Spatial Interpolation Research Articles

Optimization of user-side electrolytic hydrogen production system considering electrolyzer efficiency degradation

This paper proposes an optimized operational strategy for user-side proton exchange membrane (PEM) electrolyzer hydrogen production systems, considering the degradation of hydrogen production efficiency over time. The work is divided into three parts. First, a combined hydrogen production efficiency model based on operational data is established, using polynomial surface fitting (PSF) and gaussian process regression (GPR) to model the electrolyzer's efficiency from two perspectives: operational time and input power. Second, an optimization model for the operational strategy of the user-side electrolytic hydrogen production system is constructed, aiming to enhance overall system efficiency by adjusting the electrolyzer's input power. Finally, the hydrogen efficiency model from the first part is applied to the optimization strategy in the second part for empirical analysis. The results show that the optimized strategy, which accounts for the degradation of hydrogen production efficiency, significantly improves system performance, validating the method's effectiveness and practicality. This study provides theoretical support and practical guidance for the efficient operation of user-side electrolyzers, contributing to the promotion and application of green hydrogen technologies.

Application of Response Surface Methodology for concentration of fluorosilicic acid by extraction technique with benzyl alcohol as extractant

As a kind of alternative of natural fluorinated minerals and raw material for producing anhydrous hydrogen fluoride, fluorosilicic acid has attracted more and more attention. In order to raise its utilization rate, dilute fluorosilicic acid solution coming from chemical enterprises needs to be concentrated. The extraction technology with benzyl alcohol as extraction agent was used to concentrating fluorosilicic acid aqueous solution. In order to determine the optimizing operation conditions of the extracting process, single - factor experiments were carried out to confirm the significant affecting factors and their operating range. Then the Response Surface Methodology (RSM) was conducted to accomplish regression of a nonlinear quadratic polynomial model to the experimental data and statistical analysis. Based on these studies, the optimal technical parameters of extracting process are acquired by optimization of the model using the Design-Expert 13 software. The statistical analysis for the fitting response surface quadratic equation model indicated that the model was reliable and accurate with very low p-values (<0.0001), the predicted values with the model equation had remarkable consistency in experimental data. By a great number of optimizations carried out with the Design Expert 13 software based on the fitting model equation, the optimal extractive processing conditions for concentrating fluorosilicic acid aqueous solution were obtained: the phase ratio 7, the extraction time 120.11 min and the extraction temperature 39.70 °C. The optimal concentration of fluorosilicic acid is 26.68 %. In order to increase the concentration of fluorosilicic acid, three-stage cross-current extraction for dilute fluorosilicic acid solution was carried out at the optimizing extracting conditions, 17.43 wt% fluorosilicic acid solution can be concentrated up to 37.46 wt%. The extractant-benzyl alcohol can be recovered with vacuum distillation and recovery rate reached up to 98 %.

Examining intermediate soil properties variability through spatial interpolation methods in GIS

This study compares Kriging and Inverse Distance Weighting (IDW) spatial interpolation methods for estimating intermediate soil properties at a construction site in Astana, Kazakhstan. Using data from eight boreholes, seven engineering geological elements (EGE) were identified and analyzed at 6.5 m and 11.5 m depths. Kriging produced deformation modulus values ranging from -0.29 to 18.99 MPa at 6.5 m and -0.51 to 23.94 MPa at 11.5 m, capturing more spatial variability compared to IDW, which provided ranges of 3.3 to 18.99 MPa and 2.6 to 23.99 MPa, respectively. Kriging’s ability to account for spatial correlations resulted in more accurate predictions, particularly in areas with complex subsurface variability. Meanwhile, IDW offered reliable localized results, effective in more uniform geological conditions. The findings demonstrate that both methods are valuable for geotechnical applications, with the choice depending on data density and site variability.

Mapping Population Densities and Waste Management Systems in Mombasa County: A Literature Review of GIS and Remote Sensing Techniques

Mombasa County is one of the five counties along the coastline of Kenya. Currently, Mombasa County has a human population of 1.2 million people, and the population is rapidly increasing through rural-urban immigration and natural births. However, the inadequacy of well-established waste management systems on the mainland and coastlines of the County is a threat to the well-being of the local residents. Poorly disposed of wastes both on land and at sea can lead to health problems not only for humans and terrestrial animals but also for aquatic animals such as fish, with the latter being one of the main sources of affordable proteins for poor coastal communities. This paper aimed to conduct a review of the spatial human population density of Mombasa County and the geospatial location of waste dumping sites and their proximity to settled areas. This literature review synthesized existing research on the application of Geographic Information Systems (GIS) and Remote Sensing techniques in mapping population densities and assessing waste management systems. The review began by examining studies that investigate population distribution patterns in urban and rural areas, utilizing GIS to analyze demographic data and satellite imagery. It explored methodologies used to estimate population densities, including dasymetric mapping, spatial interpolation, and land use classification techniques. Key findings were that limited studies have utilized GIS technologies to assess the population in Kenya. GRASP and Random Forest (RF) were the main techniques previously used to assess population densities in the rural Taita Hills area and along the coastal region. The population has exponentially increased in Mombasa County since the 1950s and is projected to increase further. Additionally, waste management in Mombasa County is majorly controlled by the county government. Ten geo-tagged waste collection spots were identified during the review, spread across the residential areas. In conclusion, the county should endeavor to employ GIS techniques to assess the rapid population change within the county and have targeted interventions to address the disparities in waste collection systems against population increase.

Empirical Bayesian Kriging, a Robust Method for Spatial Data Interpolation of a Large Groundwater Quality Dataset from the Western Netherlands

No single spatial interpolation method reigns supreme for modelling the precise spatial distribution of groundwater quality data. This study addresses this challenge by evaluating and comparing several commonly used geostatistical methods: Local Polynomial Interpolation (LPI), Ordinary Kriging (OK), Simple Kriging (SK), Universal Kriging (UK), and Empirical Bayesian Kriging (EBK). We applied these methods to a vast dataset of 3033 groundwater records encompassing a substantial area (11,100 km2) in the coastal lowlands of the western Netherlands. To our knowledge, no prior research has investigated these interpolation methods in this specific hydrogeological setting, exhibiting a range of groundwater qualities, from fresh to saline, often anoxic, with high natural concentrations of PO4 and NH4. The prediction performance of the interpolation methods was assessed through statistical indicators such as root means square error. The findings indicated that EBK outperforms the other geostatistical methods in forecasting groundwater quality for the five variables considered: Cl, SO4, Fe, PO4, and NH4. In contrast, SK performed worst for the species except for SO4. We recommend not using SK to interpolate groundwater quality species unless the data exhibit low spatial variation, high sample density, or evenly distributed sampling.

Evaluation of gold mineralisation potential using AHP systems and weighted overlay analysis

The demand for sustainable development goals and the absence of systematic development and organised exploration for gold has prompted this study to integrate magnetic and radiometric datasets with lithology to evaluate the gold mineralisation potential in the Ilesha schist belt. This study considers 3168.72 km2 of the Ilesha schist belt in southwestern Nigeria, a frontier belt for gold deposits. The high-resolution airborne magnetic and radiometric datasets were processed using enhancement techniques, including the analytical signal, lineament density, and K/Th ratio. CET grid analysis, Euler deconvolution, and analytical signal depth estimation methods were used to aid the interpretation. The spatial integration and interpolation were performed using the Analytical Hierarchy Process (AHP) and weighted overlay analytical tools within the ArcGIS environment. The dominant structural controls for potential mineralisation are ENE–WSW and ESE–WNW trends. The depth of the magnetic sources revealed by the analytical signal ranged from 63.17 to 629.47 m, while depths ranging from 47.32 to 457.22 m were obtained from Euler deconvolution. The delineated highly magnetic edge sources, dense lineaments, radiometrically highlighted alteration zones, and lithological hosts for gold mineralisation were integrated to establish the gold mineralisation potential map. The AHP deductions reveal that 10.52% of the study site is within the high mineralisation potential class, a remarkable 60.39% falls within the moderate class, a significant portion (28.86%) falls within the poor class, and 0.23% is considered unfavourable. The result was optimised by validation using known mines, with 94% (i.e., 15 out of 16 mining sites) plotting within the high mineralisation potential class. This assessment provides invaluable insight for stakeholders and policymakers to embark on gold exploration and exploitation and promote sustainable mineral development.

Exposing illegal hunting and wildlife depletion in the world's largest tropical country through social media data.

Globally, illegal sport hunting can threaten prey populations when unregulated. Due to its covert nature, illegal sport hunting poses challenges for data collection, hindering efforts to understand the full extent of its impacts. We gathered social media data to analyze patterns of illegal sport hunting and wildlife depletion across Brazil. We collected data for 2years (2018-2020) across 5 Facebook groups containing posts depicting pictures of illegal sport hunting events of native fauna. We described and mapped these hunting events by detailing the number of hunters involved, the number of species, the mean body mass of individuals, and the number and biomass of individuals hunted per unit area, stratified by Brazilian biome. We also examined the effects of defaunation on hunting yield and composition via regression models, rank-abundance curves, and spatial interpolation. We detected 2046 illegal sport hunting posts portraying the hunting of 4658 animals (∼29t of undressed meat) across all 27 states and 6 natural biomes of Brazil. Of 157 native species targeted by hunters, 19 are currently threatened with extinction. We estimated that 1414 hunters extracted 3251kg/millionkm2. Some areas exhibited more pronounced wildlife depletion, in particular the Atlantic Forest and Caatinga biomes. In these areas, there was a shift from large mammals and reptiles to small birds as the main targeted taxa, and biomass extracted per hunting event and mean body mass across all taxonomic groups were lower than in other areas. Our results highlight that illegal sport hunting adds to the pressures of subsistence hunting and the wild meat trade on Brazil's wildlife populations. Enhanced surveillance efforts are needed to reduce illegal sport hunting levels and to develop well-managed sustainable sport hunting programs. These can support wildlife conservation and offer incentives for local communities to oversee designated sport hunting areas.

Spatial Analysis of Air Pollutants in an Industrial City Using GIS-Based Techniques: A Case Study of Pavlodar, Kazakhstan

The given research employs high-resolution air quality monitoring and contemporary statistical methods to address gaps in understanding the urban air pollution in Pavlodar, a city with a significant industrial presence and promising touristic potential. Using mobile air quality sensors for detailed spatial data collection, the research aims to quantify concentrations of particulate matter (PM2.5, PM10), carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ground-level ozone (O3); assess their distribution; and identify key influencing factors. In this study, we employed Geographic Information Systems (GISs) for spatial analysis, integrating multi-level B-spline interpolation to model spatial variability. Correlation analysis and structural equation modeling were utilized to explore the relationships between variables, while regression analysis was conducted to quantify these relationships. These techniques were crucial for accurately mapping and interpreting spatial patterns and their underlying factors. The study identifies PM2.5 and NO2 as the primary contributors to air pollution in Pavlodar, with NO2 exceeding the 24 h threshold in 87.38% of locations and PM2.5 showing the highest individual air quality index (AQI) in 75.7% of cases. Correlation analysis reveals a positive association between PM2.5 and AQI and a negative correlation between NO2 and AQI, likely due to the dominant influence of PM2.5 in AQI calculations. Structural equation modeling (SEM) further underscores PM2.5 as the most significant impactor on AQI, while NO2 shows no significant direct impact. Humidity is positively correlated with AQI, though this relationship is context-specific to seasonal patterns observed in May. The sectoral analysis of landscape indices reveals weak correlations between the green space ratio (GSR) and air quality, indicating that while vegetation reduces pollutants, its impact is minimal due to urban planting density. The road ratio (RR) lacks sufficient statistical evidence to draw conclusions about its effect on air quality, possibly due to the methodology used. Spatial variability in pollutant concentrations is evident, with increasing PM2.5, PM10, and AQI towards the east-northeast, likely influenced by industrial activities and prevailing wind patterns. In contrast, NO2 pollution does not show a clear geographic pattern, indicating vehicular emissions as its primary source. Spatial interpolation highlights pollution hotspots near industrial zones, posing health risks to vulnerable populations. While the city’s overall AQI is considered “moderate”, the study highlights the necessity of implementing measures to improve air quality in Pavlodar. This will not only enhance the city’s attractiveness to tourists but also support its sustainable development as an industrial center.

Refining soil nutrient assessment: Incorporating land use boundaries for precision agriculture.

Soil nutrient levels play a crucial role in determining crop yield. A comprehensive understanding of the spatial distribution patterns and evaluation grades of soil nutrients is of significant practical importance for informed fertilization practices, enhancing crop production, and optimizing agricultural land utilization. This study focuses on the urban area of Kashi Prefecture in Xinjiang as a case study. Utilizing soil sample data, GIS spatial interpolation analysis was conducted, incorporating plot boundary information to propose a comprehensive evaluation method for assessing soil nutrient levels at the plot level. Experimental findings revealed the following: (1) The average values of soil organic matter (SOM), total nitrogen (AN), total potassium (AK), and total phosphorus (AP) in the study area were determined to be 13.3 g/kg, 0.74 g/kg, 0.33 g/kg, and 0.03 g/kg, respectively. Among these, AN and SOM were classified as the fourth grade, indicating relatively deficient levels, while AK and AP were classified as the first and second grade, indicating relatively abundant levels. (2) The comprehensive evaluation of soil nutrient grades in the study area primarily fell within the third, fourth, and second grades, representing areas of 29.08 km2, 25 km2, and 4.05 km2, accounting for 50.03%, 43%, and 6.97% of the total area, respectively. (3) The evaluation results of soil nutrient levels at the plot level emphasized the boundary characteristics and provided a more refined assessment grade. This evaluation method is better suited to meet the practical production requirements of farmers and is considered feasible. The outcomes of this study can serve as a reference for precision agriculture management.

Clay resource estimation using a geostatistical approach in Tam Binh district, Vinh Long province, Viet Nam

ABSTRACT Clay is an essential resource for improving the brick and ceramic industries. However, the demand for clay needs to grow. Estimating industrial mineral reserves requires an optimal and unbiased statistical tool. Geographical information systems and geostatistics are two promising tools that could be used to predict clay resources. The study objective was to apply these two tools to compare the performance of the methods for spatial clay resource prediction, considering clay thickness and spatial distribution. The demonstration site is in Tam Binh district, Vinh Long Province, Viet Nam. The results show that the Gaussian variation model at a 600 × 400 m distance is good for sampling and extrapolating clay thickness. This is based on 240 geolocated samples with different densities. Projected clay reserves match measurements, and the spatial statistical method is more accessible and valuable than the traditional method. The interpolated method’s total selected soil borehole number was optimized for clay resource estimation, and geostatistics can help manage and plan clay resources and save money. Future research should examine various spatial interpolation techniques and approaches in management, land use, and topography to improve the accuracy of calculations.

Prenatal exposure to ambient air pollution and persistent postpartum depression

BackgroundAmbient air pollution during pregnancy has been linked with postpartum depression up to 12 months, but few studies have investigated its impact on persistent depression beyond 12 months postpartum. This study aimed to evaluate prenatal ambient air pollution exposure and the risk of persistent depression over 3 years after childbirth and to identify windows of susceptibility. MethodsThis study included 361 predominantly low-income Hispanic/Latina participants with full-term pregnancies in the Maternal and Developmental Risks from Environmental and Social Stressors (MADRES) cohort. We estimated daily residential PM2.5, PM10, NO2, and O3 concentrations throughout 37 gestational weeks using inverse-distance squared spatial interpolation from monitoring data and calculated weekly averaged levels. Depression was assessed by the 20-item Center for Epidemiologic Studies-Depression (CES-D) scale at 12, 24, and 36 months postpartum, with persistent postpartum depression defined as a CES-D score ≥16 at any of these timepoints. We performed robust Poisson log-linear distributed lag models (DLM) via generalized estimating equations (GEE) to estimate the adjusted risk ratio (RR). ResultsDepression was observed in 17.8 %, 17.5 %, and 13.4 % of participants at 12, 24, and 36 months, respectively. We found one IQR increase (3.9 ppb) in prenatal exposure to NO2 during the identified sensitive window of gestational weeks 13–29 was associated with a cumulative risk ratio of 3.86 (95 % CI: 3.24, 4.59) for persistent depression 1–3 years postpartum. We also found one IQR increase (7.4 μg/m3) in prenatal exposure to PM10 during gestation weeks 12–28 was associated a cumulative risk ratio of 3.88 (95 % CI: 3.04, 4.96) for persistent depression. No clear sensitive windows were identified for PM2.5 or O3. ConclusionsMid-pregnancy PM10 and NO2 exposures were associated with nearly 4-fold increased risks of persistent depression after pregnancy, which has critical implications for prevention of perinatal mental health outcomes.

Research on the Cell Wall Breaking and Subcritical Extraction of Astaxanthin from Phaffia rhodozyma.

This study focused on developing an effective cell wall-breaking method for Phaffia rhodozyma, followed by utilizing subcritical fluid extraction to isolate, extract, and concentrate astaxanthin from the complex fermentation products of P. rhodozyma. A comprehensive comparison of seven distinct methods for disrupting cell walls, including dimethyl sulfoxide treatment, lactic acid treatment, sodium hydroxide treatment, β-glucanase enzymatic digestion, β-mannanase enzymatic digestion, and a combined enzymatic treatment involving both β-mannanase and β-glucanase was conducted. The results identified the lactic acid method as the most effective in disrupting the cell walls of P. rhodozyma. The software, Design Expert, was used in the process of extracting astaxanthin from cell lysates using a subcritical extraction method. Through fitting analysis and response surface optimization analysis by Design Expert, the optimal extraction conditions were determined as follows: an extraction temperature of 41 °C, extraction frequency of two times, and extraction time of 46 min. These parameters facilitated the efficient extraction, concentration, and enrichment of astaxanthin from P. rhodozyma, resulting in an astaxanthin concentration of 540.00 mg/L. This result can establish the foundation for its high-value applications.

Boosting house price estimations with Multi-Head Gated Attention

Evaluating house prices is crucial for various stakeholders, including homeowners, investors, and policymakers. However, traditional spatial interpolation methods have limitations in capturing the complex spatial relationships that affect property values. To address these challenges, we have developed a new method called Multi-Head Gated Attention for spatial interpolation. Our approach builds upon attention-based interpolation models and incorporates multiple attention heads and gating mechanisms to better capture spatial dependencies and contextual information. Importantly, our model produces embeddings that reduce the dimensionality of the data, enabling simpler models like linear regression to outperform complex ensembling models. We conducted extensive experiments to compare our model with baseline methods and the original attention based interpolation model. The results show a significant improvement in the accuracy of house price predictions, validating the effectiveness of our approach. This research advances the field of spatial interpolation and provides a robust tool for more precise house price evaluation. Our GitHub repository.11Final_file/tree/main/ASI-main. contains the data and code for all datasets, which are available for researchers and practitioners interested in replicating or building upon our work.

Stochastic Dynamic Buckling Analysis of Cylindrical Shell Structures Based on Isogeometric Analysis

In this paper, we extend our previous work on the dynamic buckling analysis of isogeometric shell structures to the stochastic situation where an isogeometric deterministic dynamic buckling analysis method is combined with spectral-based stochastic modeling of geometric imperfections. To be specific, a modified Generalized-α time integration scheme combined with a nonlinear isogeometric Kirchhoff–Love shell element is used to simulate the buckling and post-buckling problems of cylindrical shell structures. Additionally, geometric imperfections are constructed based on NURBS surface fitting, which can be naturally incorporated into the isogeometric analysis framework due to its seamless CAD/CAE integration feature. For stochastic analysis, the method of separation is adopted to model the stochastic geometric imperfections of cylindrical shells based on a set of measurements. We tested the accuracy and convergence properties of the proposed method with a cylindrical shell example, where measured geometric imperfections were incorporated. The ABAQUS reference solutions are also presented to demonstrate the superiority of the inherited smooth and high-order continuous properties of the isogeometric approach. For stochastic dynamic buckling analysis, we evaluated the buckling load variability and reliability functions of the cylindrical shell with 500 samples generated based on seven nominally identical shells reported in the geometric imperfection data bank. It is noted that the buckling load variability in the cylindrical shell obtained with static nonlinear analysis is also presented to show the differences between dynamic and static buckling analysis.

A Global Nonlinear Model for Photovoltaic Modules Based on 3-D Surface Fitting

A Global Nonlinear Model for Photovoltaic Modules Based on 3-D Surface Fitting

Masked pretraining strategy for neural potentials

We propose a masked pretraining method for Graph Neural Networks (GNNs) to improve their performance on fitting potential energy surfaces, particularly in water and small organic molecule systems. GNNs are pretrained by recovering the spatial information of masked-out atoms from molecules selected with certain ratios and then transferred and fine-tuned on atomic force fields. Through such pretraining, GNNs learn meaningful prior about the structural and underlying physical information of molecule systems that are useful for downstream tasks. With comprehensive experiments and ablation studies, we show that the proposed method improves both the accuracy and convergence speed of GNNs compared to their counterparts trained from scratch or with other pretraining techniques. This approach showcases its potential to enhance the performance and data efficiency of GNNs in fitting molecular force fields.

Effects of coal mining disturbance on spatial and temporal distribution of soil water content in Northwest China‐based on 3D EBK model

AbstractNorthwest China is both an important coal storage base and an ecologically fragile area, and soil water content (SWC) is a key factor limiting the ecological development of Northwest China. Revealing the characteristics of spatial and temporal distribution changes of soil water content in mining areas under coal mining disturbance and the influencing mechanism is crucial for the protection of water resources in mining areas. In this study, the soil water content (depth 0–1000 cm) of a typical coal mine subsidence area in the western part of Shendong Coal Group was monitored in situ for 1 year after mining, and the absolute value, variability, and spatial distribution of soil water content were temporally analysed by combining classical statistics, 2D Ordinary Kriging and 3D Empirical Bayesian kriging spatial interpolation. sequence analysis. The results showed that the shallow SWC (0 ~ 60 cm) was distributed horizontally in bands, and gradually increased along the direction from northwest to southeast; with the increase of coal mining time, the absolute value of SWC decreased by 0.81% ~ 33.58%, and the coefficient of variation decreased and then increased, with the range of variation from 2.19% ~ 34.49%. The deep SWC (100 ~ 1000 cm) was stratified vertically and increased with soil depth, and the shallow and deeper soil moisture would gradually migrate to the middle layer under the influence of coal mining. In addition, this paper accurately portrays the three‐dimensional spatial and temporal distribution of soil water content by the 3D EBK model, which further reveals the mechanism of coal mining's influence on soil water content. This study can provide technical and data support for predicting and evaluating the potential impacts of mining activities on the water cycle, and help mining areas to formulate policies for managing and protecting water resources.

3D Mapping In Construction Projects for Soil Characterizations And EIA Enhancement

With the advancement on automation systems in our life and construction projects the development of automated Environmental Impact assessment EIA system is desirable. This could be achieved by set of integrated technologies, software, and hardware to perform a task which is in this research paper is Quality EIA. The environmental impact assessment must be improved, assessed, and quality checked for the human and the environment health and safety benefit first, and the advancement of human activities and needs towards the future. If we consider the development and construction projects is one of the markers and the signature of how far is countries and human civilized or advanced, the EIA quality review and development of this document in any project should be improved and advanced. Ideally EIA system, document, will lead to quickly and easily obtain information, improve project value, and return of investment, improving facilities and reach the optimum clients and government requirements. This often necessitates to build a tailored applications sometime combine or develop innovative, and integrate, new, existing hardware, software, methods, and technologies to reach that aim. Which is the research methodology of this research paper This research paper concluded that 3D mapping for soil characterizations a technology where spatial interpolation methods play a significant part as a backbone of its core in relation to the accuracy of the data which will provides essential elements towards site characterization information, that need to be informed in to the EIA in structure and elegant way to improve decision making, planning strategies, environmental management, risk assessment, more than those projects estimations. This research paper adopts a multi-method approach involving reviewing the literature, case studies, and computer simulation.

Effect of Acidulated Phosphate Fluoride Gel on Zirconia Intaglio Surface: An In-Vitro Study

Abstract Aim: To evaluate the micro-shear bond strength (µ-SBS) of resin-modified glass ionomer cement and to assess the chemical and topographical changes in the zirconia fitting surface induced by acidulated phosphate fluoride (APF) gel using scanning electron microscope (SEM) analysis and Fourier transform infrared (FTIR) spectroscopy. Materials and Methods: Thirty-two samples were prepared from two zirconia materials, UPCERA HT White and BruxZir® Solid Zirconia, milled by a computer-aided design/computer-aided manufacturing system. From each zirconia sample, six plates were prepared for FTIR and SEM testing. Following sintering, the samples were divided into control and test groups for each material. The APF gel (1.23%) was applied to the intaglio surface of each test group. To measure the µ-SBS between the zirconia materials and luting cement, 20 rectangular samples of zirconia material were prepared. Ten samples were obtained from Upcera and ten from Bruxzir, with five assigned to the control and five to APF groups. Statistical Analysis: For the µ-SBS test, independent samples t test was conducted to determine the level of significance between the tested groups. Results: FTIR spectroscopy revealed new bands for Upcera and Bruxzir zirconia owing to ion exchange between the formed sodium phosphate and the zirconia surface and the formation of zirconium phosphate by an ester reaction. SEM assessment identified lines, scratches, or surface dissociation that appeared on the intaglio–zirconia surface after conditioning. The µ-SBS test, as indicated by the independent samples t test, showed a significant increase in bond strength of 1.266 and 1.566 MPa for Upcera and Bruxzir zirconia, respectively. Conclusion: This study offers new practical, cost-effective, and accurate tests to enhance the µ-SBS of luting cement to yttria-stabilized tetragonal zirconia polycrystal ceramics.

Damage assessment of fixed U-shaped stiffeners stiffened square plates subjected to close-in explosions

The aim of this study is to suggest an approach for evaluating the damage modes and damage levels of the U-shaped stiffeners stiffened plates exposed to close-in explosions. To achieve this objective, a numerical model was developed in LS-DYNA, and the numerical model was verified by experimental results of the authors. Using this validated model, various U-shaped stiffeners stiffened plates were examined under different blast loads. The study investigated the factors that influence the damage modes of the plates, including the strength and distribution of the blast loads, as well as the geometry of the stiffened plates. The results indicated that the small size stiffened plates exhibited overall damage mode while the large stiffened plate showed localized damage at the same blast load. The blast load distribution on the different size plate led to different damage modes. Additionally, this study found that the slenderness of the stiffened plate had a great influence on the support rotation angle of the tensile tearing threshold. The larger span to thickness ratio can result in larger tensile tearing threshold. Moreover, 400 numerical cases were included and used to classify the damage modes of stiffened steel plates. Based on the damage mechanism of the stiffened steel plate, the damage number (Φ1 and Φ2) which considering the blast loads and material parameters of plate, the maximum incident angle of the blast load(α), and the relative stiffness of the stiffeners and the flat steel plate (κ1 and κ2), and the thickness to span ratio of the stiffened plate (λ) were combined to identify the damage modes of stiffened plates. Furthermore, a flow chart was constructed for determining damage modes based on these considerations. Additionally, a curve surface fitting was performed using Φ1 and λ, the fitting formulas were then used to estimate the deformation characteristics of the stiffened plates with U-shaped stiffeners. The damage levels of the U-shaped stiffeners stiffened plates were then classified as low damage, medium damage, severe damage, and collapse by combining the nominal support rotation angle with the threshold of the global tensile tearing at boundaries. The study result can provide insight into the damage mode identification and damage level classification of stiffened steel plates under explosion. Additionally, this study was a base of damage evaluation of the structure elements (such as steel box girder) include stiffened steel plates under explosions.