants and selected four variables, including percentage of population, precipitation in January, percentage of sheep, and percentage of goats, to be used in spatial regression models to estimate variation in spatial heterogeneity. In our analysis, we employed global models, namely ordinary least squares (OLS), spatial error model (SEM), and spatial lag model (SLM), as well as local models, including geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR). The MGWR model yielded the highest adjusted {R}^{2} of 90%, outperforming the other local and global models. Using local models to map the effects of environmental determinants (such as the percentage of sheep and precipitation) on the spatial variability of FMD incidence provides decision-makers with helpful information for targeted interventions. Our findings advocate for multiscale and multidisciplinary policies to reduce FMD incidence.

A spatial panel autoregressive model specification with inverse quantile separation distances of locations

This paper proposes an alternative spatial weight that efficiently captures a spatial dependence. In the past, researchers often used sparse or inverse distance spatial weights. A dense spatial weight is defined by partitioning the separation distances between locations based on quantile values over large spatial scales, where each partition forms conjoint neighborhood sectors and the weights of the respective inverse quantile of separation distances are assigned to each sector. Instead of joint modeling of the spatiotemporal process, a simultaneous spatial panel model is employed after the panel component is imposed on the proposed spatial weight using Kronecker product to perform maximum likelihood estimation and Bayesian inference via MCMC Gibbs method. The specification also involves space, time, and space–time simultaneous components. The performance of the models for the proposed spatial weight is compared with the existing spatial weights using parameter bias. A smaller value of the bias close to zero indicates a stronger value of the spatial error parameter for the proposed spatial weight over the existing spatial weights. It also induces spatial auto-correlations both in the spatial panel data of neighboring locations as well as in the errors. Thus, the proposed method affirms the best efficiency for the dynamic combined spatial panel autoregressive model with random effect specification over the lag and error models, and fixed effect specification.

The Effects of Urban Sprawl on Electricity Consumption: Empirical Evidence from 283 Prefecture-Level Cities in China

Under the urban development trend of sprawl, improving energy use efficiency is a proper way to promote green and low-carbon construction in cities. This paper uses panel data from 283 prefecture-level and above cities in China from 2008 to 2019 to measure the urban sprawl index, and analyze the spatial-temporal evolution law of urban sprawl and electricity consumption. The relationship between urban sprawl and electricity consumption is empirically examined, and the differential effect of urban sprawl on electricity consumption is analyzed. Finally, the impact of urban sprawl on electricity consumption based on a spatial perspective is explored in depth by establishing a spatial error model. We found the following: (1) The levels of urban sprawl and urban electricity consumption are on the rise. The spatial distribution of urban sprawl is more dispersed, and cities with high electricity consumption levels are mostly concentrated in the eastern coastal areas. (2) Urban sprawl exacerbates electricity consumption, and this conclusion is still robust after a series of robustness tests were conducted and endogeneity issues were taken into account. In terms of the influence mechanism, urban sprawl mainly affects electricity consumption by changing the allocation of land resources, increasing the dependence on private transportation, and inhibiting green technology innovation. (3) The incremental effect of urban sprawl on electricity consumption is more pronounced in cities with high sprawl levels, weak environmental regulations, and low green innovation levels, as well as in west cities. (4) Urban sprawl and electricity consumption both have a significant positive spatial correlation. Electricity consumption of cities is not only related to their own regions but also influenced by the adjacent regions, and the spatial correlation is mainly reflected in the random error term. This paper deepens the understanding of the basic laws of urban sprawl affecting urban low-carbon development, which also has implications for new urbanization strategies and green development.

Policies for carbon-zero targets: Examining the spillover effects of renewable energy and patent applications on environmental quality in Europe

This study examined the spillover effects of CO2 and the marginal effects of trade and renewable energy in 29 countries in Europe using the spatial panel econometric models namely; the spatial error model, spatial lag model, and spatial Durbin model. The study further applied the pooled OLS, random, and fixed effects from the non-spatial models for comparative analysis. The Wald and likelihood tests showed that the dynamic spatial Durbin approach was the best model for the study. Furthermore, the fixed-effects model was selected as the most appropriate model for explaining the elasticities of the exploratory variables by the Hausman test. The results suggest that growing growing renewable energy in a country results in a decrease of carbon emissions in that country and neighboring countries, respectively. Additionally, the findings support the claim that CO2 emissions from one particular area spread to the next nation and the far-off trading partner. According to the findings, to achieve sustainable growth, regulatory agencies must take steps to reduce CO2 emissions from both within the target country and from other neighboring countries, suggesting that cooperation is the best option for mitigating environmental issues efficiently. Considering all the evidence, it is reasonable for national economies to cooperate for environmental protection purposes with their neighbors and create a commmon regulatory framework for efficient mitigation of carbon emissions.

Development of spatial econometric models for estimating the bicycle sharing trip activity

In developing countries, the implementation of bicycle sharing programs is in a nascent stage; this paper provides a preliminary understanding of bicycle sharing trip activity introduced in Kerala, India. Fundamentally, a framework is utilized to incorporate spatial interaction effects and estimate the bicycle sharing trip activity using spatial econometric techniques. MYBYK is the associated bicycle rental company, with 67 stations in the various major locations at Cochin, Kalamasery, Choornikkarra, and Aluva cities in India. Information such as socio-demographic, bicycle sharing trip activity, weather, and built environment information have been collected in the study regions. Initially, spatiotemporal variation is analyzed to understand the bicycle-sharing trip activity. Then, spatial indicators are determined using Moran's I technique, and the study area's locational information is transmitted to the spatial weight matrix to illustrate the spatial neighbourhood structure. Finally, Trip Production and Trip Attraction models were developed for estimating the bicycle sharing trip activity using five spatial econometric techniques as Spatial Autoregressive (SAR) model, Spatially Lagged X model (SLX), Spatial Error Model (SEM), Spatial Durbin Error model (SDEM), and Spatial Durbin Model (SDM). The models are evaluated, and an appropriate model is found using the Bayesian approach. The findings indicate that SDM effectively estimates the bicycle sharing trip activity, and its spillover effects are also addressed for each spatial indicator variable.

Impact of the Acuros XB spatial discretization error on ArcCHECK VMAT QA for small-field SBRT.

To evaluate the impact of the Acuros XB spatial discretization errors on ArcCHECK volumetric modulated arc therapy (VMAT) QA for small-field SBRT plans. Eighteen SBRT VMAT arcs that failed the ArcCHECK VMAT QA were retrospectively analyzed. Plan verification doses were calculated using Eclipse Acuros XB, and absolute 3%/2mm gamma passing rates were calculated to compare ArcCHECK and MapCHECK2 with MapPHAN. Verification doses were recalculated using AAA in Eclipse and with the EGSnrc Monte Carlo package. In addition, error-reduced Acuros XB doses were calculated by subdividing the entire arc into several sub-arcs ("split-arc" method), with the angular ranges of the sub-arcs optimized to balance accuracy and efficiency. Relative gamma passing rates were calculated and compared for the four methods: (1) Acuros XB; (2) AAA; (3) EGSnrc Monte Carlo; and (4) the split-arc method. The absolute gamma passing rates were below 90% for ArcCHECK and above 95% for MapCHECK2. The averaged relative gamma passing rates were (1) 84.7% for clinical Acuros XB; (2) 96.8% for AAA; (3) 98.8% for EGSnrc Monte Carlo; and (4) 96.8% for the split-arc method with 60° sub-arc angle. Compared to the clinical Acuros XB, the split-arc method improved the relative gamma passing rate by 12.1% on average. No significant difference was found between AAA and the split-arc method (p>0.05). The Acuros XB spatial discretization errors can significantly impact the ArcCHECK VMAT QA results for small-field SBRT plans. The split-arc method may be used to improve the VMAT QA results.

Blockage of calcium-sensing receptor improves chronic intermittent hypoxia-induced cognitive impairment by PERK-ATF4-CHOP pathway

Obstructive sleep apnea-hypopnea syndrome (OSAHS) is involved in cognitive impairment of children. Chronic intermittent hypoxia (CIH) is considered as the critical pathophysiological mechanism of OSAHS. Calcium sensitive receptor (CaSR) mediated apoptosis in many neurological disease models by endoplasmic reticulum stress (ERS)-related pathway. However, little is known about the role of CaSR in OSAHS-induced cognitive dysfunction. In this study, we explored the effect of CaSR on CIH-induced cognitive impairment and possible mechanisms on regulation of PERK-ATF4-CHOP pathway in vivo and in vitro. CIH exposed for 9 h in PC12 cells and resulted in the cell apoptosis, simulating OSAHS-induced neuronal injury. CIH upregulated the level of CaSR, p-PERK, ATF4 and CHOP, contributing to the cell apoptosis. Treated with CaSR inhibitor (NPS-2143) or p-PERK inhibitor (GSK2656157) before CIH exposure, CIH-induced PC12 cell apoptosis was alleviated via inhibition of CaSR by downregulating p-PERK, ATF4 and CHOP. In addition, we established CIH mice model. With CIH exposure for 4 weeks in mice, more spatial memory errors were observed during 8-arm radial maze test. CIH significantly increased apoptotic cells in hippocampus via upregulating cleaved Caspase-3 and downregulating ratio of Bcl-2 to Bax. Besides, treatment of CaSR inhibitor alleviated the hippocampal neuronal apoptosis following CIH with downregulated p-PERK, ATF4 and CHOP, suggesting that CaSR contributed to CIH-induced neuronal apoptosis in hippocampus via ERS pathway. Sum up, our results demonstrated that CaSR accelerated hippocampal apoptosis via PERK-ATF4-CHOP pathway, holding a critical function on CIH-mediated cognitive impairment. Conversely, inhibition of CaSR suppressed PERK-ATF4-CHOP pathway and alleviated cognitive impairment.

Spatial Dependence of Poverty Rate and Socio-Economic Indicators in Nigeria: An Empirical Analysis

This study investigates the spatial dependence between the poverty rate and various socio-economic indicators in Nigeria. The analysis is based on a dataset comprising unique geographic identifiers and the poverty rate along with other relevant variables. Descriptive statistics reveal that the poverty rate exhibits moderate variability with an average of 4.1240. The correlation analysis shows significant relationships between the poverty rate and household size as well as income level, indicating that larger households and higher incomes are associated with higher and lower poverty rates, respectively. Spatial regression models, including Spatial Autoregressive (SAR), Spatial Error (SEM), Spatial Durbin (SDM), and Spatial Autoregressive Conditional (SAC) models, are employed to explore the spatial dependence. Results indicate the presence of spatial clustering and positive autocorrelation in the poverty rate, as indicated by the Moran's I index with a value of 0.3579 (p-value = 0.0012). However, tests for spatial heteroscedasticity do not reveal significant departures from the assumption of constant error variance. The findings suggest that spatial factors play a crucial role in explaining the poverty rate in Nigeria. The positive spatial autocorrelation indicates the presence of localized poverty clusters, emphasizing the importance of considering spatial effects in policy formulation and targeted interventions. The significant relationships between the poverty rate and household size and income level underscore the need for comprehensive strategies to address these socio-economic indicators for poverty reduction.

How Does the Built Environment in Urban Villages Affecting Ride-Sourcing Commuting Trips Interacting with the Spatial Dependence Effect

Urban renewal provides opportunities to improve urban transport structures during the process of improving built environments. It is necessary to clarify the impact of different elements of the built environment on travel behaviors in the context of urban village renewal. This paper presents a microscopic perspective of individual travel behavior by proposing analytical frameworks to investigate travel behavior in terms of DiDi commuting trips. Considering the effect of spatial dependence, a Spatial Durbin Error Model was established, incorporating a spatial lag and spatial error. Traveling information was employed from the ride-sourcing company DiDi during the morning and evening peaks within in the urban village areas and workplaces of Shenzhen, and the variables of a built environment were scaled within travel analysis zones (TAZs). The results show that the impacts of the built environment on ride-sourced commuting were different between job and housing locations, with more influential factors in residential locations (urban villages). On the other hand, working locations had an influential magnitude more than twice that of residential locations. Alongside that, due to the spillover effect, it was more effective to hinder ride-sourced commuting and promote green traveling modes by increasing the number of bus stops. The findings provide some insights into transit-oriented urban renewal. Therefore, when transforming urban villages, an emphasis should be placed on the enhancement of transit availability, and the mixed land use could be considered last due to limited time and funds.

An adaptive space and time method in partially explicit splitting scheme for multiscale flow problems

We propose a space and time adaptive framework for a partially explicit splitting scheme of flow problems in multiscale media. We consider the parabolic equation with high-contrast multiscale coefficients. It is known that sufficient number of multiscale basis are needed to capture non-local features and information across all scales. Moreover, the time step size in an explicit discretization of the problem is affected by the magnitude of the coefficient. To improve computational efficiency, in this work, we introduce an adaptive algorithm to refine temporal size and enrich multiscale spaces. We first utilize a stable temporal splitting scheme with an appropriate construction of multiscale spaces to ensure that the time step size is independent of the contrast. The multiscale subspaces are then constructed to handle the fast-flow and slow-flow parts separately, and implicit and explicit discretization is adopted for the degrees of freedom with respect to the two multiscale subspaces, respectively. A multirate time stepping is introduced for the two parts considering that the flow rates vary significantly in different regions due to the high-contrast features of the multiscale coefficients. We derive both temporal and spatial error estimators to identify local regions for two components of the solutions. Start with coarser time step sizes and a few important multiscale basis functions in each subspace, we can adaptively refine time stepping and add additional basis with constraint energy minimizing properties based on the derived error indicators. The stability and convergence of the method are studied, and several numerical tests are presented to demonstrate the performance of the proposed method.

Sub Miniature Kiel Probe for Total Pressure Measurements in Three Dimensional Boundary Layers

The design, fabrication and calibration details of a sub miniature Kiel probe for three-dimensional boundary layer measurements are presented in this paper. The probe has a nominal measurement dimension of 0.3 mm and a shroud size of 0.7 mm in the boundary layer direction thus minimizing spatial and flow gradient errors. The probe is calibrated in a calibration tunnel at a velocity of 50 m/s in the yaw and pitch angle ranges of ± 45° and ± 25° at 5° interval respectively. The non-dimensional pressure measured by the probe is plotted as contours. The non-dimensional pressure has a value within ± 1% of total pressure in the yaw and pitch angle ranges of ± 40° and ± 15° respectively. The probe is used to measure total pressure at the exit of a centrifugal impeller at four volume flows. The total pressure measured is found to follow expected trends.

Two-Phase Flow Measuring with Ultrasonic Tomography

The exact measurement of multiphase flow is an important and essential task in the oil and petrochemical related industries. Several methods have already been proposed in this field. In the existing methods, flow rate measurement depends on the fluid flow pattern. Flow pattern recognition requiring calibration has created instability in such systems. In this paper, a imple and reliable method is proposed which is based on ultrasonic tomography. It is free from calibration and instability problems that existing methods have. The obtained data from a 32-digit array of ultrasonic sensors have been used and the two-phase flow rate including liquid and gas phases have been calculated through a simple algebraic algorithm. Simulation results show that while applying this method the measurement technique is independent from the fluid flow pattern and the system error is decreased. For the proposed algorithm, the average amount of the spatial imaging error (SIE) for a bubble at different positions inside the pipe is about 5%.

Analysis of Ground Subsidence Vulnerability in Urban Areas Using Spatial Regression Analysis

The main cause of ground subsidence accidents in urban areas is cavities formed by damage to underground utilities. For this reason, the attribute information of underground utilities should be used to prepare against ground subsidence accidents. In this study, attribute information (pipe age, diameter, burial depth, and density) of six types of underground utilities (water, sewer, gas, power, heating, and communication) and history information of ground subsidence were collected. A correlation analysis was conducted using the collected data, and a prediction model of vulnerability to ground subsidence was developed through the ordinary least squares (OLS) method and spatial regression analysis (spatial lag model (SLM) and spatial error model (SEM)). To do this, the target area was divided into a grid of 100 m × 100 m. Datasets were constructed using the attribute information of underground utilities included in the divided grid and the number of ground subsidence occurrences. To analyze the OLS of the constructed data, the variance inflation factor (VIF) of the attribute information of underground utilities was studied. An OLS analysis was conducted using the appropriate factors, and the results show that the spatial data were autocorrelated. Subsequently, SEM and SLM analyses, which were spatial regression analyses, were conducted. As a result, the model using SLM was selected as suitable for analyzing the vulnerability of ground subsidence, and the density of six types of underground utilities was found to be the highest influencing factor. In addition, a vulnerability map of ground subsidence in the target area was prepared using the model. The vulnerability map demonstrates that regions with frequent ground subsidence can be predicted to be highly vulnerable.