Lower Mean Research Articles

Global evaluation of sentinel 2 level 2A Sen2Cor aerosol optical thickness retrievals

ABSTRACT The present study aims to conduct the first known comprehensive global evaluation of Aerosol Optical Thickness (AOT) retrievals derived from the Sentinel 2 Sen2Cor algorithm between 2018 and 2022, using data from over 400 AERONET stations. The results indicate that Sentinel 2 tends to underestimate AOT, especially at higher aerosol loadings. Although there appears to be a good overall correlation (r = 0.57) with low RMSE (0.16) and relative mean bias (RMB = -2.46%) between AERONET and Sentinel 2 AOT datasets, regional analysis reveals significant variation in performance across regions, with areas like Europe and Americas exhibiting stronger correlations and lower RMSE than others like Indian subcontinent and Southern Africa. Temporal analysis also suggests an improvement in Sentinel 2 performance, particularly in capturing extreme AOT values in 2022. While elevation does not appear to have any noticeable impact on AOT estimation, slight variations could be observed over certain land cover types like sparse vegetation and permanent water bodies. Though improvements are certainly required over certain geographical regions like Indian subcontinent, Sentinel 2 AOT can, nevertheless, be reliably used for aerosol studies in urban areas or at a finer spatial resolution, especially in Europe, Mainland U.S.A. and East Asia.

Prediction of cardiovascular events and all-cause mortality using race and race-free estimated glomerular filtration rate in African Americans: the Jackson Heart Study

BackgroundNew Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations without a race adjustment were developed to estimate the glomerular filtration rate (eGFR). We aimed to compare the performance of five CKD-EPI eGFR equations, with or without race, in predicting cardiovascular disease (CVD) events and all-cause mortality in Black Americans from the Jackson Heart Study.MethodsJHS is an ongoing population-based prospective cohort study of African Americans in the Jackson, Mississippi, metropolitan area. Five CKD-EPI equations were used to estimate GFR at baseline using serum creatinine (Cr) or cystatin C (cys), including 2009 eGFRcr(ASR [age, sex, race]), 2021 eGFRcr(AS [age and sex]), 2012 eGFRcr-cys(ASR), 2021 eGFRcr-cys(AS), 2012 eGFRcys(AS). Endpoints were incident CVD events and all-cause mortality. Cox proportional hazards regression was used to assess the associations between different eGFRs and outcomes adjusting for atherosclerotic risk factors. Harrell’s C-statistics and Net Reclassification Index (NRI) were used to assess the predictive utility.ResultsAmong 5,129 participants (average age 54.8 ± 12.8 yrs), 1898 were male (37.0%). eGFRcr(AS) provided lower estimates and resulting in a greater proportion of participants categorized as CKD than eGFRcr(ASR), eGFRcr-cys(ASR), eGFRcr-cys(AS) and eGFRcys(AS). A median follow-up of 13.7 and 14.3 years revealed 411 (9.3%) CVD incidents and 1,207 (23.5%) deaths. Lower eGFRs were associated with CVD incidents and all-cause mortality. eGFRcr-cys(ASR), eGFRcr-cys(AS) and eGFRcys(AS) were strongly associated with incident CVD events and all-cause mortality than eGFRcr(ASR) and eGFRcr(AS). A significant discrimination improvement was found in C-statistics for predicting incident CVD events and all-cause mortality after adding each eGFR measure to the basic model including atherosclerotic risk factors. Across a 7.5% 10-year risk threshold, eGFRcys(AS) improved net classification of all-cause mortality (NRI: 2.19, 95%CI: 0.08, 4.65%).ConclusioneGFR based on creatinine omit race has the lowest mean and detects more CKD patients in Black population. The eGFRs incorporating cystatin C strengthens the association between the eGFR and the risks of incident CVD and all-cause mortality. Cystatin C-based eGFR equations might be more appropriate for predicting CVD and mortality among Black population.

Plasmonic properties of silicon nitride encapsulated copper

We investigated the optical properties (i.e. plasmonic loss) of thin-film copper that was deposited and processed in a 300mm CMOS pilot line. The optical properties at 1550 nm (QSPP,Cu = 3921 ± 350) were evaluated by measuring the propagation loss of dielectric loaded plasmonic waveguides on samples with high and low root mean square surface roughness (Rq) with and without a silicon nitride diffusion barrier and at room temperature and cryogenic temperatures. In our fabrication result, the average grain size is 2.08±0.05μm, which is much larger than the mean free path of free electrons in copper, so the surface roughness becomes the main cause of the waveguide propagation loss from the fabrication constrains. Further, we show that copper can be encapsulated by a 15 nm silicon nitride diffusion and oxidation barrier without degrading the optical properties.

Analysis of the Effectiveness of Multi-Armed Bandit Algorithms in Crop Pricing

This study explores the application of multi-armed bandit (MAB) algorithms in dynamic pricing of crops, with a focus on evaluating the adaptive upper confidence bound (asUCB) and Thompson Sampling (TS) algorithms. Through simulation experiments on historical data, the study analyzed the performance of these algorithms in fitting actual market price trends and responding to future price fluctuations. The results indicate that the asUCB algorithm performed particularly well in both the training dataset and simulation tests, demonstrating low mean squared error (MSE) and minimal cumulative regret, reflecting its rapid convergence and stable pricing capabilities. In contrast, although the TS algorithm initially performed slightly less effectively, it demonstrated unique advantages in dealing with market volatility due to its strong adaptability. This study demonstrates the potential application of MAB algorithms in dynamic pricing, providing valuable insights for pricing strategies in the agricultural product market.

Aortic valve calcium score to mean gradient ratio: a new frontier in aortic stenosis prognostication

Abstract Introduction Aortic stenosis (AS) presents diagnostic challenges, especially in patients with discordant low gradient AS (LGAS), i.e., small aortic valve area (AVA) and low Doppler mean gradient (MG). LGAS is associated with worse outcomes compared to concordant AS. Aortic valve calcium score (AVCS) is a measure of AS severity independent of flow and helps diagnose severe AS. It is unknown if the degree of discordance in AVCS and MG is of any clinical significance. A high AVCS/MG ratio may indicate impaired ventricular function and response to pressure overload despite preserved LVEF, potentially contributing to worse outcomes. Purpose This study aims to elucidate the prognostic significance of the AVCS/MG ratio in patients with severe AS and preserved left ventricular ejection fraction (LVEF) undergoing TAVR. We hypothesized a higher AVCS/MG ratio correlates with increased mortality. Methods Patients who underwent transcatheter aortic valve replacement (TAVR) from 2010 to 2020 for native valve severe AS with LVEF≥50% were identified from an institutional database. AVCS were obtained within 90 days of pre-TAVR echocardiography. Patients were divided into tertiles based on their AVCS/MG ratio. Primary outcome was overall mortality. Results The analysis included 1067 patients (mean age 79±9 years, 43% female); overall AVA was 0.80±0.14 cm², LVEF 63±6%, MG 46±12 mmHg, AVCS 2630±1528 AU, and AVCS/MG ratio 58±31. Patients stratified by AVCS/MG tertiles showed no difference in overall comorbidity index across tertiles (Figure 1). However, the group in the highest tertile was the oldest (80±8 vs 79±8 vs 77±9 years), predominantly male (87% vs 61% vs 24%), with the highest prevalent atrial fibrillation (44% vs 28% vs 25%) and stroke volume index <35 mL/m² (16% vs 11% vs 5%). 9-year survival was 15% vs 25% vs 36% (p<0.001, Figure 2). In multivariate analysis adjusted for age, sex, comorbidities, LVEF and AVA, AVCS/MG ratio was independently associated with increased risk of mortality (HR 1.005; 95% CI [1.001-1.008], p=0.005). Conclusion Higher AVCS/MG discordance is associated with increased risk of mortality following TAVR. Serial AVCS measurements during progressive AS may help identify patients at higher risk who may potentially benefit from earlier aortic valve intervention.

Phenotype of patients with severe aortic stenosis and small aortic annulus: how does it impact on hemodynamics and on transcatheter aortic valve implantation outcomes

Abstract Background Discordant grading of severe aortic stenosis can result from a tight aortic valve area (<1cm2) with a low transvalvular mean gradient (<40 mmHg). Small aortic annulus dimensions and low flow status may contribute to discordant grading of severe aortic stenosis. However, the frequency of patients with small aortic annulus and discordant grading, along with their characteristics and outcomes after intervention, remains unknown. Purpose To investigate the frequency of discordant grading severe aortic stenosis in patients with a small aortic annulus and to evaluate their clinical characteristics, as well as outcomes after transcatheter aortic valve implantation (TAVI). Methods Patients with an aortic annulus diameter < 23 mm determined by cardiac computed tomography who underwent TAVI between 2018 to 2023 were retrospectively evaluated. Patients were followed for the occurrence of the composite endpoint of of all-cause mortality, rehospitalizations for heart failure, non-fatal myocardial infarction, and non-fatal stroke. Results Among 230 patients with severe aortic stenosis and a small aortic annulus, 52 (23%) had discordant gradient (24 with low flow low gradient (LFLG) and 28 with normal flow low gradient (NFLG)) while 120 (52%) exhibited normal flow high gradient (NFHG) and 58 (25%) had low flow high gradient (LFHG). The majority of the patients were female (Table 1). Differences were noted among the three groups regarding aortic transvalvular gradients, stroke volume index and ejection fraction. During a median follow-up of 2 years, the composite endpoint occurred in 20% of the total cohort. Patients with discordant grading severe aortic stenosis experienced the worse outcome (Figure1). Conclusions Most patients with severe aortic stenosis and small annulus have high gradient and only 23% of patients presented discordant grading. Patients with discordant grading severe aortic stenosis showed worse outcomes, suggesting potential delays in referral due to challenges in diagnosing severe aortic stenosis among patients with small aortic annulus.Basal clinical characteristicsKaplan-Meier survival curves

Accurate protein-ligand binding free energy estimation using QM/MM on multi-conformers predicted from classical mining minima

Accurate prediction of binding free energy is crucial for the rational design of drug candidates and understanding protein-ligand interactions. To address this, we have developed four protocols that combine QM/MM calculations and the mining minima (M2) method, tested on 9 targets and 203 ligands. Our protocols carry out free energy processing with or without conformational search on the selected conformers obtained from M2 calculations, where their force field atomic charge parameters are substituted with those obtained from a QM/MM calculation. The method achieved a high Pearson’s correlation coefficient (0.81) with experimental binding free energies across diverse targets, demonstrating its generality. Using a differential evolution algorithm with a universal scaling factor of 0.2, we achieved a low mean absolute error of 0.60 kcal mol-1. This performance surpasses many existing methods and is comparable to popular relative binding free energy techniques but at significantly lower computational cost.

An Artificial Neural Network Model for Short-Term Traffic Flow Prediction in Two Lane Highway in Khulna Metropolitan City, Bangladesh

Short-term traffic flow prediction is one of the most significant research topics in traffic engineering. It is instrumental in designing a more modern transport network to manage traffic signals and reduce congestion. Short-term traffic flow is a challenge that a third-world country like Bangladesh is all too familiar with. Like the other cities of Bangladesh, Khulna Metropolitan City is gradually becoming more aware of this situation. The Khulna-Jashore National Highway (N-7), which runs through the city and provides a linear shape, serves as the backbone of the Khulna Metropolitan City traffic flow. This study developed an Artificial Neural Network (ANN) model for the short-term Traffic Flow Prediction on Two-Lane Highway in Khulna Metropolitan City, Bangladesh. Data was collected from March 1, 2021, through June 30, 2021, during 600–900 hours and 1200–1500 hours. Good-quality electronic cameras recorded the vehicles at the full designated length. The regression graphs displayed the network outputs with targets for the training, validation, and test sets. The various speed level parameters for which the fit is reasonable for all data sets, with R values of 0.98426 in each case. The various traffic volume parameters for which the fit is reasonable for all data sets, with R values of 0.96758 in each case. The model's superiority is indicated by its low mean squared error values. This study demonstrated that the neural network has a good prediction effect on specific road traffic flow, which can achieve the goal of short-term prediction and has improved practicability through testing on real traffic data. This study provides an opportunity to provide a suitable alternative for short-term traffic flow forecasting in Khulna Metropolitan City with traffic flow conditions for two-lane undivided highways.

Forecast of Sugarcane Yield in Chongzuo, Guangxi—LSTM Model Based on Fusion of Trend Yield and Meteorological Yield

Purpose: This paper develops a high-precision yield fusion prediction model for the sugarcane industry in Chongzuo, Guangxi, based on the trend yield and meteorological yield using the long short-term memory (LSTM) model to cope with the multiple factors affecting sugarcane production. Decision support is provided to agricultural producers, policymakers, and supply chain managers so that they can plan resource allocation, market strategies, and policy directions more effectively. Methods: The paper modeled trend yield and weather yield separately to explore the complex relationship between the two in influencing sugarcane production. Trend yields were predicted using the exponential smoothing and multilayer perceptron (MLP) models, while meteorological yields were modeled using stepwise regression. The predicted yields were used again as input variables into the LSTM deep learning network to fit the nonlinear relationship between the two yields. Results: The results showed that (1) the fusion strategy of meteorological yield and MLP trend yield adopted by the model was superior to the fusion strategy of meteorological yield and exponentially smoothed trend yield, achieving a very low mean square error (MSE) of 0.011 and a goodness of fit as high as 0.979, which indicated that the model prediction was highly in agreement with the actual yield, confirming the validity of the method. (2) The prediction curve is basically consistent with the trend of actual sugarcane yield, which predicts that the sugarcane yield in Chongzuo, Guangxi, is expected to maintain a stable and small growth trend in the next eight years. (3) The fusion prediction model proposed in this study provides an accurate and practical solution for sugarcane yield prediction in Chongzuo, Guangxi, with the unique advantage of effectively analyzing and integrating the natural and socio-economic factors affecting the yield, which is of significant reference value for the prediction of sugarcane yield in the local area and even in similar ecoregions.

Comparative analysis of cloud properties over drought- and flood-prone regions of western India using machine learning techniques

ABSTRACT Cloud properties are pivotal in analyzing rainfall patterns globally, especially in monsoon-dependent countries such as India. The impact of climate change becomes more important in regions susceptible to hydrometeorological events due to different monsoon regimes. To examine regional heterogeneity of cloud properties, this study investigates long-term trends and predictive capabilities for cloud properties in drought-prone and flood-prone regions of western India, utilizing satellite data and employing various machine learning (ML) models to comprehend intricate data patterns and enhance predictive accuracy. The results show higher mean and variability in cloud parameters over the flood-prone area due to favorable rain conditions, reflecting higher cloud microphysical and optical properties. These parameters negatively correlate with some cloud macrophysical properties along with the aerosol property in the drought-prone area. Additionally, a moderate correlation exists between certain cloud characteristics of one region and another. Employing ML algorithms for regression analysis and comparing them for cloud effective radius across regions shows promising results, with random forest (RF) demonstrating high coefficient of determination (0.86, 0.93) and low root mean squared error (0.76, 1.15) due to its robustness and high accuracy. This research enhances the understanding of regional heterogeneity in India and shows that ML can be helpful in predicting future cloud dynamics and climate variables identifying the most suitable model.

Raman spectroscopy coupled with the PLSR model: A rapid method for analyzing gamma-oryzanol content in rice bran oil

Rice bran oil (RBO) is widely used in food, nutraceutical, and cosmetic industries, due to its γ-oryzanol content, a key quality indicator. This study developed a rapid, non-destructive method for quantifying γ-oryzanol in RBO using Raman spectroscopy combined with partial least squares regression (PLSR). The optimal PLSR model, based on orthogonal signal correction (OSC)-pretreated data of Raman spectra from 800 to 1800 cm−1, demonstrated high accuracy with a strong R2-Pearson correlation coefficient of 0.9827 and low root mean square error of prediction (RMSEP) of 0.5314. Principal component analysis (PCA) of OSC-pretreated data showed improved sample grouping by concentration of γ-oryzanol compared to untreated data. Additionally, Bland-Altman plots comparing results from Raman and HPLC methods showed random scatter within ±2 SD of the mean difference, confirming the method's reliability. This study indicates that Raman spectroscopy can serve as a reliable method for determining γ-oryzanol content in RBO products within the related industries.

Concurrent Validity and Interunit Reliability of 25-Hz GNSS Units for Profiling Sprinting Performance.

Dennison, L, Duthie, GM, Ehrmann, F, and Psarakis, MA. Concurrent validity and interunit reliability of 25-Hz GNSS units for profiling sprinting performance. J Strength Cond Res XX(X): 000-000, 2024-Profiling sprint performance by assessing within-race velocity or time is crucial for understanding an athlete's capabilities and identifying areas for improvement. While traditional gold standard systems provide valid and reliable measurements, they are often costly, laboratory-based, or impractical for field-based settings. New Global Navigation Satellite System (GNSS) units (25 Hz) with higher sampling rates may address some of these limitations. The purpose of the project was to evaluate the concurrent validity and interunit reliability of 25-Hz GNSS units by examining their agreement with laser devices and timing gates for velocity measurements and sprint/interval times. Concurrent validity was assessed during a real track and field training session. Thirty subjects each completed 3 to 6 maximal 40-meter sprints, for a total of 106 sprints, while being assessed simultaneously through all systems. Interunit reliability was assessed by placing 3 GNSS units on a motor vehicle and completing 60 accelerations from 0 to 60 m. Low mean bias (<1%) and typical error less than <2% for all measurements demonstrate excellent agreement between GNSS and criterion devices. The units demonstrated good reliability for the 0-10 m interval time (intraclass correlation coefficient [ICC] = 0.86), excellent reliability for all remaining 10 m to 60 m intervals (ICC = 0.91-0.99), sprint times 0-60 m (ICC = 0.97), and maximal sprint velocity (ICC = 1.0). These GNSS units provide an efficient and feasible alternative to traditional measurement devices. For coaches, this technology offers a scalable method to concurrently assess the sprint performances of multiple athletes during training and competitions, enabling evidence-based decisions to guide athletic development.

Modeling Root Development of Rice (Oryza sativa L) under Varying Drought Stress

In the current era, where drought occurrences are frequent and devastating, extensive study on drought tolerance mechanisms is imperative. Root growth under water stress is a key indicator of drought tolerance. Modeling a crop's root-length under such conditions aids in predicting seminal root length (SRL) and, consequently, its tolerance level. Under consecutive stress using Poly-ethylene-glycol (PEG), root length of a landrace originating from the drought-prone area of West Bengal, India was measured through image analysis and then mathematically modulated using an equation. The highest root length under stress was predicted at 6% PEG stress which aligned with the experimental readings. On the 15th day of stress, predicted SRL values were 104.67mm and experimental values were 90.00mm. Despite some over and underestimations with a low root mean square error of 14.68 mm, the equation provides insight into root elongation trends under various stress levels, offering a basis for predicting SRL and yield capabilities under water stress for diverse crop species.

Three-dimensional dynamics of unstable lean premixed hydrogen-air flames: Intrinsic instabilities and morphological characteristics

The 3D swinging laser sheet technique was employed to study the development and morphological characteristics of premixed hydrogen-air unstable flames in a spherical explosion vessel. Pressure dependencies for laminar flame propagation were sought to exploit the role of the Darrieus-Landau (DL) and Thermal-diffusive (TD) instabilities in the unstable self-accelerating flame regime. A sufficiently low Markstein number, as a consequence of the increased pressure, leads to more cracking and smaller cells over the flame surface. The degree of wrinkling on the flame surface is proportional to the increase in flame burning velocity, a relationship that holds true for low pressures but is not applicable under high pressures. External turbulence can significantly alter the extent of flame surface wrinkling even at low root mean square velocities, producing a more wrinkled flame surface compared to intrinsic cellularity, and distinctly affecting flame dynamics. The increased wrinkling and flame speed due to external turbulence can be attributed to the synergistic effects between thermo-diffusive instabilities and turbulence, resulting in higher fuel consumption rates per flame surface area and the formation of finger-like structures that enhance flame displacement speed in curved segments. The parameters, ϵ, deviation of the Lewis number from a critical value, and ω2, obtained through classical linear stability analysis, display a clear linear relationship with the ratio of the wrinkled surface area observed in planar flames. This study enhances the understanding of hydrogen flame instabilities, which is crucial for preventing explosions in hydrogen storage and utilization, and provides valuable insights into flame dynamics, supporting the design of safer and more efficient hydrogen-fueled engines and turbines.

Revealing the optimal strategy for tree plantations on controlling soil erosion in China: A meta-analysis

Tree plantation has increased globally, driven by a desire to supply multiple ecosystem services including soil erosion control. However, how to plant trees can more effectively mitigate soil erosion still lacks systematic investigation. Therefore, we conducted a meta-analysis consisting of 412 paired observations from 80 studies to rigorously compare the effects of tree plantations on soil erosion control across different plantation approaches in China. The results showed that plantation age and ground cover type were the primary factors influencing the efficiency of tree plantations on runoff and sediment reduction. Mature plantations (>20 years) showed more efficiency in runoff (80.9 %) and sediment (97.2 %) reduction than young plantations (<10 years). The benefit of tree plantations with grass cover on runoff reduction was 70 %, while the capacity of plantations without ground cover was not significant. In addition, tree plantations in areas with steep slope, low mean annual precipitation, high soil sand content, and low soil organic carbon had better benefits on soil erosion control. Loess Plateau and Karst areas suffer the most severe water erosion in China, with distinct environmental conditions. This paper further revealed the targeted plantation strategy for these two regions: tree plantations in Karst areas should focus on the lithophyticity and drought tolerance of tree species, in the Loess Plateau should consider the trade-off between water consumption and soil erosion control.

Predictive Modeling for Enhancing MOOC Completion Rates: A Case Study

In the realm of online asynchronous learning platforms, accurately tracking student performance to predictcourse completion times poses a significant challenge. Completion rates for MOOCs are typically low, with abias towards participants with higher education levels. Understanding factors such as student motivation,engagement, participation, and learning pathway design is crucial for improving student outcomes in onlinecourses. This research developed a predictive framework utilizing advanced deep learning techniques toaccurately forecast course completion times for participants enrolled in an introductory programming course(&amp;quot;Python for Beginners&amp;quot; course on the Open Learning Platform of University of Moratuwa Sri Lanka). Byaccurately tracking student performance and leveraging a diverse dataset encompassing demographic andeducational variables, the research seeks to identify factors influencing course completion and predictindividual student outcomes. By utilising deep learning techniques, the prediction performance of the modelwill be improved, ultimately contributing to a more precise forecast of course completion times forparticipants. Evaluation of the model resulted in low Mean Absolute Error (MAE) of 0.0080 and low MeanSquared Error (MSE) of 0.0033 which promises the effectiveness of the developed method in accuratelypredicting course completion times for students. The findings of this study may help increase the successfulcompletion rate of such courses which are delivered in the online asynchronous mode. The study employedadvanced deep learning models optimized through Bayesian methods, highlighting the potential of thesetechniques to enhance MOOC completion rates by offering precise forecasts and actionable insights intostudent engagement. The comprehensive analysis revealed that variables such as &amp;#39;Current_Lesson&amp;#39;, &amp;#39;SessionTime Category&amp;#39;, and &amp;#39;District_Score&amp;#39; significantly influence completion times. The robust methodologicalframework, including feature engineering, model training, and hyperparameter optimization, sets a precedentfor future research in the field. This research contributes to educational data mining and predictive analytics,offering a scalable approach to improving completion rates and educational outcomes across various onlinelearning platforms. Future research should explore incorporating real-time data and longitudinal studies toenhance model accuracy and generalizability. Additionally, addressing potential biases in the dataset, such asdemographic, prior knowledge, and resource access disparities, is essential to ensure the fair and equitableapplication of the model across diverse student populations. Expanding the research to include a wider rangeof courses and institutions will further validate the model&amp;#39;s robustness and applicability in differenteducational contexts.

Graphical Analysis of Multi-environmental Trials for Bread Wheat (Triticum aestivum L.) Grain Yield Based on GGE Bi-Plot Analysis

Bread wheat (Triticum aestivum L.) is a crucial crop in Ethiopia, and breeders test newly developed elite lines for superiority to existing cultivars to boost national productivity. The study was undertaken during the 2021–22 to 2022–23 cropping seasons at seven environments in optimum moisture areas of Ethiopian using 36 diverse and advanced bread wheat genotypes to evaluate the GEI by the graphical method of GGE biplot and to identify the genotypes with high mean yield performance and stability. Field experiments were conducted at the Adet, Asasa, Kulumsa, and Sinana research centers in Ethiopia. The experiments were planted in an alpha lattice design replicated three times in six rows of 2.5m long. Row-to-row distance and distance between blocks were 0.2m and 1.5m, respectively. The analysis of variance revealed that genotype, environment, and their interaction showed a highly significant effect on the yield as reflected in the GGE model and the GGE model indicated the suitability of the genotypes EBW202136 (33), Boru (1), and EBW202172 (12), with high mean yield and stability, whereas the genotypes EBW202185 (16) and Deka (36) produced high mean yield, but unstable. Likewise, the genotypes EBW202164 (27) and EBW202192 (29) produced low mean yield and unstable. The AMMI analysis of variance for grain yield across the environments showed that 17.26% of the total variation was attributed to genotypic effects, 64.03% to environmental effects, and 18.71% to GEI effects. Two mega environments were identified based on GGE biplot analysis and the which-won–model indicated the adaptation of genotypes Boru (1), EBW202159 (4), EBW202172 (12), EBW202171 (19), and EBW202136 (33) to first mega-environment and genotypes EBW202157 (3), EBW202166 (5), EBW202160 (6), EBW202162 (9), EBW202185 (16), Dursa (17) and Deka (36) in the second. These approaches allowed the identification of stable and high-yielding genotypes (EBW202136 (33) and EBW202172 (12)) which can be included in the national verification program, with a plan to release a new variety, and other genotypes with high yield could be utilized in breeding programs to further improve grain yield in bread wheat.

An experimental investigation to predict the compressive strength of lightweight Ceramsite aggregate UHPC using boosting and bagging techniques

Ceramsite aggregate concrete is renowned for its lightweight properties but often exhibits low compressive strength (CS), limiting its structural applications. This study introduces ultra-high-performance powder into Ceramsite aggregate concrete to enhance its CS while maintaining low density. Achieving optimal CS for this composite material requires a carefully designed mix. In this research, advanced machine learning (ML) techniques, including random forest, extreme gradient boosting, categorical boosting, light gradient boosting machine (LightGBM), natural gradient boosting (NGBoost), and adaptive boosting, were employed to predict the CS of lightweight Ceramsite aggregate UHPC (LWCA-UHPC). The study utilized 189 datasets from the authors' own experimental results for training and testing the models. The results demonstrated that NGBoost and LightGBM outperformed other models, with R² values of 0.9614 and 0.9645, respectively, in the testing phase. The models also showed low errors, with NGBoost having a mean absolute error (MAE) of 2.0624 and a root mean squared error (RMSE) of 2.9140, while LightGBM recorded an MAE of 2.0161 and an RMSE of 2.7778. Moreover, feature importance and Shapley additive explanations (SHAP) analyses revealed that the most influential factors affecting the CS of LWCA-UHPC were cement content, density, sand, and Ceramsite aggregate content, consistent with experimental findings. Additionally, a graphical user interface was developed to integrate the hybrid ML model into a user-friendly desktop tool, achieving an R² value of 0.9671 with a low mean squared error of 3.5204 when tested against experimental datasets. This tool not only enables practical applications in engineering but also helps reduce the need for extensive experimental work by providing accurate predictions of CS.

Serum 25 Hydroxy – D in Patients without Vitamin D Supplementation: Perspectives from Cohorts with Rheumatoid Arthritis and Systemic Lupus Erythematosus

Hypovitaminosis D is common in the sunny UAE. This study focusses on the perspectives of 25 hydroxy-D in patients with RA and others with SLE ((Autoimmune disease, ADs)) and to compare them to others of patients without ADs) .The latter were used as control group. All included individuals were without history of vitamin D intake. Hypovitaminosis D was common in the 3 groups (RA, SLE and controls). However, the prevalence of hypovitaminosis D in patients with each of ADs was significantly lower to that of the control group yet with higher means of 25 (OH) D levels. These findings were in contrast to those of number of regional and international studies conducted earlier. Data on the high prevalence of hypovitaminosis D and the low mean values of 25 (OH) D were comparable between the two groups of ADs. Overall, the results of this study may lead to some controversy or perhaps a challenge to the widespread understanding of the contrary as per previous international and regional studies. They also argue further the concept of specific association of hypovitaminosis D and autoimmune disease and certainly encourage wider estimation of 25(OH) D among larger numbers of patients in general medical practice. Finally, the outcomes of this work deserve further study/ies in larger groups in the future.

Analysis of Mannose-Binding Lectin Protein and mRNA Levels on Selected Chicken Breeds in South Africa.

Mannose-binding lectin (MBL) is a key component of the innate immune system that plays a crucial role in binding to the microbial sugar surface to recognize and eliminate pathogens by activating the complement system. To detect and quantify the MBL protein concentration and chicken MBL expression in selected chicken breeds in South Africa. Forty-five blood samples from three indigenous chicken breeds, Ovambo (OV=9), Venda (VD=9) and Potchefstroom Koekoek (PK=9), and two exotic chicken breeds, Rhode Island Red (RIR=9) and Lohmann Brown (LB=9), were used for MBL protein concentration using enzyme-linked immunosorbent assay (ELISA) techniques. Also 20 liver samples from symptomatic two indigenous chicken breeds, OV (5) and PK (5), and two exotic chicken breeds, RIR (5) and LB (5), were used for MBL expression using quantitative polymerase chain reaction (qPCR) techniques. A general linear model was done using Tukey's multiple comparison post hoc test. The findings revealed MBL protein concentration from 5.26 to 18.56µg/mL. The LB breed had the lowest mean 6.40±0.80µg/mL, whereas the PK breed had the highest mean MBL concentration of 17.70±0.24µg/mL of MBL protein concentration. At 12, 25 and 35 weeks, the MBL proteins of OV, VD, PK, RIR and LB varied significantly at p≤0.05. The mRNA MBL expression of OV and LB breeds showed a 1-fold decrease in MBL expression, while RIR showed a 2-fold increase in MBL expression, and the PK showed more than a 3-fold increase in MBL expression relative to the control. The least-squares means for OV, LB, PK and RIR mRNA MBL expression were 0.54±0.19, 0.68±0.30, 4.46±2.76 and 2.89±0.19µg/mL, respectively. MBL protein was detected and quantified with distinct differences in concentration and expression levels at the presence of mycoplasma gallisepticum among the sampled South African chicken breeds. This highlights the genetic diversity of MBL as a tool for disease prevention in South African chicken breeds.