Test Data Research Articles

The symptomatology of Attention-Deficit/Hyperactivity Disorder and the genetic control of vitamin D levels.

Vitamin D deficiency has been associated with psychiatric disorders and behavioral phenotypes such as Attention-Deficit/Hyperactivity Disorder (ADHD). Considering that vitamin D levels are polygenic, we aim to evaluate the overall effects of its genetic architecture on symptoms of inattention, hyperactivity, and impulsivity and on the serum levels of vitamin D in two independent samples of adults, as well as the specific effects of five relevant polymorphisms in vitamin D-related genes. We evaluated 870 subjects from an ADHD sample (407 cases and 463 controls) and 319 subjects from an academic community (nutrigenetic sample). Vitamin D serum levels were obtained through Elisa test and genetic data by TaqMan™ allelic discrimination and Infinium PsychArray-24 BeadChip genotyping. Polygenic Scores (PGS) were calculated on PRSice2 based on the latest GWAS for Vitamin D and statistical analyses were conducted at Plink and SPSS software. Vitamin D PGSs were associated with inattention in the ADHD sample and with hyperactivity when inattention symptoms were included as covariates. In the nutrigenetic sample, CYP2R1 rs10741657 and DHCR7 rs12785878 were nominally associated with impulsivity and hyperactivity, respectively, and both with vitamin D levels. In the clinical sample, RXRG rs2134095 was associated with impulsivity. Our findings suggest a shared genetic architecture between vitamin D levels and ADHD symptoms, as evidenced by the associations observed with PGS and specific genes related to vitamin D levels. Interestingly, differential effects for vitamin D PGS were found in inattention and hyperactivity, which should be considered in further studies involving ADHD.

Intelligent Detection of Surface Defects in High-Speed Railway Ballastless Track Based on Self-Attention and Transfer Learning

The detection of ballastless track surface (BTS) defects is a prerequisite for ensuring the safe operation of high-speed railways. Traditional convolutional neural networks fail to fully exploit contextual information and lack global pixel representations. The extensive stacking of convolutions leads deep learning models to play a black-box detection role, lacking interpretability. Due to the current lack of sufficient high-quality surface data for ballastless tracks, it is a severe constraint on the accurate identification of the substructure state in high-speed railways. This paper proposes an intelligent detection method for BTS defects named TrackNet based on self-attention and transfer learning. The method enhances the fusion ability of global features of BTS defects using multihead self-attention. The model’s dependence on extensive defect data is reduced by transferring knowledge from large-scale publicly available datasets. Experimental results demonstrate that compared to advanced Swin Transformer model results, the TrackNet model achieves improvements in average accuracy and F1-score by 5.15% and 5.16%, respectively, on limited test data. The TrackNet model visualizes the decision regions of the model in identifying BTS defects, revealing the black-box recognition mechanism of deep learning models. This research performs engineering applications and provides valuable insights for the multiclass recognition of BTS defects in high-speed railways.

Enhancing Transparency of Climate Efforts: MITICA’s Integrated Approach to Greenhouse Gas Mitigation

Under the Paris Agreement, countries must articulate their most ambitious mitigation targets in their Nationally Determined Contributions (NDCs) every five years and regularly submit interconnected information on greenhouse gas (GHG) aspects, including national GHG inventories, NDC progress tracking, mitigation policies and measures (PAMs), and GHG projections in various mitigation scenarios. Research highlights significant gaps in the definition of mitigation targets and the reporting on GHG-related elements, such as inconsistencies between national GHG inventories, projections, and mitigation targets, a disconnect between PAMs and mitigation scenarios, as well as varied methodological approaches across sectors. To address these challenges, the Mitigation-Inventory Tool for Integrated Climate Action (MITICA) provides a methodological framework that links national GHG inventories, PAMs and GHG projections, applying a hybrid decomposition approach that integrates machine learning regression techniques with classical forecasting methods for developing GHG emission projections. MITICA enables mitigation scenario generation until 2050, incorporating over 60 PAMs across Intergovernmental Panel on Climate Change (IPCC) sectors. It is the first modelling approach that ensures consistency between reporting elements, aligning NDC progress tracking and target setting with IPCC best practices while linking climate change with sustainable economic development. MITICA’s results include projections that align with observed trends, validated through cross-validation against test data, and employ robust methods for evaluating PAMs, thereby establishing its reliability.

A tightly coupled GNSS RTK/IMU integration with GA-BP neural network for challenging urban navigation

Abstract Intelligent transportation system is increasing the importance of real-time acquisition of positioning, navigation, and timing information from high-accuracy global navigation satellite systems (GNSS) based on carrier phase observations. The complexity of urban environments, however, means that GNSS signals are prone to reflection, diffraction and blockage by tall buildings, causing a degraded positioning accuracy. To address this issue, we have proposed a tightly coupled single-frequency multi-system single-epoch real-time kinematic (RTK) GNSS/inertial measurement unit (IMU) integration algorithm with the assistance of genetic algorithm back propagation based on low-cost IMU equipment for challenging urban navigation. Unlike the existing methods, which only use IMU corrections predicted by machine learning as a direct replacement of filtering corrections during GNSS outages, this algorithm introduces a more accurate and efficient IMU corrections prediction model, and it is underpinned by a dual-check GNSS assessment where the weights of GNSS measurements and neural network predictions are adaptively adjusted based on duration of the integrated system GNSS failure, assisting RTK/IMU integration in GNSS outages or malfunction conditions. Field tests demonstrate that the proposed prediction model results in a 68.69% and 69.03% improvement in the root mean square error in the 2D and 3D component when the training and testing data are collected under 150 s GNSS signal-blocked conditions. This corresponds to 52.43% and 51.27% for GNSS signals discontinuously blocked with 500 s.

IAQ Prediction in Apartments Using Machine Learning Techniques and Sensor Data

This study explores the capability of machine learning techniques (MLTs) in predicting IAQ in apartments. Sensor data from kitchen air monitoring were used to determine the conditions in the living room. The analysis was based on several air parameters—temperature, relative humidity, CO2 concentration, and TVOC—recorded in five apartments. Multiple input–multiple output prediction models were built. Linear (multiple linear regression and multilayer perceptron (MLP)) and nonlinear (decision trees, random forest, k-nearest neighbors, and MLP) methods were investigated. Five-fold cross-validation was applied, where four apartments provided data for model training and the remaining one was the source of the test data. The models were compared using performance metrics (R2, MAPE, and RMSE). The naive approach was used as the benchmark. This study showed that linear MLTs performed best. In this case, the coefficients of determination were highest: R2 = 0.94 (T), R2 = 0.94 (RH), R2 = 0.63 (CO2), R2 = 0.84 (TVOC, based on the SGP30 sensor), and R2 = 0.92 (TVOC, based on the SGP30 sensor). The prediction of distinct indoor air parameters was not equally effective. Based on the lowest percentage error, best predictions were attained for indoor air temperature (MAPE = 1.57%), relative humidity (MAPE = 2.97%RH), and TVOC content (MAPE = 0.41%). Unfortunately, CO2 prediction was loaded with high error (MAPE = 20.83%). The approach was particularly effective in open-kitchen apartments, and they could be the target for its application. This research offers a method that could contribute to attaining effective IAQ control in apartments.

Development of a defined approach for eye hazard identification of solid chemicals according to the three UN GHS categories.

Currently two OECD adopted defined approaches (DA) for eye hazard identification of non-surfactant liquids exist (OECD TG467). The purpose of the current study was to develop a DA for eye hazard identification according to the three UN GHS categories (Cat.1, Cat. 2, No Cat.) for solid chemicals: the DAS. The DAS combines two test methods described in OECD TG437 and TG492. The DAS was developed based on in-depth statistical analysis of a database on solids for which in vitro and historically curated in vivo Draize eye test data exist. The performance of the DAS was assessed by comparing the predictions with the classification based on in vivo Draize eye test data, on the one hand, and with the performance criteria established by the OECD expert group, on the other hand. In a first tier of the DAS, the SkinEthic™ HCE EIT method (TG492) is used to distinguish No Cat. from classified substances. For classified substances the BCOP LLBO method (TG437) is used to identify Cat. 1, the remaining solids are predicted Cat. 2. In summary, 77.4% Cat. 1 (N = 31), 52.3% Cat. 2 (N = 18) and 70.0% of No Cat. (N = 60) solids were correctly identified compared to the classification based on the Draize eye test. The percentage of correct predictions met the minimum OECD established performance values of 75% Cat. 1, 50% Cat. 2, and 70% No Cat. and the percentage of mispredictions was below the established maximum values. Therefore, inclusion of the DAS in OECD TG 467 has been achieved.

Machine Learning-Enabled Image Classification for Automated Electron Microscopy.

Traditionally, materials discovery has been driven more by evidence and intuition than by systematic design. However, the advent of "big data" and an exponential increase in computational power have reshaped the landscape. Today, we use simulations, artificial intelligence (AI), and machine learning (ML) to predict materials characteristics, which dramatically accelerates the discovery of novel materials. For instance, combinatorial megalibraries, where millions of distinct nanoparticles are created on a single chip, have spurred the need for automated characterization tools. This paper presents an ML model specifically developed to perform real-time binary classification of grayscale high-angle annular dark-field images of nanoparticles sourced from these megalibraries. Given the high costs associated with downstream processing errors, a primary requirement for our model was to minimize false positives while maintaining efficacy on unseen images. We elaborate on the computational challenges and our solutions, including managing memory constraints, optimizing training time, and utilizing Neural Architecture Search tools. The final model outperformed our expectations, achieving over 95% precision and a weighted F-score of more than 90% on our test data set. This paper discusses the development, challenges, and successful outcomes of this significant advancement in the application of AI and ML to materials discovery.

Generalizability of polygenic prediction models: how is theR2defined on test data?

Polygenic risk scores (PRS) quantify an individual's genetic predisposition for different traits and are expected to play an increasingly important role in personalized medicine. A crucial challenge in clinical practice is the generalizability and transferability of PRS models to populations with different ancestries. When assessing the generalizability of PRS models for continuous traits, the is a commonly used measure to evaluate prediction accuracy. While the is a well-defined goodness-of-fit measure for statistical linear models, there exist different definitions for its application on test data, which complicates interpretation and comparison of results. Based on large-scale genotype data from the UK Biobank, we compare three definitions of the on test data for evaluating the generalizability of PRS models to different populations. Polygenic models for several phenotypes, including height, BMI and lipoproteinA, are derived based on training data with European ancestry using state-of-the-art regression methods and are evaluated on various test populations with different ancestries. Our analysis shows that the choice of the definition can lead to considerably different results on test data, making the comparison of values from the literature problematic. While the definition as the squared correlation between predicted and observed phenotypes solely addresses the discriminative performance and always yields values between 0 and 1, definitions of the based on the mean squared prediction error(MSPE) with reference to intercept-only models assess both discrimination and calibration. These MSPE-based definitions can yield negative values indicating miscalibrated predictions for out-of-target populations. We argue that the choice of the most appropriate definition depends on the aim of PRS analysis - whether it primarily serves for risk stratification or also for individual phenotype prediction. Moreover, both correlation-based and MSPE-based definitions of can provide valuable complementary information. Awareness of the different definitions of the on test data is necessary to facilitate the reporting and interpretation of results on PRS generalizability. It is recommended to explicitly state which definition was used when reporting values on test data. Further research is warranted to develop and evaluate well-calibrated polygenic models for diverse populations.

TAKOTSUBO SYNDROME PRECEDING THE INDEX EVENT: A DANGEROUS CLINICAL SETTING

Abstract We describe the case of a 68–year–old patient who presented to the Emergency Department following an unwitnessed syncopal episode with slow recovery of consciousness. On admission, the patient had dyspnoea together with physical signs of pulmonary congestion. Cardiac ultrasound showed severe left ventricular dysfunction with an ejection fraction of 30%, and apical and paraapical akinesis. The admission electrocardiogram showed sinus tachycardia and diffuse repolarization abnormalities, hs TnI was elevated and BNP vl blood gas analysis were unremarkable. During observation in the Emergency Department, the patient experienced epileptic seizure, promptly resolved by intravenous Midazolam. Contrast–enhanced chest CT ruled out aortic dissection disclosing a clinically irrelevant subsegmental pulmonary embolism. A non–contrast CT scan of the brain yielded negative results. In consideration of the atypical clinical presentation and scattered evidence of an acute coronary syndrome, the patient was admitted to our Cardiac Intensive Care Unit (CICU) for additional diagnostic workup delaying coronary angiography in the presence of stable TnI elevation. Three hours after admission the patient continued showing altered consciousness and a subsequent brain CT was performed, demonstrating an intraparenchymal hemorrhagic spreading. Repeat ECGs showed precordial T wave inversion and prolongation of the QT interval. These data, together with LV apical dysfunction, raised the suspicion of Takotsubo syndrome secondary to an acute cerebrovascular event. During the hospital stay, recovery of consciousness, spontaneous absorption of the cerebral hemorrhagic spread and complete recovery of left ventricular function at repeat cardiac ultrasound examinations were observed, confirming the almost unequivocal diagnosis of Takotsubo syndrome. The patient was discharged uneventfully and was scheduled for an outpatient coronary CT scan. This case underscores the crucial importance of a thorough clinical assessment in the management of complex patients in the presence of nonunivocal clinical signs and misleading blood test and imaging data. Moreover, Takotsubo syndrome is a protean clinical entity that can precede the index event as in our patient, i.e. cerebral hemorrhage. Misclassification of these patients as having an acute coronary syndrome could have led to the initiation of antithrombotic therapy, with potential fatal outcomes for the patient.

Tensile testing data of additive manufactured ASTM D638 standard specimens with embedded internal geometrical features

Additive manufacturing (AM) is now widely used for research and industrial production. The benchmark data for mechanical properties of additively manufactured specimens is very useful for many communities. This data article presents a tensile testing dataset of ASTM D638 size specimens without and with embedded internal geometrical features printed using polylactic acid (PLA) in a Fused Filament Fabrication (FFF) additive manufacturing process. The added features can mimic defects of various shapes and sizes. This work is a supplement to the published research article Assisted defect detection by in-process monitoring of additive manufacturing using optical imaging and infrared thermography (Additive Manufacturing, 2023, 103483). The printed specimens were tensile tested. Stress-strain graphs were developed and used to calculate the mechanical properties such as ultimate tensile strength (UTS) and strain at UTS. The mechanical properties, the correlations between mechanical properties and size, shape and location of geometrical features (defects), and the trends in mechanical properties can be useful in benchmarking the results of other researchers.

Influences of bedding angle and soaking time on the mechanical behaviour of silty mudstone: laboratory testing and theoretical modelling

Silty mudstone is highly susceptible to the influence of hot and humid environments, leading to the deterioration of its mechanical properties presenting a geohazard and even resulting in geological disasters. Accurately characterizing the effects of bedding and water–rock interaction on the mechanical behaviour of silty mudstone is a crucial prerequisite for the protection and reinforcement of silty mudstone slopes. To this end, uniaxial compression tests and Brazilian splitting tests were conducted to examine the mechanical properties of bedded silty mudstone. Based on the test results, the effects of bedding angle and soaking time on mechanical properties such as tensile strength, uniaxial compressive strength and elastic modulus of bedded silty mudstone were revealed. The test results showed that the tensile strength increased exponentially with increasing bedding angle. The observed split failure patterns of bedded silty mudstone encompassed splitting–pulling damage, shear damage and splitting damage. The uniaxial compressive strength of silty mudstone exhibited a U-shaped variation with an increase in bedding angle. The specimen with a bedding angle of 45° had the lowest uniaxial compressive strength at 7.64 MPa. Furthermore, the elastic modulus of silty mudstone was positively correlated with the bedding angle. The failure patterns of bedded silty mudstone under uniaxial compression included splitting-tension damage, shear-slip damage and splitting-shear damage. The saturated tensile strength, uniaxial compressive strength and elastic modulus of bedded silty mudstone exhibited an exponential decrease with increasing soaking time. The test data confirmed the applicability of the Jaeger equation to the uniaxial compressive strength of bedded silty mudstone. Subsequently, a modified Hoek–Brown failure criterion was derived by combining fracture mechanics theory and test results and introducing a softening factor. The parameters A , D and m in the above failure criterion decreased exponentially with increasing soaking time and an empirical Hoek–Brown failure criterion considering the soaking time was established. Compared with previous theoretical models, this model is more adaptable to the actual engineering situation, which results in more accurate calculations.

A Modular Nitinol Model with Lode Angle-Based Asymmetry and Improved Representation of the Superelastic Loops for Medical Device Applications

Abstract We describe a phenomenological constitutive model for representing Nitinol in medical device applications. The intent is to capture some of the response features important to medical device applications that are neglected in prevalent state of the art models. These features include better representation of the response under compression which is needed to assess bending, and asymmetric plastic yield. We will refer to this implementation as the Thornton Tomasetti (TT) model to distinguish it from other existing implementations that have evolved from the baseline underlying theory. We implement the model in a modular form suitable for use in commercial finite element software and describe initialization from laboratory test data.

Characteristics and Sources of CBM Well-Produced Water in the Shouyang Block, China

The Shouyang Block was selected as the research subject. Comprehensive analysis was conducted using coalbed methane (CBM) well production data, geochemical test data on water produced from the coalbed methane well, and fundamental geological information. The findings reveal the water dynamics in the Shouyang Block are characterized by weak groundwater runoff or retention in most areas. The groundwater head height exhibits a gradual decrease from the north to south, which is closely associated with the monoclinic structure of the Shouyang Block. Overall, water production is relatively high. As the average water production increases, the average gas production gradually decreases. A concentration of high water production wells is observed in the northern part of the Shouyang Block, which gradually increases towards the southeast direction. A comprehensive analysis was conducted on the factors influencing water production, including total water content of coal seams, coal seam porosity, groundwater stability index, groundwater sealing coefficient, D value of the fracture fractal dimension, fault fractal dimension, and sand–mud ratio. The correlation degree was calculated and ranked in order of magnitude through grey correlation analysis. The order of factors that influence water production, from strongest to weakest, is as follows: sand–mud ratio > porosity > fractal dimension of fault > fracture fractal dimension D value > groundwater sealing coefficient > groundwater stability index > total water content of coal seams. The dissolution amounts of carbonate and sulfate are both small, and the water source may mainly come from the sandstone aquifer. Attention should be paid to the distribution and lithological combination of sandstone aquifers in coal-bearing strata in the future exploration and development process of the Shouyang Block. This will help to avoid the potential influence of fault structures and enable the identification of favorable areas for low water and high gas production.

Implications of current soil phosphorus levels for manureshed analysis in North Carolina

AbstractManure relocation strategies are needed to mitigate excessive phosphorus (P) application to agricultural land in areas of intensive animal agricultural production. This requires conceptual frameworks such as the manureshed, which categorizes agricultural areas according to the potential to export or receive manure for P fertilization. To further understand how the manureshed concept could be utilized, assessments of the potential implementation and necessity of the manureshed model are needed. With North Carolina at the center of the largest manureshed in the United States, North Carolina is an ideal test case to identify areas of concern for manure relocation under the manureshed framework. Swine and poultry dominate North Carolina's agricultural production, and because the vast majority of North Carolina producers are not required to limit manure applications to a P‐based rate, P accumulates. Therefore, soil test data from samples submitted to the North Carolina Department of Agriculture and Consumer Services (NCDA&CS) from 2017 to 2019 were used to determine how manureshed classes defined by Spiegal et al. correspond to current soil test P levels. It was determined that 36% of counties experience very high (>100 mg P kg−1; N = 36) median P concentrations in soil. Furthermore, fields cultivated with warm‐season forages had the highest mean P concentration (188 mg kg−1) and high median P trended toward counties with high animal production. Lastly, while mean soil P for all manureshed classifications fell into the very high category, manure source counties had the highest mean soil P concentrations (188 mg kg−1), which was 39%–52% higher than the other classifications. This suggests that, in addition to manuresheds classification, soil test data are needed to design and promote manure redistribution strategies.

A numerical model simulating cyclic behavior of high-strength steel

High-strength steel (HSS) can effectively and economically design structural members that withstand large forces induced by extreme events such as earthquakes. To evaluate the seismic performance of HSS members and structures using nonlinear finite element (FE) analyses, using an accurate material model is important, which can simulate the inelastic cyclic behavior of the HSS. The peculiar material behavior of HSS needs to be considered in the model. This study used a combined hardening model to construct the material model. The configuration of the model and the constituent model parameter values are determined to precisely simulate the low-cycle fatigue (LCF) behavior of HSS. An efficient particle swarm optimization (PSO) algorithm is used to determine parameter values with LCF test data of 54 individual HSS coupons. In additions, to conveniently determine the model parameter values with only monotonic tensile test data instead of conducting expensive LCT tests, empirical equations are proposed. It is shown that the LCF curves of HSS can be accurately simulated using the constructed material model with the proposed equations.

Optimizing reverse osmosis desalination from brackish waters: Predictive approach employing response surface methodology and artificial neural network models

A mathematical model frame has been developed to optimize the performance of reverse osmosis (RO) desalination in brackish waters with medium to high salt concentrations using response surface methodology (RSM) and artificial neural network (ANN), specifically catered towards efficiently operating RO desalination systems. The RSM model aims at predicting RO performance with second-order polynomials based on 15 experimental datasets. Inputs for RSM model include feed TDS concentration, feed water flow rate, and concentration-to-desalination ratio, while the outputs consider rejection, membrane flux, performance index, and specific energy consumption. The Genetic Algorithm optimized back-propagation Neural Network was employed to update network weights and biases, leading to an optimal ANN network structure of 3–10-4. A ratio of 70 % training, 15 % validation, and 15 % test data was chosen to establish an ANN model that predicts the performance of RO desalination. The ANN model demonstrated significantly higher prediction accuracy than the RSM model, with impressive correlation coefficients of 0.9936 for the performance index and 0.9932 for SEC prediction, and low RMSE of 0.85 for rejection. To maximize the performance index and minimize specific energy consumption, a two-step optimization method was further set up by applying the RSM model to initially determine optimal process conditions followed by the ANN model to enhance the accuracy of performance forecasts. Optimal process conditions were proposed for specific feed TDS scenarios, which involved setting a feed TDS concentration limit to meet drinking water standards.

Lap-slip model of rebar-to-concrete in RC/ECC/UHPC based on GA-BP neural network

Rebar lap splicing is a widely used method of reinforcement connection in precast concrete structures, and the lap-slip model serves as an important prerequisite for conducting detailed mechanical calculations of reinforced concrete lap splice members. Most traditional bond-slip mechanical models are semi-empirical and semi-theoretical formulas derived from experimental data; they do not consider the effect of reinforcement lap spacing, which prevents them from accurately reflecting the force transfer characteristics of lap reinforcement in concrete. In this paper, a Back Propagation (BP) neural network based on a Genetic Algorithm (GA) is used to establish a lap-slip model of reinforced concrete considering rebar spacing, which improves on the current bond-slip mechanical model. Several sets of rebar lap tests based on RC/ECC/UHPC were designed, and full-sample parameter sensitivity analyses were carried out using the lap length, the thickness of the concrete cover, the rebar spacing and the stirrup ratio as test parameters. The results showed that rebar lap spacing was an important influencing factor in the proposed lap-slip model, with a sensitivity coefficient reaching 0.471. The lap-slip model, based on the GA-BP neural network, was developed and compared with existing models; the predictive ability of the model was verified by the ten-fold crossover method. The generalizability of the GA-BP model was verified by randomly selecting non-training test data as well as lap test data of rebar in different types of concrete. The results indicated that the model exhibits high predictive and generalization abilities, with most correlation coefficients exceeding 0.95. The proposed model was compared with a mechanical model established using the traditional fitting formula method, and the results showed that the prediction accuracy of the lap-slip model established based on the GA-BP neural network is slightly lower than that of the mechanical model obtained using the fitting formula method, but it still offers high prediction accuracy, and the prediction results are more objective and realistic, which is of guiding significance for practical applications and related research.

A Full-Scale Tidal Blade Fatigue Test using the FastBlade Facility

Fatigue testing of tidal turbine blades requires the application of cyclic loads without the ability to match the natural frequency of the blade due to their high stiffness and the associated thermal issues of testing composite materials at those frequencies (i.e., 18–20 Hz). To solve this, loading the blades with an auxiliary system is necessary; in most cases, a conventional hydraulic system tends to be highly energy-demanding and inefficient. A regenerative digital displacement hydraulic pump system was employed in the FastBlade fatigue testing facility, which saved up to 75 % compared to a standard hydraulic system. A series of equivalent target loads were defined using Reynolds-Averaged Navier Stokes (RANS) simulations (based on on-site collected water velocity data) and utilised in FastBlade to demonstrate an efficient way to perform fatigue testing. During the test, a series of measurements were performed on the blade response and the Fastblade test structure itself, providing novel insights into the mechanical behaviour of a blade, and enabling improved testing practice for FastBlade. Without catastrophic failure, the blade withstood the principal tidal loading for 20 years (equivalent). This test data will enable FastBlade to identify improvements to the testing procedures, i.e., control strategies, load introduction, instrumentation layout, instrument calibration, and test design.

Certification of Meissen Granite IAG GMN‐1 Using the GeoPT Proficiency Testing Certification Protocol

Following a full assessment of the GeoPT proficiency testing scheme against the recommendations in ISO Guide 35:2017 for the use of proficiency testing in the certification of reference materials, this paper presents the first application of the GeoPT certification protocol in the characterisation of a new geochemical CRM, IAG GMN‐1, Meissen Granite. This protocol is applied to the measurement results reported in Round 51 of the GeoPT programme in which the candidate CRM was used as the test material, together with an established CRM (CGL 008 MGT‐1 Granite) to provide validation of the results. Following the presentation of mineralogy, grain‐size analysis and homogeneity testing data for IAG GMN‐1, certified values for nine major elements and thirty‐nine trace elements are reported.

Mechanical properties of geopolymer concrete specimens exposed to fire retrofitted with glass and basalt fiber reinforced polymer laminates

AbstractExposure of concrete to elevated temperature affects its mechanical and physical properties. One of the important reasons for the use of fiber‐reinforced polymer (FRP) composite materials is because of its superior mechanical properties. These mechanical properties include impact resistance, strength, stiffness, flexibility, and also it is enhanced ability to carry loads. The high strength–weight ratio, and corrosion resistance of FRPs made them suitable for repair. For strengthening and retrofitting of concrete structures confinement with FRP has received considerable attention in recent years. Geopolymer concrete (GPC) can be expressed as “concrete without cement.” GPC is environmentally friendly and an inventive way to replace the conservative concrete which contributes 7%–8% of total CO2 production in the world. “The concrete which entails of the source materials and the alkaline solution.” The composite material made of fiber‐reinforced polymer (FRP) has recently gained attention as a potential repair method for concrete structures that have been damaged. This study investigates the performance of glass and basalt fiber‐reinforced polymer (GFRP and BFRP) laminates as a retrofit solution for fire‐damaged GPC specimens. The specimens were subjected to temperatures of 200°C, 400°C, 600°C, and 800°C for a period of 1, 2, and 3 h, followed by retrofitting with FRP laminates. The results indicated an increase in the ultimate load‐carrying capacity of GPC specimens when subjected to 200°C for 1 h, beyond which it decreased. However, a fall in ultimate load capacity was observed for higher ranges of temperature and duration of the fire. In this project, geopolymer specimens that had been damaged by fire are wrapped with GFRP and BFRP laminates to study the effect of confinement on the strength of GPC specimens. The fiber laminate used was bi‐directional glass and bi‐directional basalt. Based on the analysis of test data, there was the conclusion that the fire‐damaged GPC was strengthened when retrofitted with GFRP and BFRP laminates was attainable.