High Correlation Values Research Articles

Reductive leaching behaviour of manganese and cobalt phases in laterite and manganese ores

The reductive leaching behaviour of manganese (Mn) and cobalt (Co) in a laterite and a pure manganese ore was investigated by varying and optimising various leaching parameters for the recovery of Mn and Co. The effect of different reducing agents (FeSO4, Na2SO3), sulfuric acid concentration, reductant/ore mass ratio, leaching time, and reaction temperature on the dissolution of Mn and Co from the laterite and manganese ores were studied. Furthermore, the kinetics of Mn and Co leaching from laterite ore was studied. The Mn and Co recovery from both ores increased with increasing H2SO4 concentration, reductant/ore mass ratio, leaching time, and reaction temperature. Based on the manganese and cobalt extraction efficiency, FeSO4 was the best reductant among the two evaluated reductants. The optimum leaching of Mn and Co from the laterite ore occurred at a H2SO4 concentration of 0.51 M, a reductant/ore mass ratio of 2.7, a leaching time of 5 h, and a reaction temperature of 363.15 K. For the manganese ore the optimum leaching of these elements at the same temperature occurred at a higher H2SO4 concentration of 1.02 M, a reductant/ore mass ratio of 2.4, and a shorter leaching time of 2 h. The results from the optimization studies showed that the leaching trends of Mn and Co phases in the manganese ore are similar to those of the Mn and Co phases in the laterite ore. Moreover, Co was co-leached together with Mn suggesting a degree of correlation of cobalt to manganese mineral phases in both, the laterite, and the manganese ores. The kinetics of the reductive leaching of Mn and Co for the laterite ore could well be described by the Avrami model reflected by high correlation coefficient values of R2 > 0.95. The leaching of Mn and Co occurred rapidly at the initial leaching stage but gradually slowed down with prolonged leaching time. This was indicated by the modal parameter (n) values that were <1, at varying reaction temperatures. The experiments showed further that Mn and Co leaching in laterite ore is a diffusion-controlled reaction, which was indicated by the apparent activation energy of 11.7 kJ mol−1 and 11.1 kJ mol−1, respectively.

Prediction of Mean Sea Level with GNSS-VLM Correction Using a Hybrid Deep Learning Model in Australia

The prediction of sea level rise is extremely important for improved future climate change mitigation and adaptation strategies. This study uses a hybrid convolutional neural Network (CNN) and a bidirectional long short-term (BiLSTM) model with successive variational mode decomposition (SVMD) to predict the absolute sea level for two study sites in Australia (Port Kembla and Milner Bay). More importantly, the sea level measurements using a tide gauge were corrected using Global Navigation Satellite System (GNSS) measurements of the vertical land movement (VLM). The SVMD-CNN-BiLSTM model was benchmarked by a multi-layer perceptron (MLP), support vector regression (SVR) and gradient boosting (GB). The SVMD-CNN-BiLSTM model outperformed all the comparative models with high correlation values of more than 0.95 for Port Kembla and Milner Bay. Similarly, the SVMD-CNN-BiLSTM model achieved the highest values for the Willmott index, the Nash–Sutcliffe index and the Legates and McCabe index for both study sites. The projected linear trend showed the expected annual mean sea rise for 2030. Using the current trend, Port Kembla was projected to have an MSL value of 1.03 m with a rate rise of approx. 4.5 mm/year. The rate of the MSL for Milner Bay was comparatively lower with a value of approx. 2.75 mm/year and an expected MSL value of 1.27 m for the year 2030.

Effect of Influencer Marketing on Purchase Intention a systematic review and Meta-Analysis

This research aims at providing a meta-analysis of empirical findings of the literature on the impact of influencer marketing on purchase intention. Four factors of influencer marketing were derived like trust, brand attitude, consumer attitude &amp; attractiveness. Results from random effects model showed significant effects of Trust towards influencer marketing on purchase intention (r= 0.541, [LI= 0.387-0.666], Z = 6.008), Consumer Attitude towards influencer marketingon purchase intention (r=0.622, [LI=0.435-0.758], Z= 5.434 Brand Attitude towards influencer marketing on purchase intention (r=0.402, [LI=0.303-0.491] ,Z=7.410), Attractiveness towards influencer marketing on purchase intention (r=0.506, [LI=0.350-0.645], Z=5.680). whereas the effect of Brand Attitude towards influencer marketing on purchase intention is having the lowest correlation value among all the variables and Consumer Attitude towards influencer marketing on purchase intention is having the highest correlation value among the following. Result of the study shows Consumer Attitude towards influencer marketing is going to have the highest impacton purchase intention. Only a small number of studies were accessible for meta-analysis since thestudy was only confined to Scopus and Web of Science, and several publications were also omitted because they lacked correlation

Development of novel chromones as antioxidant COX2 inhibitors: in vitro, QSAR, DFT, molecular docking, and molecular dynamics studies

The chromone derivatives are playing a prominent role in many plant cycles, for instance, the regulation of growth, stimulation of oxygen uptake in plants, and essential food constituents with valuable pro-health properties. Determination of the antioxidant activity of these compounds is an interesting approach to drug design and development. The antioxidant activity of the novel fifteen chromone compounds was estimated by using a spectrophotometric Dichloro-5,6-dicyano 1,4-benzoquinone (DDQ) assay method and the mechanism of antioxidant activity was discussed based on the Density functional theory (DFT) calculations. The compounds showed significant antioxidant activity which was correlated to their molecular structure by considering various molecular descriptors. Further, by using regression analysis QSAR-modeled equation was proposed and it has shown a high correlation coefficient value (0.946. We perform molecular docking and molecular dynamics simulations against the cyclooxygenase (COX2) enzyme to investigate the molecule’s anti-inflammatory activity and stability of protein-ligand complexes. Molecular docking and dynamics simulations revealed the compounds B3 and B8 were interacting with essential residues TYR385, HIS386, ASN382, TRP387, and HIS388 in the binding site that were crucial for optimizing heme and the resultant peroxidase and cyclooxygenase activities. The root mean square displacement and root mean square fluctuation plots revealed the stability of the B3-COX2 and B8-COX2 complexes. Based on our results, B3 and B8 compounds are considered as best antioxidants as well as COX2 inhibitors. Communicated by Ramaswamy H. Sarma

Space-Time Causality Analysis of Regional Impacts of ENSO on Terrestrial and Oceanic Precipitation

Future changes are expected in precipitation under climate change, therefore, changes are projected in the oceanic and terrestrial components. However, it remains poorly elucidated how the El Niño–Southern Oscillation (ENSO) can influence these changes. Therefore, we aimed to perform a space-time causality analysis of regional ENSO impacts on terrestrial and oceanic precipitation by using the Granger causality method as a function of eight temporal lags (lags 1–8). The monthly values of total precipitation obtained using the Lagrangian approach and their respective terrestrial (PLT) and oceanic (PLO) components were used. The analysis was performed for the two regions of western North America (WNA) and eastern South America (ESA) with strong ENSO signals. For the WNA region in winter, the maximum Granger causality was observed in the component of oceanic origin for temporal lags 1 and 2 (3 and 6 months), with a predominance of both positive and negative ENSO conditions. For the ESA region, it was verified that the causality of the ENSO index was maximum for PLT. Temporal lags 2–5 (6–15 months) stood out in winter when there was a marked region of the Granger causality over the La Plata Basin. In autumn, for lags 1–4 (3–12 months), the Granger causality values were predominant in the southern and western areas of ESA and showed a tendency to move northward with an increased temporal lag. Finally, it was shown that high correlation values did not imply the causality of the relationship between the ENSO index and precipitation in the two regions.

Fiber Optic Localized Surface Plasmon Resonance Sensor Based on Carboxymethylated Dextran Modified Gold Nanoparticles Surface for High Mobility Group Box 1 (HMGB1) Analysis

Rapid, sensitive, and reliable detection of high mobility group box 1 (HMGB1) is essential for medical and diagnostic applications due to its important role as a biomarker of chronic inflammation. Here, we report a facile method for the detection of HMGB1 using carboxymethyl dextran (CM-dextran) as a bridge molecule modified on the surface of gold nanoparticles combined with a fiber optic localized surface plasmon resonance (FOLSPR) biosensor. Under optimal conditions, the results showed that the FOLSPR sensor detected HMGB1 with a wide linear range (10-10 to 10-6 g/mL), fast response (less than 10 min), and a low detection limit of 43.4 pg/mL (1.7 pM) and high correlation coefficient values (>0.9928). Furthermore, the accurate quantification and reliable validation of kinetic binding events measured by the currently working biosensors are comparable to surface plasmon resonance sensing systems, providing new insights into direct biomarker detection for clinical applications.

Classification of foot type from podography: correlation of results between six quantitative assessment methods

Objective: Apply the different methods available in the literature to classify a sample of podography and evaluate the level of agreement between the results. Methods: Six quantitative and one qualitative method to classify foot type from podography were recorded on 30 feet. The podography indexes were calculated, and the level of agreement between methods was explored. Results: Correlation values were above r = 0.84 except for the test arch footprint angle. The highest correlation values were found between the truncated arch index and footprint index (0.99), arch index and footprint index (-0.94), and arch index and truncated arch index (-0.94), and the lowest was the arch footprint angle with the other parameters. However, there was a difference in the classification between the foot types, indicating a lack of agreement of thresholds between foot types. Qualitative visual inspection was the faster method to classify foot type.Conclusion: The visual inspection was the fastest test to apply, followed by the quantitative arch footprint angle test. High correlation values were found between tests, especially the arch index and the footprint index, arch-length index, truncated arch index, and Chippaux-Smirak index tests. Level of Evidence IV; Therapeutic Studies; Case Series.

Using Artificial Intelligence Methods for Detection of HCV-Caused Diseases

The Hepatitis C Virus (HCV) can cause chronic diseases and even lead to more serious conditions such as cirrhosis and fibrosis. Early detection of HCV infection is crucial to prevent these outcomes. However, in the early stages of infection, when symptoms are not yet evident, patients rarely undergo HCV testing. This highlights the need for alternative materials to guide HCV testing for early detection of the disease. In this study, we investigate the use of artificial intelligence technology to determine the disease status of individuals using blood data. A total of 615 individuals were included in the study. Preprocessing, filtering, feature selection, and classification processes were applied to the blood data. The correlation method was used for feature selection, where the features with high correlation values were selected and given as input to five different classification algorithms. The results of the study showed that the K-Nearest Neighbor (KNN) algorithm achieved the best classification success for detecting HCV patients, with a rate of 99.1%. This research demonstrates that artificial intelligence technology can be an effective tool for early detection of HCV-related diseases. The results indicate that the KNN algorithm can provide clear information about hepatitis infection from different blood values. Future studies can explore the use of other AI techniques and expand the sample size to improve the accuracy of the model.

Identification of Rainfall events on Climate Phenomena in Medan based on Machine Learning

Indonesia has diverse topographical conditions that result in Indonesia having a unique climate. One of the unique climate elements to be studied is rainfall, because rainfall has a different pattern in each region, this different rainfall pattern is caused by several climate phenomena factors that affect the rainfall pattern, including El-Nino Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) and Madden Julian Oscillation (MJO). Medan City is the capital of North Sumatra province which is one of the areas in the flood-prone category in North Sumatra, where the factor of flooding is due to rainfall events in a long period of time, so the author wants to know which climatic phenomena factors can affect rainfall events in Medan city by using Machine Learning technology through the Matlab application, where in this study has a method by forming four combination models, namely the combination of the influence of IOD, SOI and MJO; second combination of IOD and SOI; third combination of SOI and MJO; and fourth combination of MJO and IOD, these four combinations will be the rainfall value of the four models. Furthermore, the rainfall value of the model is compared with the observed rainfall value and verification test using Mean Absolute Error (MAE) and correlation. Then the calculation of the comparison between the four rainfall models with the observed rainfall obtained the lowest MAE value during the SOI and MJO phenomenon of 15.0 mm and the highest correlation value during the IOD and SOI and SOI and MJO phenomena. So it is concluded that the combination of SOI and MJO has the best verification value. This shows that based on Machine Learning modeling, the model shown as the best predictor in Medan city is when the model combination consists of SOI and MJO.

Determination of cannabinoids in human cerumen samples by use of UPLC-MS/MS as a potential biomarker for drug use

A quantitative analytical procedure was developed and validated by the use of Ultra- Performance Liquid Chromatography tandem Mass Spectrometry (UPLC-MS/MS) for the determination of Cannabidiol (CBD), Cannabinol (CBN), Δ9-Tetrahydrocannabinol (Δ9-THC), Cannabichromene (CBC), Cannabigerol (CBG) and 11-Nor- 9- Carboxy- Tetrahydrocannabinol (THC-COOH) in an unconventional biological matrix, cerumen. All the investigated calibration curves were characterized by high correlation values (R2 ≥ 0.9965). The LODs and LOQs ranged from 0.004 to 0.009 μg g-1 and 0.012–0.029 μg g-1, respectively. Intra-assay and inter-assay precision were found to be 0.6–2.5%, and 0.8–2.2%, respectively. All recovery values of cannabinoids, with the use of the optimum cotton swab, at low (0.008 μg g-1 of cerumen), medium (0.037 μg g-1of cerumen) and high (0.16 μg g-1 of cerumen) control levels, were estimated to be above 86%. The method developed here permitted the analysis of real cerumen samples obtained from fourteen cannabis users. In twelve out of fourteen cases, Δ9-THC was found to be positive, while in six cases, three major cannabinoids, CBN, CBG and Δ9-THC were quantified at concentrations 0.02–0.21 μg g-1, 0.01–0.24 μg g-1 and 0.01–4.86 μg g-1, respectively. Subject #8 has the highest amount of the detected substances in both left and right ear, with Δ9-THC at a concentration of 1.85 and 4.86 μg g-1, CBG 0.06 and 0.24 μg g-1, CBN 0.10 and 0.21 μg g-1, respectively. In addition, a detection window for the substances Δ9-Tetrahydrocannabinol, Cannabinol and Cannabigerol, in cerumen, was defined with success. In this case, Δ9-THC reached a maximum detection frame of up to fifteen days after smoking 0.5 g of marijuana cigarette. ANOVA-one-way analysis also indicated that the average earwax production of non-cannabis users differs significantly from the one of cannabis users (p = 0.048, <0.05). On the other hand, no significant difference was noticed between male and female users as the p value exceeded 0.05. In addition, no significant effect was observed on earwax production in regard to age, frequency and the last time of use (p > 0.05). These last three factors proved to have a significant impact on cannabinoids concentrations, since p values were less than 0.05.

AFTER-SCHOOL ACTIVITIES AND SECONDARY STUDENTS ACADEMIC PERFORMANCE IN SELECTED SCIENCE-RELATED SUBJECTS IN YALA LOCAL GOVERNMENT AREA OF CROSS RIVER STATE, NIGERIA: CURRICULUM IMPLICATION

The study focused on after-school activities and secondary students’ academic performance in selected science-related subjects in Yala Local Government Area of Cross River State, Nigeria: Curriculum implication. The research design adopted for the study is the survey design and the population of this study comprised all Senior Secondary School One (SSS1) students in selected public Secondary schools in Yala Local Government Area. The population stood at 3,201, with males 1, 501 and 1, 700 females in ten (10) selected Secondary schools in the area. The simple random sampling technique was adopted to select the respondents in the study area thus, (111 males and 119 females). The instruments used for data collection were two questionnaires titled After-School Activities Questionnaire and Student’s Academic Performance Test in Chemistry, Physics, and Mathematics (ASAQAPTCPM), designed by experts in the chosen field of study, and the reliability was established with spilt-half reliability techniques result showed a high correlation value of 0.87 to 0.89 and this was considered high enough to use the instrument for data collection. The hypotheses formulated to guide the study were tested using the appropriate statistical technique at 0.05 level of significance using simple linear regression analysis and the findings revealed that engagement in playing after school, engagement in homework, and engagement in extra moral classes significantly predict students’ academic performance in Chemistry, Physics, and Mathematics. It was recommended that parents should encourage their children by providing the needed learning materials like textbooks, prompt payment of school fees, school uniforms, and engagement in extra-curricular activities to facilitate playing activities in the learning process, such as sports, drama music, scouting, dance, and various clubs which will promote harmony in learning.

Bimetallic (Fe/Zn-ZIF-8) crystals: Fabrication and adsorptive removal activity

This work presents an investigation on the fabrication and characterization of Fe doped zeolitic imidazolate framework (ZIF-8) of 1:1 M ratio of Zn:Fe (Fe/Zn-ZIF-8) and adsorption performances of acquired materials. The synthesized Zn-ZIF-8, Fe-ZIF-8, and Fe/Zn-ZIF-8 materials were characterized for the phase structure, morphology, elemental analysis and surface area by using X-ray diffraction (XRD), Field emission scanning electron microscope (FESEM), Energy Dispersive X-Ray (EDX), and BET surface area, respectively. The results revealed the adsorption capacity was enhanced by incorporation of Fe into ZIF-8 structure. The CR dye adsorption capacities were 287, 219, and 412 mg/g for Zn-ZIF-8, Fe-ZIF-8, and Fe/Zn-ZIF-8 adsorbers, respectively. The CR adsorption obeyed to the pseudo 2nd order model and the Langmuir model was most closely matched during the high value of correlation coefficient (R2), signifying a palpable monolayer adsorption rather than multilayer.

STATISTICAL DOWNSCALING MODEL WITH PRINCIPAL COMPONENT REGRESSION AND LATENT ROOT REGRESSION TO FORECAST RAINFALL IN PANGKEP REGENCY

Climate information, especially rainfall, is needed by various sectors in Indonesia, including the marine and fisheries sectors. Estimating high-resolution climate models continues to develop by involving global-scale climate variables, one of which is the global circulation model (GCM) output precipitation. Statistical downscaling (SD) relates global scale climate variables to local scales. Principal component regression (PCR) and latent root regression (LRR) techniques are statistical methods used in the SD model to overcome the high correlation between GCM data grids. PCR focuses on the variability in the predictor variables, while the LRR focuses on the variability between the response variables and predictors. This method was applied to Pangkep Regency rainfall data as a local scale response variable and GCM precipitation as a predictor variable (January 1999 to December 2020). This study aimed to obtain the number of principal component (PC) in the SD model and the forecast value of the 2020 rainfall data. In addition, the dummy variable resulting from K-means was used as a predictor variable in PCR and LRR. The result is that using the first 11-15 PC has a cumulative diversity proportion of 98%. Furthermore, by using the data for the 1999-2019 period, adding a dummy variable to the PCR can increase the accuracy of the model (the coefficient of determination is 92.27%-92.43%). However, LRR with and without dummy variables produces relatively the same model accuracy. In general, the LRR model is better at explaining the diversity of the Pangkep District rainfall data than the PCR model. The prediction of rainfall for the 2020 period at LRR with 13 PC is an accurate prediction based on the highest correlation value (0.97) and the lowest root mean square error prediction (75.17).

Development and validation of anthropometric-based fat-mass prediction equations using air displacement plethysmography in Mexican infants

Background/ObjectivesFat-mass (FM) assessment since birth using valid methodologies is crucial since excessive adiposity represents a risk factor for adverse metabolic outcomes. Aim: To develop infant FM prediction equations using anthropometry and validate them against air-displacement plethysmography (ADP).Subjects/MethodsClinical, anthropometric (weight, length, body-mass index –BMI–, circumferences, and skinfolds), and FM (ADP) data were collected from healthy-term infants at 1 (n = 133), 3 (n = 105), and 6 (n = 101) months enrolled in the OBESO perinatal cohort (Mexico City). FM prediction models were developed in 3 steps: 1) Variable Selection (LASSO regression), 2) Model behavior evaluation (12-fold cross-validation, using Theil-Sen regressions), and 3) Final model evaluation (Bland-Altman plots, Deming regression).ResultsRelevant variables in the FM prediction models included BMI, circumferences (waist, thigh, and calf), and skinfolds (waist, triceps, subscapular, thigh, and calf). The R2 of each model was 1 M: 0.54, 3 M: 0.69, 6 M: 0.63. Predicted FM showed high correlation values (r ≥ 0.73, p < 0.001) with FM measured with ADP. There were no significant differences between predicted vs measured FM (1 M: 0.62 vs 0.6; 3 M: 1.2 vs 1.35; 6 M: 1.65 vs 1.76 kg; p > 0.05). Bias were: 1 M −0.021 (95%CI: −0.050 to 0.008), 3 M: 0.014 (95%CI: 0.090–0.195), 6 M: 0.108 (95%CI: 0.046–0.169).ConclusionAnthropometry-based prediction equations are inexpensive and represent a more accessible method to estimate body composition. The proposed equations are useful for evaluating FM in Mexican infants.

Bio-adsorbent hydroxyapatite for drinking water defluoridation: column performance modelling studies.

Waste marble powder (WMP) is a rich source of calcium and magnesium salts having an affinity for fluoride ions and therefore serves as a good defluoridation agent. Hydroxyapatite was synthesized from WMP generated by the marble processing industry to make an adsorbent for drinking water defluoridation. The synthesized marble hydroxyapatite (MA-Hap LR) powder was further formed into 2-3mm pellets by extrusion spheronization technique using a polyvinyl alcohol binder. Continuous column defluoridation studies were conducted to obtain optimized column parameters such as input fluoride concentration, column inflow rates, optimum pellet size, and adsorbent bed parameters to obtain maximum fluoride adsorption capacity. The best breakthrough column performance was a maximum adsorption capacity of 1.21mg/g, treating 10mg/L fluoride concentration. The optimized column flow rate was at 1 LPH using an adsorbent bed height of 25cm, which processed 28.5-bed volumes at an adsorbent exhaustion rate of 7.4g/L. The column breakthrough performance data were fit into various kinetic models (Thomas model and Yoon-Nelson model) to describe adsorption kinetics and obtain correlation coefficients. Thomas's model fitted well with a high correlation coefficient value. Modelling studies indicate MA-Hap as a promising adsorbent for drinking water treatment, and optimum column design parameters were identified for scale-up for real applications.

Quantitative measurements and morphological evaluation of fetal cardiovascular structures with fetal cardiac MRI

PurposeFetal cardiac magnetic resonance imaging (FCMR) can be used as an imaging modality in fetal cardiovascular evaluation as studied in recent years. We aimed to evaluate cardiovascular morphology using FCMR and to observe the development of cardiovascular structures according to gestational age (GA) in pregnant women. MethodIn our prospective study, 120 pregnant women between 19 and 37 weeks of gestation in whom absence of cardiac anomaly could not be excluded by ultrasonography (US) or, who were referred to us for magnetic resonance imaging (MRI) for suspected non-cardiovascular system pathology, were included. According to the axis of the fetal heart, axial, coronal, and sagittal multiplanar steady-state free precession (SSFP) and 'real time' untriggered SSFP sequence, respectively, were obtained. The morphology of the cardiovascular structures and their relationships with each other were evaluated, and their sizes were measured. ResultsSeven cases (6.3%) contained motion artefacts that did not allow the assessment and measurement of cardiovascular morphology, and three (2.9%) cases with cardiac pathology in the analysed images were excluded from the study. The study included a total of 100 cases. Cardiac chamber diameter, heart diameter, heart length, heart area, thoracic diameter, and thoracic area were measured in all fetuses. The diameters of the aorta ascendens (Aa), aortic isthmus (Ai), aorta descendens (Ad), main pulmonary artery (MPA), ductus arteriosus (DA, superior vena cava (SVC), and inferior vena cava (IVC) were measured in all fetuses. The left pulmonary artery (LPA) was visualised in 89 patients (89%). The right PA (RPA) was visualised in 99 (99%) cases. Four pulmonary veins (PVs) were seen in 49 (49%) cases, three in 33 (33%), and two in 18 (18%). High correlation values were found for all diameter measurements performed with GW. ConclusionIn cases where US cannot achieve adequate image quality, FCMR can contribute to diagnosis. The very short acquisition time and parallel imaging technique with the SSFP sequence allow for adequate image quality without maternal or fetal sedation.

An Imine-Based Porous 3D Covalent Organic Polymer as a New Sorbent for the Solid-Phase Extraction of Amphenicols from Water Sample.

In this paper, an imine-based porous 3D covalent organic polymer (COP) was synthesized via solvothermal condensation. The structure of the 3D COP was fully characterized by Fourier transform infrared spectroscopy, scanning electron microscopy, transmission electron microscopy, and powder X-ray diffractometry, thermogravimetric analysis, and Brunauer-Emmer-Teller (BET) nitrogen adsorption. This porous 3D COP was used as a new sorbent for the solid-phase extraction (SPE) of amphenicol drugs, including chloramphenicol (CAP), thiamphenicol (TAP), and florfenicol (FF) in aqueous solution. Factors were investigated for their effects on the SPE efficiency, including the types and volume of eluent, washing speed, pH, and salinity of water. Under the optimized conditions, this method gave a wide linear range (0.1-200 ng/mL) with a high correlation coefficient value (R2 > 0.99), low limits of detection (LODs, 0.01-0.03 ng/mL), and low limits of quantification (LOQs, 0.04-0.10 ng/mL). The recoveries ranged from 83.98% to 110.7% with RSDs ≤ 7.02%. The good enrichment performance for this porous 3D COP might contribute to the hydrophobic and π-π interactions, the size-matching effect, hydrogen bonding, and the good chemical stability of 3D COP. This 3D COP-SPE method provides a promising approach to selectively extract trace amounts of CAP, TAP, and FF in environmental water samples in ng quantities.

Artificial intelligence to growth stresses predicting in Eucalyptus clones using dendrometric variables and wood density

Eucalyptus planted forests contribute to maximizing lumber production but problems such as longitudinal growth strain can negatively influence the quality of the products. Knowing dendrometric variables and wood properties can help in the prediction of longitudinal growth strain, mainly with the help of artificial intelligence. Thus, the aim of this research was to evaluate the use of artificial neural networks to predict longitudinal growth strain in Eucalyptus trees based on dendrometric variables, spacing between trees and wood density. The longitudinal growth strain was measured in trees of four Eucalyptus clones planted in three spacings. The diameter and height of each tree were measured. The basic wood density was determined. Artificial neural networks were used to estimate longitudinal growth strain as a function of dendrometric variables, tree spacing and wood density. The results showed that the artificial neural networks presented good results for training and validation, with most of them resulting in high correlation coefficient values. The trained artificial neural networks showed a correlation coefficient above 0,56. Artificial neural networks showed that the variables clone and basic wood density were the ones that most contributed to the prediction of longitudinal growth strain. On the other hand, the spacing between trees, the height of the tree and the diameter at breast height were not relevant to predict growth stresses.

Extent of interocular (a)symmetry based on the metabolomic profile of human aqueous humor.

Aims: Interocular comparison of the metabolomic signature of aqueous humor (AH) was performed. The aim of the study was to quantitatively evaluate the symmetry in concentrations of various metabolites belonging to different categories. Methods: The study included AH samples from 23 patients, 74.17 ± 11.52years old, undergoing simultaneous bilateral cataract surgery at the Ophthalmology Department of the Medical University of Bialystok, Poland. Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS)-based targeted metabolomics and lipidomics analyses of AH samples were performed using the AbsoluteIDQ® p180 kit. Out of 188 metabolites available in the kit, 67 were measured in the majority (>70%) of the samples: 21/21 amino acids, 10/22 biogenic amines, 9/40 acylcarnitines, 0/14 lysophosphatidylcholines, 21/76 phosphatidylcholines, 5/15 sphingolipids, and 1/1sum of hexoses. Results: The comparison of both eyes revealed that the concentrations of metabolites did not differ significantly (p < 0.05) except for taurine (p = 0.037). There was moderate-to-strong positive interocular correlation (r > 0.5) between most metabolites regarding concentration. This was confirmed by the high intraclass correlation coefficient (ICC) values of different levels, which varied for the different metabolites. However, there were exceptions. Correlations were not significant for 2 acylcarnitines (tiglylcarnitine and decadienylcarnitine) and 3 glycerophospholipids (PC aa C32:3, PC aa C40:2, and PC aa C40:5). Conclusion: With a few exceptions, a single eye was found to be representative of the fellow eye in terms of the concentration of most of the analyzed metabolites. The degree of intraindividual variability in the AH of fellow eyes differs for particular metabolites/metabolite categories.

Synthesis of nanomagnetite/copper oxide/potassium carrageenan nanocomposite for the adsorption and Photo-Fenton degradation of Safranin-O: kinetic and thermodynamic studies

In the current study, a novel nanomaterial called nanomagnetite/copper oxide/potassium carrageenan nanocomposite (MKCO) was fabricated to include Fenton (nanomagnetite, NM) and Fenton-like reagent (copper oxide nanoparticles, NCO) in a matrix of potassium carrageenan biopolymer. The prepared solid materials were characterized by different physicochemical techniques, such as TGA, N2 adsorption/desorption, SEM, TEM, XRD, DRS, pHPZC, and FTIR. The prepared MKCO showed unique properties like higher specific surface area of 652.50 m2/g, pore radius of 1.19 nm, pHPZC equals 7.80, and the presence of different surface chemical functional groups. Under various application conditions, comparative experiments between Safranin-O dye (SO) adsorption and Photo-Fenton catalytic degradation were conducted. After 24 h, MKCO had a maximum adsorption capacity of 384.61 mg/g at 42 °C, while the Photo-Fenton oxidation process took only 10 min to totally decompose 93% of SO at 21 °C. Based on the higher values of correlation coefficients, Langmuir’s adsorption model is the best-fitted adsorption model for SO onto all the prepared solid materials. Studies on SO adsorption’s kinetics and thermodynamics show that it is physisorption and that it operates according to endothermic, spontaneous, and PFO model processes. While, PFO, endothermic, and non-spontaneous processes are satisfied by the catalytic decomposition of SO. After five application cycles, MKCO demonstrated good catalyst reusability with a 3.4% decrease in degrading efficiency. For lower contaminant concentrations and shorter application times, Photo-Fenton catalytic degradation of organic pollutants is more effective than adsorption.Graphical abstractFenton and Photo-Fenton degradation of Safranin-O