High Correlation Research Articles

Personality determinants, love attitudes and self-efficacy in people using dating sites

The Internet can provide an alternative space to the real world. Among these opportunities is making new acquaintances with the intention of creating a romantic relationship via portals designed for this purpose. With the growing popularity of dating sites, it was decided to study the relationship between personality determinants (independent and interdependent self-construal, communion and agency), love attitudes, attachment styles and self-efficacy in a group of people using dating sites. The study included 350 respondents. Dating site users comprised 39% of the sample (135 respondents), whereas non-users accounted for 61% of the sample (215 respondents). The following tools were used in the study: Self-Construal Scale (SCS), Self-Description Questionnaire 30, The Love Attitudes Scale: Short Form (LAS: SF), Attachment Styles Questionnaire (KSP) and General Self-Efficacy Scale (GSES). The results of the correlation analyses conducted for the entire study sample showed the existence of relationships between independent self-construal, agency, communion, Ludus love attitude, Mania love attitude, secure attachment style, anxiety-ambivalent attachment style and avoidant attachment style and self-efficacy, while the linear regression analysis showed the significance of independent self-construal, agency, Ludus love attitude and anxiety-ambivalent attachment style for self-efficacy. By comparing the obtained correlation and regression results, intergroup differences were shown for the variables studied. Differences in the correlations of the independent variables with the dependent variable between the control group and the research group are found for the relationships of communion and avoidant attachment style. In the experimental group, higher correlation coefficients were observed for the relationship with self-efficacy for the independent self-construal and communion. The level of explanation of the dependent variable by the independent variables is 43% for non-users of dating sites, while it is 36% for users of dating sites. In the experimental group, the predictors were statistically significant in explaining the variance of the dependent variable: independent self-construal, agency, communion, Ludus love style, Agape love style, anxiety-ambivalent attachment style. In the control group: agency and anxiety-ambivalent attachment style. The results of the Mann-Whitney U test showed statistically significant differences between the groups with high median differences observed for anxiety-ambivalent attachment style and avoidant attachment style.

The daily minimum leaf turgor pressure can represent the water status of apple trees under drip irrigation

IntroductionAccurate diagnosis of the water status of fruit trees is a prerequisite for precise irrigation. Measurement of leaf turgor pressure provides a means to explore the water utilization mechanisms of fruit trees and their responses to water stress. However, there are few studies on the use of daily minimum leaf turgor pressure (Ppmax) to indicate water information in apple tree.MethodsWe monitored Ppmax, stem flow rate (Vstem), leaf water potential, soil water, and the main meteorological factors under two drip irrigation levels (fully irrigated and under-irrigated) to elucidate the temporal dynamics of the daily leaf turgor minimum of apple trees and diagnose the water status of fruit trees.ResultsThe results showed that soil water deficiency could reduce leaf turgor pressure and increase Ppmax. In both years, the signal intensity and sensitivity of Ppmax were better than those of Vstem, and the sensitivity was 3.31 and 2.94 times of Vstem, respectively. Compared to Ppmax, Vstem had a higher correlation with meteorological factors and was more affected by meteorological conditions, independent of irrigation treatment. Ppmax was significantly negatively correlated with soil and leaf water potential, and its correlation coefficient was 2.58–4.89 times higher than that between Vstem and both.DiscussionThese results not only enhance our understanding of the water regulation strategies employed by apple trees under drought conditions but also provide a theoretical foundation for developing efficient water-saving practices and precision irrigation strategies for fruit trees.

Improving Daily Precipitation Estimates by Merging Satellite and Reanalysis Data in Northeast China

Precipitation plays a key control in the water, energy, and carbon cycles, and it is also an important driving force for land surface modeling. This study provides an optimal least squares merging approach to merge precipitation data sets from multiple sources for an accurate daily precipitation estimate in Northeast China (NEC). Precipitation estimates from satellite-based IMERG and SM2RAIN-ASCAT, as well as reanalysis data from MERRA-2, were used in this study. The triple collocation (TC) approach was used to quantify the error uncertainties in each input data set, which are associated with the weights assigned to each data set in the merging procedure. The results revealed that IMERG provides a better consistency with the other two input data sets and thus was more relied on during the merging process. The accuracy of both SM2RAIN-ASCAT and MERRA-2 showed obvious spatio-temporal patterns due to their retrieval algorithms and resolution limits. The merged TC-based daily precipitation provides the highest correlation coefficient with ground-based measurements (R = 0.52), suggesting its capability to represent the temporal variation in daily precipitation. However, it largely overestimated the precipitation intensity in the summer, leading to a large positive bias.

Validating CYGNSS Wind Speeds with Surface-Based Observations and Triple Collocation Analysis

Existing validation of mean wind speed estimates via reflectometry from global navigation systems of satellites (GNSS-R)—has been largely limited in spatial coverage to equatorial buoys or tropical cyclone events near continental United States. Two alternative validation techniques are presented for the Cyclone GNSS (CYGNSS) mission using surface-based observations along coasts and coral reefs instead of buoys, and triple collocation analysis (TCA) instead of a 1:1 gridded comparison for tropical cyclone (TC) events. For the surface-based analysis, Fully Developed Seas (FDS) v3.2 and NOAA v1.2 were compared to anemometer data provided by the Australian Bureau of Meteorology across the Australia and Pacific regions. Overall, the products performed similarly to previous studies with NOAA having higher correlations and lower errors than FDS, though FDS performed better than NOAA over the Australian dataset for high wind speed events. TCA was used to validate NOAA v1.2 and Merged v3.2 datasets with other satellite remotely sensed products from the Soil Moisture Active Passive (SMAP) mission and Synthetic Aperture Radar (SAR). Both additive and multiplicative error models for TCA were applied. The performance overall was similar between the two products, with NOAA producing higher errors. NOAA performed better than Merged for mean winds above 17 m/s as the large temporal averaging reduced sensitivity to high winds. For SMAP winds above 17 m/s, NOAA’s average bias (−2.1 m/s) was significantly smaller than the average bias in Merged (−4.4 m/s). Future ideas for rapid intensification detection and constellation design are discussed.

Surviving COVID-19 and Battling Fibrosis: A Retrospective Cohort Study Across Three Pandemic Waves

Background/Objectives: We aimed to characterize the fibrosis following COVID-19 pneumonia, using quantitative analysis, after three months and subsequently, after two years of patients’ release from the hospital, and to identify the risk factors for pulmonary fibrosis. Methods: We performed a retrospective, observational cohort study on 420 patients with severe forms of COVID-19. For all patients, we registered demographic, inflammatory and biochemical parameters, complete blood count and D-dimers; all patients underwent three computed tomography scans (at admittance, at 3 months and at 2 years). Results: We found fibrosis in 67.9% of patients at the 3-month evaluation and in 42.4% of patients at the 2-year evaluation, registering a significant decrease in the severe and moderate fibrosis cases, with a slight increase in the mild fibrosis cases. The risk of fibrosis was found to be proportional to the values of age, duration of hospital stay, inflammatory markers (ESR, fibrinogen), cytolytic markers (LDH, AST) and D-dimers. The highest correlations with lung fibrosis were registered for interstitial pulmonary involvement (for the 3-month evaluation) and total pulmonary involvement (for the 2-year evaluation). Conclusions: Lung fibrosis represents a significant post-COVID-19 complication found in 42% of patients with severe forms of pneumonia at the 2-year evaluation. A significant overall decrease in the severity of lung fibrosis was registered at the 2-year evaluation compared to the 3-month evaluation. We consider that the amount of interstitial pulmonary involvement represents the optimal parameter to estimate the risk of lung fibrosis following SARS-CoV-2 pneumonia.

Decoding continuous motion trajectories of upper limb from EEG signals based on feature selection and nonlinear methods.

Brain-computer interface (BCI) system has emerged as a promising technology that provides direct communication and control between the human brain and external devices. Among the various applications of BCI, limb motion decoding has gained significant attention due to its potential for patients with motor impairment to regain independence and improve their quality of life. However, the reconstruction of continuous motion trajectories in BCI systems based on electroencephalography (EEG) signals remains a challenge in practical life. This study investigates the feasibility of applying feature selection and nonlinear regression for decoding motion trajectory from EEG. We propose to fix the time window, select the optimal feature set, and reconstruct the motion trajectory of motor execution tasks using polynomial regression. The proposed approach is validated on a public dataset consisting of EEG and hand position data recorded from 15 subjects. Several methods including ridge regression and multiple linear regression are employed as comparisons. The cross-validation results show that the proposed reconstructed method has the highest correlation with actual motion trajectories, with an average value of 0.511±0.019 (p<0.05). This finding demonstrates the great potential of our approach for real-world motor kinematics BCI applications.

VME-EFD : A novel framework to eliminate the Electrooculogram artifact from single-channel EEGs.

The diagnosis of neurological disorders often involves analyzing EEG data, which can be contaminated by artifacts from eye movements or blinking (EOG). To improve the accuracy of EEG-based analysis, we propose a novel framework, VME-EFD, which combines Variational Mode Extraction (VME) and Empirical Fourier Decomposition (EFD) for effective EOG artifact removal. In this approach, the EEG signal is first decomposed by VME into two segments: the desired EEG signal and the EOG artifact. The EOG component is further processed by EFD, where decomposition levels are analyzed based on energy and skewness. The level with the highest energy and skewness, corresponding to the artifact, is discarded, while the remaining levels are reintegrated with the desired EEG.&#xD;Simulations on both synthetic and real EEG datasets demonstrate that VME-EFD outperforms existing methods, with lower RRMSE (0.1358 vs. 0.1557, 0.1823, 0.2079, 0.2748), lower ΔPSD in the α band (0.10±0.01 and 0.17±0.04 vs. 0.89±0.91 and 0.22±0.19, 1.32±0.23 and 1.10±0.07, 2.86±1.30 and 1.19±0.07, 3.96±0.56 and 2.42±2.48), and higher correlation coefficient (CC: 0.9732 vs. 0.9695, 0.9514, 0.8994, 0.8730). The framework effectively removes EOG artifacts and preserves critical EEG features, particularly in the α band, making it highly suitable for brain-computer interface (BCI) applications.&#xD.

Kinetics of Non-exchangeable Potassium Release in Rice-groundnut Cropping System under Alluvial Soils of Odisha, India

Non exchangeable K release can very much influence soil K fertility. The experiment was carried out to investigate the amount of non-exchangeable potassium released from Inceptisols with rice-groundnut cropping system and to use various kinetic equations to describe the release. Release rates of non-exchangeable potassium were determined for five different soils collected from rice-groundnut cropping system fertilized @ 0 and 40 kg K ha-1 over three years. The non-exch K release rate was studied by successive extraction with 0.01M CaCl2 methods. The results show that the proportion of exch.K and non-exch.K to total K varied between 2.76 to 4.95% and 8.39 to 9.95%, respectively. Per cent of clay or silt+clay correlated significantly with water soluble, exch. and non-exch. K. Five mathematical models (elovich equation, power function, parabolic diffusion, first and zero order equation) were used to describe cumulative K release pattern. The elovich and parabolic equation described the K release kinetics satisfactory as evidenced by the highest correlation coefficients and lowest values of standard error of estimates (SEE). The release of Step-K and CR-K was higher in state recommend dose (SRD) than K control which concluded that the SRD of K (40 kgha-1) is adequate to sustain the productivity of intensive rice-ground nut cropping system in alluvial soils of Odisha.

Optimising Venturi flume oxygen transfer efficiency using uncertainty-aware decision trees

ABSTRACT This study optimizes standard oxygen transfer efficiency (SOTE) in Venturi flumes investigating the impact of key parameters such as discharge per unit width (q), throat width (W), throat length (F), upstream entrance width (E), and gauge readings (Ha and Hb). To achieve this, a comprehensive experimental dataset was analyzed using multiple linear regression (MLR), multiple nonlinear regression (MNLR), gradient boosting machine (GBM), extreme gradient boosting (XRT), random forest (RF), M5 (Pruned and Unpruned), random tree (RT), and reduced error pruning (REP). Model performance was evaluated based on key metrics: correlation coefficient (CC), root mean square error (RMSE), and mean absolute error (MAE). Among the proposed models, M5_Unprun emerged as the top performer, exhibiting the highest CC (0.9455), the lowest RMSE (0.1918), and the lowest MAE (0.0030). GBM followed closely with a CC value of 0.9372, an RMSE value of 0.2067, and an MAE value of 0.0006. Uncertainty analysis further solidified the superior performance of M5_Unpruned (0.7522) and GBM (0.8055), with narrower prediction bands compared to other models, including MLR, which exhibited the widest band (1.4320). One-way analysis of variance confirmed the reliability and robustness of the proposed models. Sensitivity, correlation, and SHapley Additive exPlanations analyses identified W and Hb as the most influencing factors.

Tuning the Localized Microenvironment near a Continuous Glucose Meter to Ensure Monitoring Accuracy and Longevity by Plasma-Induced Grafting Zwitterionic Brushes.

Diabetes mellitus is a metabolic disorder that affects millions of individuals worldwide. Continuous glucose monitoring (CGM) offers a prevalent method for continuously monitoring interstitial glucose levels instead of traditional self-monitoring of blood glucose (BG), eliminating the need for finger pricking and providing only discrete data. However, challenges in accuracy persist in CGM, including substantial noise interference and tissue fluid erosion, as well as the pH fluctuations in the localized ISF microenvironment during acute inflammation periods. Herein, we reported a facile atmospheric plasma-induced grafting technique to surface functionalize a zwitterionic brush coating on the sensor, with the aim to adjust the sensor's microenvironmental chemistry. The zwitterionic brush-coated CGM (Z-coated CGM) could regulate pH values with a good glucose response in the pH range from 6.2 to 7.6 and a prolonged sensor life over the uncoated sensor. We evaluated the rat practice that the Z-coated CGM consistently outperformed the uncoated in tracking BG fluctuations, with higher correlation coefficients and significant noise reduction for both non-recalibration and recalibration. This technology holds substantial implications for subcutaneous embedded glucose monitors and facilitates CGMs in achieving independence from routine BG fingerstick calibrations.

Deep learning-based body composition analysis from whole-body magnetic resonance imaging to predict all-cause mortality in a large western population

Manually extracted imaging-based body composition measures from a single-slice area (A) have shown associations with clinical outcomes in patients with cardiometabolic disease and cancer. With advances in artificial intelligence, fully automated volumetric (V) segmentation approaches are now possible, but it is unknown whether these measures carry prognostic value to predict mortality in the general population. Here, we developed and tested a deep learning framework to automatically quantify volumetric body composition measures from whole-body magnetic resonance imaging (MRI) and investigated their prognostic value to predict mortality in a large Western population. The framework was developed using data from two large Western European population-based cohort studies, the UK Biobank (UKBB) and the German National Cohort (NAKO). Body composition was defined as (i) subcutaneous adipose tissue (SAT), (ii) visceral adipose tissue (VAT), (iii) skeletal muscle (SM), SM fat fraction (SMFF), and (iv) intramuscular adipose tissue (IMAT). The prognostic value of the body composition measures was assessed in the UKBB using Cox regression analysis. Additionally, we extracted body composition areas for every level of the thoracic and lumbar spine (i) to compare the proposed volumetric whole-body approach to the currently established single-slice area approach on the height of the L3 vertebra and (ii) to investigate the correlation between volumetric and single slice area body composition measures on the level of each vertebral body. In 36,317 UKBB participants (mean age 65.1±7.8 years, age range 45-84 years; 51.7% female; 1.7% [634/36,471] all-cause deaths; median follow-up 4.8 years), Cox regression revealed an independent association between VSM (adjusted hazard ratio [aHR]: 0.88, 95% confidence interval [CI] [0.81-0.91], p=0.00023), VSMFF (aHR: 1.06, 95% CI [1.02-1.10], p=0.0043), and VIMAT (aHR: 1.19, 95% CI [1.05-1.35], p=0.0056) and mortality after adjustment for demographics (age, sex, BMI, race) and cardiometabolic risk factors (alcohol consumption, smoking status, hypertension, diabetes, history of cancer, blood serum markers). This association was attenuated when using traditional single-slice area measures. Highest correlation coefficients (R) between volumetric and single-slice area body composition measures were located at vertebra L5 for SAT (R=0.820) and SMFF (R=0.947), at L3 for VAT (R=0.892), SM (R=0.944), and at L4 for IMAT (R=0.546) (all p<0.0001). A similar pattern was found in 23,725 NAKO participants (mean age 53.9±8.3 years, age range 40-75; 44.9% female). Automated volumetric body composition assessment from whole-body MRI predicted mortality in a large Western population beyond traditional clinical risk factors. Single slice areas were highly correlated with volumetric body composition measures but their association with mortality attenuated after multivariable adjustment. As volumetric body composition measures are increasingly accessible using automated techniques, identifying high-risk individuals may help to improve personalised prevention and lifestyle interventions. This project was conducted using data from the German National Cohort (NAKO) (www.nako.de). The NAKO is funded by the Federal Ministry of Education and Research (BMBF) [project funding reference numbers: 01ER1301A/B/C, 01ER1511D, and 01ER1801A/B/C/D], federal states of Germany and the Helmholtz Association, the participating universities and the institutes of the Leibniz Association. This research has been conducted using the UK Biobank Resource under Application Number 80337. MJ was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)-518480401. VKR was funded by American Heart Association Career Development Award 935176 and National Heart, Lung, and Blood Institute-K01HL168231.

Automated and quantitative assessment of aortic root based on cardiac computed tomography angiography using a new deep-learning tool: a comparison study.

Accurate assessment of aortic root is crucial for the preprocedural planning of transcatheter aortic valve replacement (TAVR). A variety software is emerging for the semiautomated or automated measurements during TAVR planning. This study evaluated a new deep-learning (DL) tool based on cardiac computed tomography angiography (CCTA) for fully automatic assessment of aortic root. The study included 126 patients with CCTA, 63 of whom underwent TAVR. In the overall population, the DL method was compared to manual measurements of the annulus dimensions. Within the TAVR group, the DL method was also compared to 3mensio software-derived aortic root measure, including the annulus, left ventricular outflow tract (LVOT), sinotubular junction (STJ), ascending aorta (AAo), and the heights of both the coronary ostia. Data were successfully analyzed using the DL method in 122 (96.8%) of patients. The correlation of annular diameters between the DL and manual methods was good to excellent for the overall cohort (n=118; r=0.83), the TAVR group (n=59, r=0.86), and its subgroups [bicuspid aortic valve (BAV): n=12, r=0.74; tricuspid aortic valve (TAV): n=47, r=0.93]. In the comparison of the DL method with 3mensio, the highest correlation was found for AAo (r=0.99). Among the four diameter indices [minimum, maximum, perimeter-derived diameter (pDD), and area-derived diameter (aDD)], excellent correlation was observed for aDD (LVOT: r=0.92; annulus: r=0.89). The DL method offers an effective and efficient tool for the quantification of aortic roots for TAVR planning.

Unveiling Immunotherapy Evasion in Lung Cancer: The Role of Fanconi Anemia and Stemness Genes in Shaping an Immunosuppressive Microenvironment.

The study aimed to investigate the fanconi anemia (FA)-related and stemness-related genes in lung cancer (LC) patients. Firstly, we identified stemness-related genes through weighted gene co-expression network analysis combined with TCGA database. Further combined stemness-related genes with FA-related genes to screen for prognostic-related genes. Risk score was constructed from the screened genes and comprehensive bioinformatics analyses were performed. Finally, single-cell data and in vitro experiment were used to validate our results. We screened a total of eight genes to construct a risk score. The risk score was an independent prognostic factor for LC. The validation results of multiple GEO databases were consistent with our results. Functional and pathway enrichment analysis showed that risk score was associated with cell cycle, DNA replication, DNA damage repair, and immune-related pathways. The results showed to be related to the stem cell self-renewal and proliferation. Besides, we also found that patients with higher risk scores had lower immune activity and function, and the effectiveness of immunotherapy might be poorer, with a higher rate of immune escape. Finally, our results revealed that SLC2A1 had the highest correlation with B cells in single-cell data analysis, and we validated its correlation with B cells and its expression with FA-related genes, tumor invasiveness, stemness, and drug sensitivity. Our research constructed a risk score based on FA-related and tumor stemness-related specific genes. In addition to accurately predicting the prognosis of patients with LC, the risk score may also serve as an innovative and viable predictor of immunotherapy response.

Optimal intensity measures and probabilistic fragility assessment for the long-span aqueduct structure with four-column bents

The intensity measure (IM) is a key factor in determining the accuracy of seismic performance assessment, which can represent the power of ground motion. However, the optimal IMs selection for long-span aqueduct structures with four-column bents has not been investigated in the previous study. This study aims to evaluate the optimal IMs for use in probabilistic seismic demand model (PSDM) and propose the scalar- and vector-valued fragility methods of the long-span aqueduct structure. To achieve this goal, taking the long-span aqueduct structure with four-column bents in the Central Yunnan Water Diversion Project in Southwest China as a typical case, a series of nonlinear dynamic time history analysis are conducted. Then, the 21 commonly-used IMs are tested and evaluated based on the different metrics (e.g. correlation, efficiency, practicality and proficiency). The optimal vector-valued IMs for the fragility analysis of the long-span aqueduct structure with four-column bents is proposed. Meanwhile, the PSDMs of optimal vector-valued IMs are also established and compared. Finally, the scalar- and vector-valued fragility functions are developed in terms of the optimal IMs. A method for seismic fragility analysis of the long-span aqueduct structure with four-column bents based on vector-valued IMs is proposed. The numerical results reveal that peak ground acceleration (PGA), peak pseudo acceleration spectrum (PSA), root-mean-square of acceleration (Arms) and characteristic intensity (Ic) are relatively appropriate IMs for seismic performance evaluation of the long-span aqueduct structure with four-column bents. In particular, PGA is considered to be the optimal IM due to its highest correlation, better efficiency, practicality and proficiency. The scalar-valued fragility curves can only describe the impact of the single IM on the damage probability of the aqueduct structure, which may overestimate or underestimate its damage probability. Furthermore, the vector-valued IMs can significantly increase the fitting ability of the PSDMs. The fragility surfaces are superior to scalar-valued fragility curve due to the vector-valued IMs can more accurately describe the ground motion information. In summary, the findings of this study highlight the significance of proposing the optimal IMs and developing vector-valued fragility surfaces when evaluating the seismic performance of the long-span aqueduct structure with four-column bents.

Exploring the Relationships of Learners and Instructors in Online Courses

In higher education, motivational factors are considered one of “the strongest predictors of academic performance” (Honike et al., 2020, p. 1). A meta-analysis of face-to-face (f2f) courses (Richardson et al., 2012) supports these claims, finding a strong correlation between performance self-efficacy and academic performance (r = 0.59), as well as accounting for 14% of the variation in academic performance using locus of control, performance self-efficacy, and grade goal as predictors. These f2f results are compelling enough that self-efficacy is often used synonymously with online learning in primary research. However, the results of prior f2f meta-analytic reviews have yet to be extended to online and blended learning contexts. We explored student motivation, specifically subscales for attributional style, self-efficacy, achievement goal orientation, self-determination and task value in relation to student academic performance. Informed by 94 outcomes from 52 studies, our results diverge from f2f findings. The highest correlation was mastery avoidance goals (r = 0.22) and academic self-efficacy (r = 0.19) was substantially lower than f2f findings (r = 0.31; r = 0.59) in Richardson et al., (2012). Using a parsimonious model (i.e., delivery mode, learning self-efficacy, and mastery approach goals), students’ average academic performance failed to identify significantly significant predictors. These results call into question the assumption that student motivation is a strong predictor of academic performance in online and blended courses. The lack of strong relationships and the lack of predictive power hold clear implications for researchers, practitioners, and policymakers that assume these relationships are stronger.

Urban sprawl and its effects on water competition between building industry and residents: Evidence from 31 provinces in China

With accelerated urban sprawl, water scarcity and increased water stress have become global issues. Water of building industry and domestic water are two major contributors to total water consumption that cannot be ignored. This paper seeks to emphasize the effects of urban sprawl on water competition between building industry and residents. To achieve this objective, a framework for quantifying the water usage of the life cycle of the building was first constructed. Additionally, the degree of urban sprawl and water competition between domestic and building industry was computed. This study characterized the evolution path of urban sprawl and water competition using standard deviation ellipse. Spatial econometric models were used to determine the impact of urban sprawl on water competition. The results indicate that there is a high spatial correlation between urban sprawl and water competition. The center of gravity of China's urban sprawl is consistent with the north-to-south movement of water competition. As economic sprawl increases by 1%, water competition will rise by 0.440%. As the population expands by 1%, the water competition index shifts by 0.018%. Moreover, 0.017% more water was used for every 1% increase in land sprawl.

Predicting Antitumor Activity of Anthrapyrazole Derivatives using Advanced Machine Learning Techniques.

Anthrapyrazoles are a new class of antitumor agents and successors to anthracyclines possessing a broad range of antitumor activity in various model tumors. The present study introduces novel QSAR models for the prediction of antitumor activity of anthrapyrazole analogues. The predictive performance of four machine learning algorithms, namely artificial neural networks, boosted trees, multivariate adaptive regression splines, and random forest, was studied in terms of variation of the observed and predicted data, internal validation, predictability, precision, and accuracy. ANN and boosted trees algorithms met the validation criteria. It means that these procedures may be able to forecast the anticancer effects of the anthrapyrazoles studied. Evaluation of validation metrics, calculated for each approach, indicated the artificial neural network (ANN) procedure as the algorithm of choice, especially with regard to the obtained predictability as well as the lowest value of mean absolute error. The designed multilayer perceptron (MLP)-15-7-1 network displayed a high correlation between the predicted and the experimental pIC50 value for the training, test, and validation set. A conducted sensitivity analysis enabled an indication of the most important structural features of the studied activity. The ANN strategy combines topographical and topological information and can be used for the design and development of novel anthrapyrazole analogues as anticancer molecules.

Liquid-Water Transfer Coefficients of Porous Building Materials Under High-Humidity Conditions

The moisture transfer coefficient is a key parameter for analyzing the moisture-based physical properties of materials and studying the heat–moisture coupling process within building envelopes. The liquid-water transfer coefficient, as an important aspect of this process, plays a significant role, especially under high-humidity conditions. However, the global research on liquid-water transfer coefficients is still far from complete. To further enhance the research on liquid-water transfer coefficients, this study conducted capillary water absorption experiments on seven traditional and new porous building materials, focusing on testing the moisture transfer coefficients, primarily the liquid-water transfer coefficient. A novel analysis regarding the impact of sealing materials was proposed, based on the experimental results. Based on the experimental data, the concept of a critical value related to the variation in the capillary moisture content and the liquid-water diffusion coefficient was raised, and, building upon traditional empirical models, a completely new computational model was proposed. Data processing was carried out using methods such as variability analysis, correlation analysis, and nonlinear regression for model fitting. The research findings indicate the following: (1) The capillary water absorption rate and capacity of a material are influenced by its density and porosity. (2) In terms of sealing materials, self-adhesive films performed better than non-adhesive films. (3) The concept of critical capillary moisture content was proposed, based on the rate of change in the liquid-water diffusion coefficient. For the threshold of w ≤ 80%, a new calculation model with a higher correlation coefficient was proposed which can meet the calculation requirements of the diffusion coefficient under the vast majority of relative-humidity conditions.

Genetic Association Studies for Indirect Selection for Important Yield and Yield Attributing Traits in Grain Cowpea (Vigna unguiculata L. Walp subsp unguiculata)

Aims: The present study encompasses genetic association studies among F4 and F5 families generated from various crosses in grain cowpea (Vigna unguiculata L. Walp subsp unguiculata). Study Design: Augmented design-II for F4 families and checks during kharif 2023 and Randomized block design with two replications for selected F5 families and checks during rabi-summer 2023-24. Place and Duration of Study: The current experiment was conducted at the Botany garden, Department of Genetics and Plant Breeding, University of Agricultural Sciences, Dharwad, Karnataka for the evaluation of 36 F4 families and six checks during kharif 2023 and 27 selected F5 families and five checks during rabi-summer 2023-24. Methodology: The material under experiment included 36 F4 families and six checks evaluated in augmented design-II during kharif 2023. Then during rabi-summer 2023-24, 27 selected F5 families and five checks evaluated in randomized block design. Observations were recorded for nine yield and yield attributing traits viz., total yield (kg/ha), single plant yield (g), number of pods per plant, test weight (g), number of seeds per pod, pod length (cm), number of clusters per plant, days to fifty per cent flowering and days to maturity. Correlation and path coefficients were estimated using standard procedures. Results: Correlation studies indicated that yield estimates were highly positively correlated to all yield attributing traits except to days to fifty per cent flowering and days to maturity. Characters like number of pods per plant (0.59, 0.64), pod length (0.65, 0.58) and test weight (0.39, 0.58) had highest correlations with yield in F4 and F5 generation respectively. Path coefficients further highlighted the character; number of pods per plant (0.363, 0.594) to have the highest direct effect on yield at both phenotypic and genotypic level respectively.

Triple Collocation-Based Uncertainty Analysis and Data Fusion of Multi-Source Evapotranspiration Data Across China

Accurate estimation of evapotranspiration (ET) is critical for understanding land-atmospheric interactions. Despite the advancement in ET measurement, a single ET estimate still suffers from inherent uncertainties. Data fusion provides a viable option for improving ET estimation by leveraging the strengths of individual ET products, especially the triple collocation (TC) method, which has a prominent advantage in not relying on the availability of “ground truth” data. In this work, we proposed a framework for uncertainty analysis and data fusion based on the extended TC (ETC) and multiple TC (MTC) variants. Three different sources of ET products, i.e., the Global Land Evaporation and Amsterdam Model (GLEAM), the fifth generation of European Reanalysis-Land (ERA5-Land), and the complementary relationship model (CR), were selected as the TC triplet. The analyses were conducted based on different climate zones and land cover types across China. Results show that ETC presents outstanding performance as most areas conform to the zero-error correlations assumption, while nearly half of the areas violate this assumption when using MTC. In addition, the ETC method derives a lower root mean square error (RMSE) and higher correlation coefficient (Corr) than the MTC one over most climate zones and land cover types. Among the ET products, GLEAM performs the best, while CR performs the worst. The merged ET estimates from both ETC and MTC methods are generally superior to the original triplets at the site scale. The findings indicate that the TC-based method could be a reliable tool for uncertainty analysis and data fusion.