R2 Values Research Articles

Comparative analysis of advanced deep learning models for predicting evapotranspiration based on meteorological data in bangladesh.

Evapotranspiration is one of the crucial elements in water balance equations and plays a pivotal role in the water and energy cycle of an area. An accurate and precise estimation and prediction of reference evapotranspiration (ETo) is necessary for regional management of water resources and irrigation scheduling. The challenge of predicting daily evapotranspiration with limited meteorological data in Bangladesh. This study aims to predict daily evapotranspiration using limited meteorological data of Bangladesh by three deep learning (CNN, GRU, LSTM) and one hybrid (CNN-GRU) model. The novel method of hybrid CNN-GRU models, which have not been commonly used for this purpose. The performance of models was evaluated by five accuracy matrices R2, RMSE, MAE, MAPE, and CE and comparison is visualized by radar graphs. The study's novelty lies in the use of hybrid CNN-GRU models to estimate reference evapotranspiration, as this algorithm has not been commonly used for this purpose. In the case of the Rangpur station, the hybrid CNN-GRU algorithm outperformed other models, achieving the best values across various statistical metrics during both the training and testing phases. The highest correlation coefficient values of approximately 0.994 and 0.995. Moreover, during training and testing stages, the hybrid model had the lowest MAE (0.076, 0.068) and RMSE (0.138, 0.106) at the Rangpur station. Additionally, in the Sreemangal station, it can be notable that the statistical parameter RSME found superior results in the hybrid model around 0.225 and 0.174, respectively. In addition, the highest R2 and CE values were noted as 0.986, 0.987 and 0.985, 0.986 during the training and testing phases, respectively. The comparison suggests that the hybrid model will be best suited for prediction with the limited meteorological data. The outcome of the present research signifies the ability of deep learning methods in the prediction of evapotranspiration and the dominant variables affecting the changes the in context of Bangladesh.

Turkey’s Hydropower Potential in the Near Future and the Possible Impacts of Climate Change—A Case Study of the Euphrates–Tigris Basin

Hydropower is becoming an important renewable energy source in Turkey, but the ever-changing atmospheric and climatic conditions of Turkey make it very difficult to be projected efficiently. Thus, an efficient estimation technique is crucial for it to be adopted as a reliable energy source in the future. This study evaluates Turkey’s hydropower potential in the Euphrates–Tigris Basin under changing climatic conditions. We adapted an empirical equation to model reservoir outflows, considering the site-specific characteristics of 14 major dams. Initial results from employing a model with a constant empirical coefficient, α, yielded moderate predictive accuracy, with R2 values ranging from 0.289 to 0.612. A polynomial regression identified optimal α values tailored to each dam’s surface area, significantly improving model performance. The adjusted α reduced predictive bias and increased R2 values, enhancing forecast reliability. Seasonal analysis revealed distinct hydropower trends: Ataturk Dam showed a notable decrease of 5.5% in hydropower generation up to 2050, while Birecik and Keban Dams exhibited increases of 2.5% and 2.2%, respectively. By putting these discoveries into practice, water resource management may become more robust and sustainable, which is essential for meeting Turkey’s rising energy needs and preparing for future climatic challenges. This study contributes valuable insights for optimizing reservoir operations, ensuring long-term hydropower sustainability, and enhancing the resilience of water resource management systems globally.

Evaluation of lymphovascular space invasion in endometrial carcinoma by APTw and mDixon-Quant.

Lymphovascular space invasion (LVSI) is a strong and independent risk factor that increases the probability of endometrial carcinoma (EC) recurrence and reduces the survival rate of patients. To investigate the value of amide proton transfer weighted (APTw) and mDixon-Quant techniques in evaluating EC lymphovascular space invasion (LVSI). Data of 50 EC patients (18 LVSI+ and 32 LVSI-) confirmed by surgery and pathology were retrospectively analyzed. Preoperative magnetic resonance imaging (MRI) scans included APTw and mDixon-Quant imaging. APT, transverse relaxation rate (R2*), and fat fraction (FF) plots were obtained by postprocessing. The APT, R2*, and FF values of the two groups of cases were measured by two observers. The agreement between the two observers was good. The mean APT, R2*, and FF values of LVSI+ EC were 2.947% ± 0.399%, 20.605 /s (range = 18.525-27.953), and 2.234% ± 1.047%, respectively, while the parameters of LVSI- EC were 2.628% ± 0.307%, 18.968 /s (range = 16.225-20.544), and 2.103% ± 1.070%, respectively. The APT and R2* values of LVSI+ EC were higher than those of LVSI- EC (P < 0.05). There was no significant difference in FF value between the two groups. The AUC values of APT, R2*, and APT + R2* for LVSI were 0.751, 0.713, and 0.781, respectively (all P > 0.05). APT value was moderately correlated with R2* value (r = 0.528, P < 0.001) and weakly correlated with FF value (r = 0.312, P = 0.027). APTw and mDixon-Quant techniques could evaluate the LVSI status of EC, and their combined application could improve diagnostic efficiency.

Leveraging machine learning for sustainable cultivation of Zn-enriched crops in Cd-contaminated karst regions

Karst soils often exhibit elevated zinc (Zn) levels, providing an opportunity to cultivate Zn-enriched crops. (meanwhile) However, these soils also frequently contain high background levels of toxic metals, particularly cadmium (Cd), posing potential health risks. Understanding the bioaccumulation of Cd and Zn and the related drivers in a high geochemical background area can provide important insights for the safe development of Zn-enriched crops. Traditional models often struggle to accurately predict metal levels in crop systems grown on soils with high geochemical background. This study employed machine learning models, including Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost), to explore effective strategies for sustainable cultivation of Zn-enriched crops in karst regions, focusing on bioaccumulation factors (BAF). A total of 10,986 topsoil samples and 181 paired rhizosphere soil-crop samples, including early rice, late rice, and maize, were collected from a karst region in Guangxi. The SVM and XGBoost models demonstrated superior performance, achieving R2 values of 0.84 and 0.60 for estimating the BAFs of Zn and Cd, respectively. Key determinants of the BAFs were identified, including soil iron and manganese contents, pH level, and the interaction between Zn and Cd. By integrating these soil properties with machine learning, a framework for the safe cultivation of Zn-enriched crops was developed. This research contributes to the development of strategies for mitigating Zn deficiency in crops grown on Cd-contaminated soils.

Forecasting Lake Nokoué Water Levels Using Long Short-Term Memory Network

The forecasting of hydrological flows (rainfall depth or rainfall discharge) is becoming increasingly important in the management of hydrological risks such as floods. In this study, the Long Short-Term Memory (LSTM) network, a state-of-the-art algorithm dedicated to time series, is applied to predict the daily water level of Lake Nokoué in Benin. This paper aims to provide an effective and reliable method to enable the reproduction of the future daily water level of Lake Nokoué, which is influenced by a combination of two phenomena: rainfall and river flow (runoff from the Ouémé River, the Sô River, the Porto-Novo lagoon, and the Atlantic Ocean). Performance analysis based on the forecasting horizon indicates that LSTM can predict the water level of Lake Nokoué up to a forecast horizon of t + 10 days. Performance metrics such as Root Mean Square Error (RMSE), coefficient of correlation (R2), Nash–Sutcliffe Efficiency (NSE), and Mean Absolute Error (MAE) agree on a forecast horizon of up to t + 3 days. The values of these metrics remain stable for forecast horizons of t + 1 day, t + 2 days, and t + 3 days. The values of R2 and NSE are greater than 0.97 during the training and testing phases in the Lake Nokoué basin. Based on the evaluation indices used to assess the model’s performance for the appropriate forecast horizon of water level in the Lake Nokoué basin, the forecast horizon of t + 3 days is chosen for predicting future daily water levels.

A-320 Determination of Guanidinoacetate Methyltransferase Enzyme Biomarkers in Newborn Dried Blood Spot by Tandem Mass Spectrometry-A Two Tier Approach

Abstract Background Guanidinoacetate methyltransferase enzyme (GAMT) deficiency is a rare, inherited metabolic disorder that affects the synthesis of creatine. Creatine is an essential metabolite for regenerating energy, particularly in the brain and muscles. Creatine is synthesized from guanidinoacetate (GUAC) by the GAMT enzyme. Mutations in the GAMT gene result in a shortage of the GAMT enzyme that causes a deficiency of creatine and accumulation of GUAC in the body, which is a neurotoxin. Following a recommendation by the Recommended Uniform Screening Panel and in line with California State statute, all California newborns will be screened for GAMT deficiency beginning July 2024. Here, we developed and validated a quantitative two-tier tandem mass spectrometry (MS/MS) assay for the detection of GAMT biomarkers (GUAC, creatine and creatinine) in newborn dried blood spot (DBS). Methods This method punches a 3.2 mm disc of controls and newborn patient DBS into a 96-well plate. 100 μL of extraction solution, containing internal standards of GAMT biomarkers, is added to each well and incubated at 40°C for 45 minutes. The extract is transferred, evaporated, and derivatized into a 3N-butanol-HCl solution at 60°C for 30 minutes. The excess HCl is evaporated to dryness. The conversion of GUAC and creatine to their butyl esters has improved their detection sensitivity by MS/MS. The butyl esters are reconstituted into the mobile phase and shaken for 10 minutes at 27°C. The final 10 μL extract was analyzed by flow injection analysis (FIA)-MS/MS in multiple reaction monitoring (MRM) positive ion mode with a total run time of 1.5 minutes per sample. The presumptive positive samples from initial analysis are prepared following the same method, and a 5 μL extract is resolved through an ultra-performance liquid chromatography (UPLC) column and analyzed by UPLC-MS/MS in MRM positive ion mode with a total run time of 3.5 minutes per sample. The validation was performed by assessing the linearity, accuracy, imprecision, carryover, limit of detection (LOD) and lower limit of quantitation (LLOQ), matrix effect, analytical measurement range (AMR), percent total allowable error (TEa), and reference range. Results This method is linear for GUAC, creatine and creatinine over a measured range of 1.17-40.00, 121.20-857.36 and 41.34-387.46 µmol/L respectively. The R2 values=1.000, 0.997, 0.9998; slope=0.999, 0.9945, 0.9982; intercept=0.0398, 4.881, 1.7027; mean recoveries=99-104%, 100-104%, 100-104%; intra-assay precision (n=20) =4.05-5.95%, 4.62-5.31%, 3.27-5.19%, inter-assay precision (n=40)=3.7-12.6%, 4.3-7.6%, 3.8-7.4%; TEa=6-21%, 7-17%, 6-17%; LOD=0.12, 0.54, 1.11 µmol/L; AMR=0.33-1300, 1.11-2700, 3.50-4500 µmol/L; median patient results observed from 2293 normal patient=1.48, 494.02, 89.97 μmol/L for GUAC, creatine, and creatinine respectively. The tier-1 and tier-2 assay screening cutoff for GUAC at the population 95th percentile is 2.53 and 4.55 μmol/L respectively. Conclusions The tier-1 and tier-2 methods has been developed and validated to determine GAMT biomarkers in newborn DBS, making it suitable for routine screening of ∼2000 newborn specimens per day by tier-1 and followed by more specific tier-2 to separate GAMT biomarkers from potential isobaric interfering compounds. This two-tier approach greatly improved the test performance and significantly reduced false positive rate.

A-238 Lyophilized Bead Multiplex Assays for CT/NG/TV Detection

Abstract Background Chlamydia trachomatis (CT), Neisseria gonorrhoeae (NG), and Trichomonas vaginalis (TV) cause the three most prevalent sexually transmitted infectious diseases in developed countries, contributing to approximately 300 million new infections worldwide, annually. Due to the challenges of culturing and using immunoassays to detect these pathogens, DNA-based molecular tests have been instrumental, and current trends in molecular testing for CT/NG/TV have moved toward point-of-care tests. Lyophilization provides benefits required for point-of-care tests, such as ambient storage, extended shelf life, simplified downstream workflow, and variable formats. A lyophilized-bead format enables an assay to be placed in sealed consumables and stored at room temperature for up to two years. An additional consideration is the need for endogenous and exogenous controls. Exogenous in-process controls can be used to validate the performance of workflows culminating in a qPCR reaction and provide flexibility to test developers. An ideal exogenous in-process control is nonhomologous to human or human pathogen genome sequences and does not align with primers or probes designed for pathogen detection. The objective of the current study was to develop a lyophilized-bead-format assay containing an ideal exogenous control and establish whether it is a valid option for the accurate detection of CT, NG, and TV. Methods A synthetic DNA control containing targets for CT/NG/TV/RNase P (RP)/exogenous control was used to establish an analytical LOD by qPCR. The 5-plex assay, lyophilized in beads, was hydrated and tested for linearity over an 8-log range of the synthetic DNA control. Further, the qPCR lyophilized bead with assay was tested with a proficiency sample by extracting DNA from ZeptoMetrix NATtrol CT/NG/TV Positive Control and spiking-in the exogenous DNA control. qPCR was performed on the extracted DNA and human cDNA. Results The analytical LOD for the control DNA was determined to be 5 copies/reaction. Additionally, linearity was observed by qPCR over an 8-log range of the synthetic DNA control for all five targets in multiplex with good efficiencies and R2 values. Finally, all targets were amplified and detected by qPCR using DNA extracted from ZeptoMetrix NATtrol CT/NG/TV Positive Control with spiked-in exogenous DNA control. The detection of the exogenous sequence in the qPCR multiplex reaction indicated the extraction was successful. Conclusions We have developed a multiplex qPCR assay in lyophilized-bead format that can identify infectious agents, such as CT/NG/TV, in a single reaction. We have included RP as a human host control, plus an exogenous in-process control, for a total of 5-plex in separate fluorescent channels. We have shown that our lyophilized beads with assay produce a positive qPCR result when combined with a proficiency sample.

B-248 Enhanced Detection of of Hemoglobin Variants in Clinical Research using Dried Blood Spot and HRAM MS

Abstract Background Hemoglobinopathies are one of the most prevalent genetic disorders, with more than 330,000 affected births occurring annually. Common techniques used in Hb analysis are electrophoretic and chromatographic assays. However, there are challenges to differentiate isomers (i.e., between Hb C and Hb E), very similar masses, or new variants due to their lack of resolution. Mass spectrometry (MS) gained popularity in clinical research because of its robustness and versatility in analyzing a wide range of samples and analytes from small molecules to proteins. Here, we present the top-down analysis for enhanced detection of various hemoglobin variants using Orbitrap Exploris for clinical research. Methods Proteins were directly extracted from dried blood spots (DBS) and analyzed by Thermo Scientific™ Orbitrap Exploris™ 240 with BioPharma Option coupled to Vanquish HPLC system. A 3.2 mm disc was punched from the DBS cards to 96 well plate and 50 uL water was added to re-hydrate. For protein precipitation, 150 uL acetonitrile was added and incubated at -20 ⁰C for 15 minutes. After micro-centrifugation, 160 uL of supernatant was removed and 70 uL water was added to resuspend. For LC-MS analysis, 10 uL of supernatant was diluted 8 times with mobile phase A and transferred to another 96-well plate. LC separation was performed on MAbPac RP column 4 um, 1 x 100 mm, and data-dependent acquisition mode was operated. Results With direct protein extraction from DBS, the entire sample preparation time is less than 1 hour. The MAbPac column showed its capability of separating between different chains with minor overlap between normal beta and HbS beta chains. Data processing was performed by ProSightPD, resulting in ∼45% sequence coverage for both normal Hb and HbS beta chains. The mass difference between normal beta and Hb S beta chains is about 30 Da, which results in about 1.6 m/z difference at a charge state of 19. This mass difference can often be hindered by other proteins or interferences from the matrix if the mass analyzer doesn’t have sufficient resolving power. Here we successfully applied Orbitrap resolution at 120,000 to isolate these similar masses for accurate variants or subunits identification including normal alpha, normal beta, Hb S, and delta chains. The confirmation of the delta chain can be beneficial since its abundance is usually less than 0.5% in adults, which shows potential for detecting low abundant variants using this method. Also, the calibration curve was generated to demonstrate the method's feasibility which is not a typical quantitation method for hemoglobinopathies. For quantitation, three most abundant m/z values from 2 charge states were added to TraceFinder for peak area extraction and normal Hb beta chain was used as an alternative internal standard for data processing. The 3-day inter/intraday analysis generated great reproducibility with CV &amp;lt; 10% and LOQ was determined to be 2.5 mg/mL with R2 values &amp;gt;0.994. Conclusions Successful demonstration of a single-step protein extraction directly from DBS and top-down analysis of Hb variant for clinical research.

M, NM-polynomials Based on Reverse, Reduced Reverse Degree and Neighborhood Degree Based Topological Indices with Applications to Bond Energy of Y-Junction Nanotubes.

In graph theory, M polynomials like the matching polynomial are very crucial in examining the matching structures within graphs, while NM polynomials extends this to analyze non-matching edges. These polynomials are important in many fields, including chemistry and network architecture. They support the derivation of topological indices for protein structure analysis, network communication optimization, and drug design in QSAR/QSPR investigations. The aim of this paper is to define novel M and NM polynomials for different topological indices and to derive their closed-form expressions, specifically for Y-junction nanotubes. These new polynomials and indices are employed to create a robust QSPR model to predict bond energy in Y-junction nanotubes, that provide high accuracy and reliability in the model's statistical performance. This paper introduces new forms of M and NM polynomials tailored to specific topological indices related to reverse and neighborhood reverse properties. We derive closed-form expressions for these indices in Y-junction nanotubes. Furthermore, we develop a QSPR model to predict bond energy in Y-junction nanotubes using the newly defined indices. We define novel M and NM polynomials for various topological indices and derive precise expressions for Y-junction nanotubes. Utilizing these indices, we construct a highly accurate QSPR model (R² = 0.999) for predicting bond energy in Y-junction nanotubes, confirming the validity of our polynomial definitions and indices. We have presented new M and NM polynomials for different topological indices and derive their expressions specifically for Y-junction nanotubes. With these newly defined indices, we have developed a highly precise QSPR model to predict bond energy, achieving an R² value of 0.999. This work underscores the effectiveness of our polynomial definitions and indices in predicting material properties.

Multitemporal UAV study of phenolic compounds in slash pine canopies

Phenolic compounds (PC) are important secondary metabolites in plants, playing a crucial role in plant defense mechanisms against pathogens and other plants. Monitoring PC levels is important for understanding tree stress and implementing effective breeding programs. However, traditional methods for monitoring PC are time-consuming, prone to altering the phenolic composition, and mostly applicable only on a small scale. In this study, we evaluated the performance of Unoccupied Aerial Vehicles (UAV) multispectral imaging in estimating the canopy phenolic content in slash pine over an 11-month period in 2021 and a seven-month period in 2022. Three machine learning models including Partial least squares regression (PLSR), Random forest (RF) and Support Vector Machine (SVM) were compared to determine the optimal predictive model for canopy PC. The RF model provided the best predictive results, with R2 values of 0.82 for the validation set and 0.94 for the calibration set. Additionally, the study assesses the heritable variation in canopy PC over time, with the monthly heritability (h2) of PC ranging from 0 to 0.26 in 2021 and from 0 to 0.35 in 2022; The highest h2 levels were observed in July and September 2021and July 2022. The findings demonstrate significant genetic control over the variation of PC. Furthermore, we observed higher breeding values and genetic gains in July and November, which further supports the strong correlation between PC and environmental factors such as temperature and light intensity. To the best of our knowledge, this is the first study to employ time-series UAV multispectral imaging to predict secondary metabolites in pine trees and estimate their genetic variation over time. As a proof of concept, these findings provide more reliable information for tree breeding programs, ultimately enhancing their overall performance.

Moisture content determination along production line of ibuprofen soft gelatin capsule manufacturing by near infrared spectroscopy and ensemble deep neural networks

AbstractIn the manufacturing process of ibuprofen soft gelatin capsules, controlling moisture content at each production line stage, including in their components—gelatin, fill content, and shell—is vital to ensure quality and stability. This study developed and assessed an analytical method for rapid and non‐destructive moisture determination using near‐infrared spectroscopy (NIRS) coupled with deep neural networks (DNN) on all stages of the ibuprofen production line. The NIRS‐DNN classifier models were able to distinguish between the three components, achieving accuracy scores of up to 99%. The DNN models for moisture quantification also demonstrated high accuracy, with R2 values exceeding 0.997 across all production stages and prediction errors to those of previously reported models. A significant advantage of the NIRS‐DNN approach was its ability to maintain accuracy over a wide moisture concentration range, from 7% to 48%. The ensemble model, NIRS‐EDNN, seamlessly integrated classification and quantification, revealing its potential for real‐time process control in soft gel manufacturing. The comprehensive sampling approach ensured a diverse representation of moisture content, thereby enhancing the understanding of its impact on the final product stability, demonstrating that this methodology is potentially applicable to any soft gelatin capsule tracking worldwide.

Application of a support vector regression model employing meta-heuristic algorithms for estimating pile bearing capacity

ABSTRACT The design of pile foundations hinges significantly on the pile-bearing capacity (PBC), a crucial factor affected by numerous soil features and parameters. Piles serve as a key component in relocating structural loads to the ground, underscoring the need for accurately determining PBC in geotechnical design. While prior research has delved into utilizing artificial neural networks (ANN) for predicting PBC, they encounter shortcomings such as grappling with finding global minima and slow convergence rates. This study introduces Support Vector Regression (SVR) as a machine-learning approach, augmented by two meta-heuristic optimization techniques: the Giant Trevally Optimizer (GTO) and Smell Agent Optimization (SAO), to achieve optimal results. These techniques empower engineers and data scientists to develop more precise and reliable predictive models in geotechnical engineering, ultimately enhancing the safety and efficiency of foundation design and construction. Drawing upon an extensive dataset compiled from previous studies, a predictive model was constructed to estimate PBC using soft computing techniques. The outcomes overwhelmingly favored the SVGT model, a fusion of the SVR model with GTO, demonstrating excellent anticipative capacities with an outstanding R2 value of 0.998 and an impressively low RMSE value of 91.21.

B-030 Performance evaluation of Siemens Atellica® IM Anti-Müllerian Hormone Assay

Abstract Background Anti-Müllerian hormone (AMH), a glycoprotein dimer, is produced by ovarian granulosa cells and its levels have been shown to directly correlate to the number of eggs remaining in the ovaries (ovarian reserve). Thus, measurements of AMH are widely used as an aid in the assessment of the ovarian reserve in women presenting to fertility clinics. This study aimed to evaluate the performance of the Siemens Atellica® IM AMH assay and compare it to the Beckman Access AMH assay. Methods The Atellica AMH assay is a fully automated chemiluminescent monoclonal sandwich immunoassay, while the Beckman Access AMH assay is also an automated chemiluminescent monoclonal sandwich immunoassay. Precision and linearity were assessed for the Atellica AMH assay using manufacturer-provided standards. Inter-laboratory correlation studies were completed using deidentified patient samples collected from 120 reproductive-age women undergoing fertility outpatient evaluation at Yale New Haven Health. Blood was drawn on the first office visit for fertility workup. AMH values were initially performed at Yale University using the Beckman Access AMH assay. Serum samples were then frozen (-20°C) and shipped on dry ice to the laboratory at the Ohio State University where they were thawed and analyzed using the Siemens Atellica AMH assay. Linear regression was employed for data analysis. Overall percent agreement using an AMH cut-off value of 1.77 ng/mL was calculated. AMH values &amp;gt;1.77 ng/mL were classified as positive and values ≤1.77 were classified as negative. Results The Siemens Atellica AMH assay exhibited good performance across the measuring range, with intra-assay and inter-assay coefficients of variation (CVs) ranging from 1.4% to 3.2% and 2.5% to 6.0%, respectively. The assay demonstrated linearity over the four recommended ranges (0.043-24.0 ng/mL) without significant bias observed. The Access and Atellica AMH assays demonstrated good overall agreement of 97% and were well-correlated with a R2 value of 0.9721. Conclusions The AMH assay was validated and routinely measured in-house using the Atellica automated assay. The Siemens Atellica AMH assay performs well across its measuring range and shows good agreement with Beckman Access AMH at the AMH cut-off of 1.77 ng/mL.

Prediction of Wear Properties of CaO and MgO Doped Stabilized Zirconia Ceramics with Artificial Neural Networks

In this research, the production and wear behavior of CaO and MgO doped stabilized zirconia ceramics prepared by powder metallurgy method were investigated and artificial neural network (ANN) models were established and predicted with the data produced as a result of real experiments. CaO/MgO doped stabilized zirconia ceramics were produced using a combination of ball milling, cold pressing - cold isostatic pressing and sintering methods. CaO and MgO were mixed with zirconia in different amounts (0-8 mol%). These mixtures were prepared by mechanical alloying. Green compacts were sintered at 1600°C. The wear experimental results produced from the trials were converted into data suitable for modelling with ANN. Wear load, wear time of the load, MgO and CaO were given as input to the ANN model. The amount of wear according to the pressing method was set as the output variable value of the ANN. An ANN approach model was established to predict the wear behavior characteristics of zirconia ceramic composites. In order to emphasize the success of the model, the test data set was presented to the ANN model and the results produced were compared with the results produced experimentally, and as a result of these tests, high R2 values of 0.99409 for 65N and 0.97512 for 80N were produced.

Hybrid multi-stage steel footprinting unveils a more interdependent material foundation of the global economy

Steel is foundational to modern society, yet tracking its socio-economic metabolism is challenging due to the complex global trade networks. Traditional indicators, such as domestic material consumption (DMC) and consumption-based material footprints (MF), typically focus on metal ore extraction, overlooking the multiple stages of steel production and consumption. To address this, we integrate multi-national anthropogenic steel cycles and international trade networks of steel-containing products into a global monetary multi-regional input-output (MRIO) model. We construct material efficiency indicators based on footprints of iron ores, crude steel, castings, finished steel, and fabricated steel products, comparing them with conventional economy-wide material flow indicators like domestic material production (DMP) and DMC. Our findings reveal that per capita multi-stage MF indicators exhibit more robust log-linear relationships with per capita GDP with an average R2 value of 0.72 compared to 0.10 and 0.18 for DMP and DMC. Shares of Embodied trade in total global production exceed direct trade by 24 percentage points on average, emphasizing the significance of international embodied metal transfers. Using multi-stage MF indicators also reduces disparities in material efficiency between developed and developing countries. This study unravels the intricacies of global steel supply chains and the true interdependencies of steel-containing products among countries.

Artificial intelligence in education: implications for academic integrity and the shift toward holistic assessment

This study examines the impact of Artificial Intelligence (AI) on the field of education, with particular focus on its implications for academic integrity and the adoption of comprehensive assessment approaches. This research fits within the specific setting of university students and faculty members in the Kingdom of Bahrain.MethodsA cross-sectional survey was designed to examine the impact Artificial Intelligence (AI) in field of education, with particular focus on its implications for academic integrity and the adoption of comprehensive assessment approaches. A total of 218 participants were randomly selected from 250 employed in this survey study.ResultsOut of 250 invited participants, 203 responded to the survey. This study evaluated the influence of Educational Impact (EI), Policy and Ethics (PE), and Pedagogical Implications (PI) on Academic Outcomes (AO). Results revealed a significant association between EI → AO with a beta of 0.490, t-value of 4.504, and p &amp;lt; 0.001. PI also showed a significant relationship (β = 0.454, t = 2.330, p = 0.010) with more variability. PE’s impact on AO was modest (β = 0.243, t = 1.977, p = 0.024). Overall, EI was the strongest AO predictor. The R2 value was approximately 39%, indicating a good fit.ConclusionThe research reveals a strong link between the Educational Impact (EI) of AI and academic success in Bahrain’s universities, with EI being the primary predictor. Both Policy and Ethics (PE) and Pedagogical Implications (PI) play crucial roles in this relationship.

Synthesis, Microstructural Investigation, and Mechanical Behavior of AA5083/Boron Nitride Nanocomposite

The as‐received aluminum alloy (AA5083) powders (44 μm) and hexagonal boron nitride (BN) nanoparticles (65–75 nm) are used to fabricate AA5083‐BN nanocomposites with varying BN content (0, 3, 6, 9, and 12 wt%). A powder metallurgy solid‐state method is employed, involving ball milling for 20 h at 100 rpm with a ball‐to‐powder mass ratio of 10:1. The processed powders are then consolidated through forging–sintering at 550 °C for 30 min, followed by hot forging at 225 MPa. The microstructure is examined using X‐ray diffraction, scanning electron microscopy, energy‐dispersive X‐ray, transmission electron microscopy, and electron backscatter diffraction to understand the effect of processing variables. The compaction behavior is investigated both experimentally and empirically, revealing a relative green density exceeding 88% at 500 MPa. The empirical models display coefficients of determination (R2 values) exceeding 99% for predicting the compaction behavior. Compression tests on bulk samples show that BN reinforcement significantly enhances ultimate compressive strength, with values reaching 473.256 ± 5.54, 536.374 ± 2.87, 567.694 ± 4.22, and 601.911 ± 6.54 MPa for TRB‐3, TRB‐6, TRB‐9, and TRB‐12, respectively. The developed composites achieve relative densities greater than 97%, indicating promising applications in the aerospace, automotive, and marine industries.

Comparative Analysis of Deep Learning and Machine Learning-based Models for Simultaneous Prediction of Minerals in Perilla (Perilla frutescens L.) Seeds Using Near-Infrared Reflectance Spectroscopy

Perilla seeds contain a rich array of essential minerals, thus having the potential to address multiple micronutrient deficiencies at a time. However, traditional methods of mineral estimation are complex, time-consuming, expensive, and require technical expertise. This study includes the development of Near-Infrared Reflectance Spectroscopy (NIRS)-based prediction models for predicting five important minerals (Calcium, Copper, Magnesium, Manganese, and Phosphorus) using machine learning and deep learning techniques. Four models, including 1D Convolutional Neural Networks (1D CNNs), Artificial Neural Networks (ANNs), Random Forests (RFs), and Support Vector Regression (SVR), were developed and evaluated. The developed 1D CNN model outperformed other considered models in predicting calcium, magnesium, and phosphorus content with RPD (Residual Prediction Deviation) values of 1.75, 1.83, and 2.96, respectively. Whereas SVR performed best in predicting copper and manganese with an RPD of 1.82 and 2.2, respectively. The 1D CNN model demonstrated R2 (Coefficient of determination) values above 0.65 for all minerals, with a maximum of 0.88 for phosphorus. In addition, the developed models performed superior as compared to the Partial Least Square Regression method (R2= 0.32). The developed models provide efficient tools for rapidly screening perilla germplasm available in global repositories, thus aiding in the selection of mineral-rich genotypes to mitigate micronutrient deficiencies.

Time Series Analysis of Sugarcane Plant Height and Planting Area Identification Based on DPRVI and RF Algorithm

With the modernisation of agriculture, the time series analysis of sugarcane growth and the identification of planting areas play an important role in the investigation and detection of agriculture. The study conducted a time series analysis of sugarcane plant height and identification of planting areas based on dual polarisation radar vegetation index and random forest algorithm. Firstly, dual-polarisation radar vegetation index data of sugarcane were obtained using Sentienl-1A radar remote sensing data for time-series analysis of sugarcane plant height. Then the random forest model was applied to the regression and classification of Sentinel-2A remote sensing images for sugarcane plantation area identification. According to the findings, sugarcane field 4 had an R2 value of 0.835 in the quadratic regression model inversion, and the average absolute difference between the model’s predicted plant height and the actual value was only 7.3%. It shows that the dual-polarization radar vegetation index has great reliability and is capable of accurately predicting sugarcane plant height during the pre-growth stage. The user and producer accuracies are as high as 0.911 and 0.989, respectively, and the overall accuracies and Kappa coefficients are as high as 0.976 and 0.932, respectively, indicating that the Random Forest with multi-temporal phase and multi-feature is able to effectively identify sugarcane planting areas. The accuracy under multi-temporal phase and multi-feature is better than the single-temporal phase. It offers a trustworthy way for developing contemporary agricultural policy and development planning.

Green Synthesis Study: Adsorption of Congo Red Dye with Selenium Nanoparticles Obtained from Prunus Armeniaca L. Leaf Waste

AbstractIn this study, it was aimed to remove Congo Red (CR) dye in aqueous solution by biosynthesized Selenium Nanoparticles from Prunus armeniaca L. (PAL‐SeNPs) leaf wastes by green synthesis method. The characteristic structure of PAL‐SeNPs was determined by UV‐vis spectroscopy, X‐ray diffraction (XRD), Brunauer–Emmett–Teller (BET), scanning electron microscope (SEM), energy dispersive X‐ray (EDX), fourier transform infrared spectroscopy (FTIR), thermogravimetry‐differential thermal analysis (TGA‐DTA), dynamic light scattering (DLS), zetasizer, and point of zero charge (pHpzc). The effects of pH, adsorbent amount, time, initial concentration, and temperature were investigated by batch adsorption studies. 2 different kinetic and 4 isotherm models were tested and error analysis functions were determined for the most suitable model. Accordingly, the particle size, crystallinity, pHpzc value and average surface charge of PAL‐SeNPs were determined as 9.969 nm, 48.50 %, 3.47, and −23.6 mV, respectively. Also, the most suitable kinetic and isotherm models for the removal of CR dye with PAL‐SeNPs were found as Pseudo‐second‐order and Freundlich, with R2 values of 0.996, respectively. Also, where the optimum pH was 7.00, the maximum adsorption capacity was calculated as 96.59 mgCR/gPAL‐SeNPs. The results obtained show that environmentally friendly and low‐cost PAL‐SeNPs produced by the green synthesis method are a suitable alternative for the removal of CR dye.