High Correlation Values Research Articles

Spatio-temporal dynamics of methane concentration and its association to climatic and vegetation parameters: a case study of the Northern Cape Province, South Africa

Methane (CH4) is the second most abundant anthropogenic greenhouse gas after carbon dioxide (CO2), accounting for about 20% of global emissions. CH4 anthropogenic emission sources include landfills, oil and natural gas systems, agricultural activities, coal mining, stationary and mobile combustion, wastewater treatment and certain industrial processes. In this work, we examine the spatio-temporal dynamics of CH4 and its relationship to climatic and vegetation parameters in the Northern Cape province in South Africa. Various datasets from the TROPOspheric Monitoring Instrument, Moderate Resolution Imaging Spectroradiometer, Atmospheric Infrared Sounder and the Global Precipitation Climatology Project were used. The results show an increasing trend of CH4 concentration throughout the entire province. The greatest increase in CH4 concentration is observed in the western parts of the province during June–July–August (JJA) season. CH4 concentration shows negligible correlation with most climatic parameters, i.e. Temperature (Temp), Precipitation (Precip) and NDVI for both seasons. The Temp–NDVI relationship shows high correlation values of r = –0.71 and r = 0.82 for the DJF and JJA seasons, respectively. Seasonality plays a critical role in the relationships of the CH4 to climatic and vegetation parameters. This study shows that we are in a crisis, and robust mitigation strategies are needed to combat this.

STUDY OF PHENOTYPIC AND GENETIC VARIABILITY IN MAIZE CROSSINGS BETWEEN CYCLE I INBRED LINES AND ELITE LINES (CYCLE II)

Maize inbred lines derived from local populations (Cycle I lines) were tested using cycle II elite inbred lines. In the cross system, hybrids have a wide variability for yield (6601-10571 kg/ha), the highest differences between hybrids are due to the non-additive genetic effects; undesirable traits transmitted by cycle I inbred lines are weak resistance to stalk breaking and fallen plants; for the percentage of unbroken plants, the genetic variability is high, the percentage of unbroken plants for simple hybrids has a range value between 65.02% and 89.70%.There were calculated the correlations coefficients between ”per se” values for the correlation of inbred lines from crossbreeding system and additive genetic effects values calculated in the crossbreeding system ”m x n”. Positive correlations were identified between the values per se and the positive genetic effects for yield, ear weight, ear length, numbers of kernels per ear, TKW, kernel depth. These high positive correlation values indicate the possibility to use phenotypic markers in the process of inbred lines creation.

Phenology, phyllochron and productivity of sorghum in response to varying growing environments and nitrogen levels in the semiarid irrigated condition

Climate change is likely to affect agricultural production in many parts of the world. Sorghum is the stable food crop in semiarid areas, and climate change has significant adverse effects on sorghum yields. The optimum time of sowing is one of the important technologies that allow the crop to better utilise natural resources by the crop to maximise productivity. For optimizing the sowing time, the field investigation was carried out in Tamil Nadu Agricultural University, Coimbatore, under summer irrigated conditions in 2022. The experiment was laid out in a split-plot design with three replications. The main plot consists of five dates of sowing and the subplot includes different nitrogen levels. The results revealed that advanced sowing (first fortnight of April) reduced the yield by 13.73% and delayed sowing (second fortnight of May) recorded the maximum yield (4230.3kg ha-1) with higher phyllochron values. GDD manifested a significant negative relationship with a high correlation coefficient (r) value of -0.98** (p = 0.01) for days to anthesis, -0.98** (p = 0.01) for days to physiological maturity and -0.88* (p = 0.05) for phyllochron. Average temperature exhibited a significant negative correlation with days to anthesis (-0.75*), physiological maturity (-0.97**) and phyllochron (-0.89*). The best-fit regression model showed that a GDD-based model could better predict the phenology of sorghum compared to an average temperature-based model. The study indicated that understanding sorghum's phenological response and productivity level under varied dates of sowing and the nitrogen levels in semiarid environments can help determine the appropriate sowing time and optimum nitrogen level for achieving better yield in summer-irrigated sorghum.

Testing the Suitability of Snow Depth in Winter Recreation Areas in Cities in the Republican Period with InSAR Technique: Abalı Ski Center Example

The Republican period is a period that coincides with the modernization process of Turkey, and the lifestyle in the cities has also changed significantly during this period. Winter recreation areas are the areas that people prefer to have fun, do sports and relax in the cold months. The Republic of Türkiye has a great importance in the tourism sector. In addition, the alternative tourism sector is developing rapidly and leading to different investments. Increasing winter recreation areas and facility investments in order to keep four seasons tourism alive with the advantage of climatic diversity is of great importance for alternative tourism. Different criteria such as slope, aspect, height, transportation, and sunshine duration are used to determine suitable areas for these investments. However, the most important suitability criterion is the snow factor. In order for the facilities to be established, the snow must be permanent and of a certain thickness in the winter season. In this study, the winter season (December-January-February) was examined temporally (2020-21-22), in the example of the Abalı Ski Center established in the republican period, and the average snow depth change in the area was determined by the InSAR (Interferometric Artificial Aperture Radar) technique. In addition, the accuracy of the results was tested with the real measurements of the 2021-22 winter season in the study area. The analysis conducted to assess the accuracy of Sentinel-1A C-band RADAR images by comparing snow depth data has yielded a high correlation value of 0.79, indicating a strong and consistent result. As a result, the importance of determining the snow factor both as a suitability criterion in determining the suitability of land use and the suitability of snow permanence in temporal change for establishment has been revealed.

Shyness and Willingness to Communicate: Levels, Correlations, and Perspectives of Saudi EFL Learners

A primary effort has been exerted in the literature to examine how influential affective factors are in language learning. The present study extends these attempts and aims at reviewing the levels of shyness and willingness to communicate among EFL learners and the relation between these two affective factors and their impact on communication skills inside the class from the students’ perspectives. It sheds light on the need to consider the effects of these personal traits on students’ learning. 360 Saudi EFL learners at a Saudi public university completed the shyness and willingness to communicate scales. For further analysis, the researchers interviewed 27 students to explore their perceptions of how these two aspects affect their communication skills inside the class. Quantitative data analysis showed that most participants were at the mid-levels of both scales. Furthermore, there was a negative correlation between the participants’ shyness and their overall willingness to communicate and its subcategories, with the highest correlation value between shyness and these subcategories: strangers, meetings, and public speaking. Qualitative analysis exhibited the participants’ perceptions, misconceptions, correlations of the two variables, and determinants of the participants’ levels of shyness and WTC. The findings of this study shed light on the importance of considering these factors for more successful learning/teaching experiences.

Evaluation of the physical properties of athletes based on the results of the hamstring mccall test, conducted using k-deltas platform

The paper proposes a method of forming a generalized comparative assessment of the physical qualities of a group of athletes from one team based on McCall's hamstring test conducted using the K-Deltas platform. The peak loading of the legs, the rate of growth of force before reaching the peak value, the time of reaching the peak load, the rate of decrease of the load after reaching the peak value were measured. All measurements were carried out for the athlete's left and right leg, bent at the knee joint 900 and 300. At the same time, such physical parameters of the athlete as strength, reaction, neuromuscular fatigue were examined. These data are presented in the generalized relative ratings of athletes of the group. For a number of studies, for which high linear correlation values were obtained, regression linear relationships (as well as their largest relative deviations) are presented, that can be used in the future for preliminary estimation

Predicting the risk of stroke based on machine learning

Stroke, the second largest leading cause of death among all chronic diseases, is affecting about 101 million people in the world currently. It is estimated that this number of stroke cases will increase by 2.25 by the year 2050. Considering the large number of potential patients with stroke, a mathematical model is designed to predict ones risk of having a stroke in the future based on ones basic health data using machinery methods. Using the algorithm of Logistic Regression, the model reaches an accuracy of 92.28% when predicting whether one has a stroke, the model also validates that hypertension is the leading cause of the incidence of stroke by finding out the highest correlation value among all the feature variables. People who would like to know their probability of having a stroke can use the model, then they can have some precautionary measures to lower the likelihood of happening of stroke based on the prediction given, which helps save the medical costs and overuse of medical resources. Governments can enact policies and allocate medical resources based on the predictions made by the model.

Modelling, Simulation, and Performance Analysis of Intra-Vehicular Heterogeneous Networks

Vehicles today use a variety of network segments operated by different technologies and protocols within the car (CAN, LIN, Automotive Ethernet, MOST, FlexRay, etc.) to exchange data between different control modules, sensors, and actuators. The exchange of information between other network domains (heterogeneous networks) is enabled through various interconnection points called gateways/bridges. The resulting performance depends on its interconnection structure, network segment traffic aggregation scheme, and medium access technique. Although protocols such as CAN, LIN, FlexRay, and Ethernet have been used in network design for some time, performance modeling and analysis are still needed given the variety of traffic types and sources, new application limitations, and especially the lack of formal verification of network performance for different network scenarios and configurations. This paper presents an end-to-end throughput and delay performance analysis for a reference intra-vehicular network scenario. These models have been validated through simulations in which high correlation values were obtained from 98.7400 to 99.9999, with a low mean square error. The validation cases show that for different LIN, CAN, and Ethernet network configurations, the performance threshold values defined for most current vehicle applications are preserved. However, if the network configuration is modified, the proposed analytical models can be used to formally verify the corresponding performance and delay changes and thus validate whether or not the application requirements are met.

Modeling of small molecule's affinity to phospholipids using IAM-HPLC and QSRR approach enhanced by similarity-based machine algorithms

Immobilized artificial membrane chromatography (IAM) has been proposed as a more biosimilar alternative to classical lipophilicity measurement. Determination of small molecule's affinity to phospholipids can be supported for predicting their behavior in the human body. Therefore, a better understanding of the molecular interaction mechanism between small xenobiotics and phospholipids can accelerate drug discovery. Here, the quantitative structure-retention relationships (QSRR) approach was integrated with mechanistic descriptors calculated using Chemicalize software to propose an easy-to-interpretation QSRR model. Considering the heterogeneous character of the data set, locally weighted least squares kernel regression belonging to similarity-based machine learning methods have been applied. The results showed that lipophilicity, charge, and maximum projection area determine molecule binding to phospholipids. Full validation of the obtained model based on OECD recommendations has been performed and the applicability domain was defined using the probability-oriented distance-based approach. The high values of predictive squared correlation coefficient (Q2), and small root mean square error of prediction (RMSEP), 0.812 and 6.739, respectively, confirmed that the obtained QSRR model is not well-fitted to the training data but also showed prediction power. Additionally, only 1.5% of molecules from the training set and 2.8% from the validation test are outside the applicability domain, confirming great predictive abilities.

Use of tritium in characterizing the leachate migration from a landfill – Central de tratamento de resíduos sólidos (CTRS) BR040 (Belo Horizonte, Minas Gerais, Brazil)

Landfills often become a source of environmental impact due to the percolation of the leachate (liquid resulting from the decomposition of buried waste), which can eventually disperse and contaminate the soil and water bodies. This study aimed to evaluate quantitatively and spatially the leachate plume from the former Belo Horizonte landfill in the Coqueiros and Taiobas watersheds by using tritium (3H) concentration in nineteen (19) sampling points: 14 groundwater, 4 surface water and 1 leachate. Among the methodologies applied to determine the 3H content in the leachate, pre-filtration proved to be the most effective due to the ease treating of the sample, as well as the identical results obtained compared to the traditional method. The 3H contents resulted in concentrations up to thirty times higher in the leachate (=96,19 to 111,87 TU) and ten times in groundwater (PM23/3 = 45,06 TU), compared to the calculated threshold (TS) of rainfall tritium (TS_3Hrain = 3,41 TU). Temporal series results indicate no trend and no influence of seasonality regarding the entry of pluvial tritium, leachate liquid level and wells water level. The 3H contour maps shows a transport of the mixture (water and leachate) towards the Coqueiros stream, with a higher concentration of leachate in wells closer to the landfill slope (PM13, PM14, PM15, and PM23) and in the southwest margin of the Coqueiros stream (PM17). In the furthest portion of the landfill, 3H levels are higher than the base threshold only at the deepest level of PM20 (=3,90 TU), possibly due to factors such as: i) denser character of the leachate compared to groundwater; ii) greater influence of recharge for regions further away from the landfill slope; and iii) the presence of the rock about 50 m from the surface. Based on the hydrogeological conceptual model, the transport of the leachate water mixture in the groundwater water compartment of the Coqueiros watershed basin seems to follow a piston or dispersion flow, with a small Peclet number (Pd = 0,05, as an example), as indicated by the high correlation coefficient values (R2 ≈ 0,85 to 0,99) from graphic interpolation. The mixture of water and leachate results in a transient time (tt) ≈ 30 years and linear velocity (Vl) ≈ 3–14 m/year in the interpolated sections. These indicate a coherent correlation with the mean hydraulic conductivity (K ≈ 29,34 m/year), as they result in effective porosity values between (Ne) ≈ 0.16 to 0.36, which corresponds to Ne range for unconsolidated sediments, similar to the wells lithotype profiles. Time series evaluation combined with lumped parameter approach suggest to be an interesting way to better understanding the HCM, and, therefore, to determine hydrodynamic parameters using tritium as a tracer.

Quantification of fat percentage in some types of meat using low-angle X-ray scattering (LAXS) and Monte Carlo simulation

The current research used a method for the quantification of fat content in many types of meat by comparing their X-ray scattering profiles measured practically and simulated profiles from a Monte Carlo simulation code. The measured X-ray scattering profile of each meat sample was compared to Monte Carlo simulated profiles to deduce the fat percentage in each sample. Two peaks were obtained from each profile. The first peak was at a momentum transfer of 1.1 nm−1 (characteristic of fat content) and the second peak was at a momentum transfer of 1.6 nm−1 (characteristic of muscle content). The correlation coefficient and least square error between measured and simulated data were calculated to obtain the percentage of fat in each sample from the best-fitted profiles. Results showed a high correlation coefficient value and low least square error of value (>0.9) and (<0.07), respectively between measured and simulated profiles. The fat content in each sample is plotted against three profile characterization parameters (the ratio of peak intensities, the ratio of area under peaks and the ratio of area under fat peak to total profile area) for measured and Monte Carlo simulated X-ray scattering profiles. Therefore, it was possible to use this Monte Carlo simulation code as a way for precis quantification of fat percentages in some types of meat.

Chitosan coated graphene oxide incorporated sodium alginate hydrogel beads for the controlled release of amoxicillin

Biopolymers are crucial in pharmaceuticals, particularly for controlled drug release. In this study, we loaded the broad-spectrum antibacterial drug amoxicillin into sodium alginate, a well-known biopolymer. Graphene oxide was incorporated into the composite, and the hydrogel beads were coated with chitosan for its mucoadhesive properties. Various composites were formulated by adjusting the weight percentage of graphene oxide (GO).The fabricated beads demonstrated controlled and sustained drug release, with 98 % of the loaded drug molecules released over 24 h at gastric pH. The antibacterial test using the disc diffusion technique confirmed the drug release, exhibiting greater effectiveness against the gram-positive bacterium S. aureus than the gram-negative bacterium E. coli. The drug release data were optimized using zero order, first order, Higuchi, and Korsmeyer-Peppas models. The experimental data were best fit to the Korsmeyer-Peppas model with a relatively higher correlation coefficient value. Biocompatibility was evaluated through a cell viability test using mouse fibroblast cell lines (L929). The MTT viability assay confirmed high levels of cytocompatibility, even at higher concentrations (100 μg/mL), with 98.15 % viable cells. These results highlight the potential of the fabricated beads as an effective amoxicillin drug delivery system with biomedical applications.

A Short-term solar irradiance forecasting modelling approach based on three decomposition algorithms and Adaptive Neuro-Fuzzy Inference System

In this study, a case study of four Indian cities i.e. Ajmer, Jaipur, Jodhpur and Kota in the state of Rajasthan are considered wherein 30 min ahead data have been obtained via the data site of the National Institute of Wind Energy and Wind Resource (NIWE) on which a proposed Global Horizontal Irradiance (GHI) prediction technique for all seasons is applied. Here, data has been pre-processed using three different signal decomposition algorithms in parallel i.e. Empirical Mode Decomposition (EMD), Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Ensemble Empirical Mode Decomposition (EEMD). Further, based on Pearson's Correlation Coefficient (PCC), corresponding IMFs & corresponding Residual obtained using the three decomposition algorithms are compared amongst each other respectively and that corresponding IMFs & corresponding Residual are chosen for signal reconstruction which are having highest correlation coefficient values. Thereafter, these selected IMFs and Residual from each algorithm are combined to form single input. In this way, three inputs formed from three decomposition algorithms based on the PCC values are fed to Adaptive Neuro-Fuzzy Inference System (ANFIS) for solar irradiance forecasting. The proposed technique shows significantly higher accurate results with less than 2% MAPE for different seasons considered at the site locations considered. Further, it terms of performance, the proposed technique is found to be independent of site location.

Mixture design optimization and machine learning-based prediction of Al-Mg alloy composite reinforced by Zn nanoparticles: A molecular dynamics study

Al-Mg alloy is widely used in various industries due to its practicality. It is crucial to achieve the highest possible tensile properties for this alloy. In this study, first, Zn nanoparticles were combined with the aluminum-magnesium alloy to modify their tensile properties. Then, a mixture design of experiment was utilized to find the best combination of Al-Mg-Zn alloy, which was validated through molecular dynamics simulation. Mechanical tests were also conducted on Al-1%Mg-1%Zn alloy at different temperatures and strain rates. The results suggested that when the temperature increases, the tensile properties decrease, while with higher strain rates they increase. Furthermore, four machine learning-based algorithms including Generalized Linear Model (GLM), Artificial Neural Network (ANN), Decision Tree (DT), and Random Forest (RF) were compared to determine the simplest and the most accurate one for the prediction of mechanical properties of Al-Mg-Zn alloy and due to the highest values of correlation coefficient, the ANN algorithm was found as the most accurate one for the prediction of mechanical properties. The GLM algorithm was also known as an appropriate algorithm to predict the mechanical properties. However, the DT and RF algorithms had low prediction precisions. Therefore, the parameters that were influential in the prediction precision of DT and RF algorithms were changed, and the best value of each parameter was obtained. It was found that the DT algorithm with a maximum depth of 6 has the highest precision prediction of mechanical properties. Furthermore, the RF algorithm had the highest accuracy for the prediction of mechanical properties with No. of trees = 4 and maximum depth = 4.

Adsorption property and kinetic studies of activated carbon fibers prepared from tissues by CO2 activation

Well-developed micro and mesoporous activated carbon fibers (ACFs) with fiber structure were prepared from tissue by CO2 activation. The XRD patterns and Raman spectrum indicated ACFs had a graphitic and amorphous structure. The SEM results indicated that the sample exhibited fiber structure with lots of mesoporous, with an average pore size of 2-5 nm. The specific surface area of 1517 m2 /g, micro surface area of 412.9 m2 /g, and total pore volume of 1.194 cm3 /g were obtained at 900°C with CO2 activation for 2 hours. ACFs showed relatively high methylene blue adsorption properties with an equilibrium adsorption capacity of 526 mg/g. The kinetic model of the Pseudo-second-order equation was more suitable for MB adsorption than the Pseudo-first-order equation and Intraparticle diffusion kinetics, with a high correlation coefficient value (R&gt;0.999). The present research provided a new idea of bionics for the manufacture of ACFs and brought forward a creative prospect for achieving energy-related CO2 emissions to net-zero and mitigating global warming.

Emerging Mesoporous Polyacrylamide/Gelatin–Iron Lanthanum Oxide Nanohybrids towards the Antibiotic Drugs Removal from the Wastewater

The polyacrylamide/gelatin–iron lanthanum oxide (P-G-ILO nanohybrid) was fabricated by the free radical grafting co-polymerization technique in the presence of N,N-methylenebisacrylamide (MBA) as cross linker and ammonium persulfate (APS) as initiator. The P-G-ILO nanohybrid was characterized by the various spectroscopic and microscopic techniques that provided the information regarding the crystalline behavior, surface area, and pore size. The response surface methodology was utilized for the statistical observation of diclofenac (DF) adsorption from the wastewater. The adsorption capacity (qe, mg/g) of P-G-ILO nanohybrid was higher (254, 256, and 258 mg/g) than the ILO nanoparticle (239, 234, and 233 mg/g). The Freundlich isotherm model was the best fitted, as it gives the higher values of correlation coefficient (R2 = 0.982, 0.991 and 0.981) and lower value of standard error of estimate (SEE = 6.30, 4.42 and 6.52), which suggested the multilayered adsorption of DF over the designed P-G-ILO nanohybrid and followed the pseudo second order kinetic model (PSO kinetic model) adsorption. The thermodynamic study reveals that adsorption was spontaneous and endothermic in nature and randomness onto the P-G-ILO nanohybrids surface increases after the DF adsorption. The mechanism of adsorption of DF demonstrated that the adsorption was mainly due to the electrostatic interaction, hydrogen bonding, and dipole interaction. P-G-ILO nanohybrid was reusable for up to five adsorption/desorption cycles.

Adsorption of anionic methyl orange dye by polyaniline, poly(o‐methylaniline) and poly(o‐methoxyaniline)

AbstractThis study reports the effect of substituents in the ortho position of polyaniline on the adsorption capacity to remove the anionic dye methyl orange (MO) from an aqueous solution. The aim of this study is the synthesis of polyaniline (PANI) and its derivatives, poly‐o‐methylaniline (poly‐o‐toluidine, POT) and poly‐o‐methoxyaniline (poly‐o‐anisidine, POA) for the adsorption removal of MO dye. All polymers were obtained by oxidative polymerization of the corresponding monomers and characterized by scanning electron microscopy (SEM) and infrared spectroscopy (IR). The average particle size of the polymer was about 200 nm. The effect of various parameters such as pH, temperature, adsorption time and initial concentration was analyzed. It was found that the adsorption capacity for dye removal increases from 50.68 to 222.56 mg g−1 for PANI, from 16.89 to 66.57 mg g−1 for POT, and from 97.26 to 532.54 mg g−1 for POA with an increase in the initial dye concentration from 5 up to 50 mg L−1. For all polymers, the equilibrium state of MO adsorption was reached in 50 min. The results showed that MO adsorption on PANI, POT, and POA is well described by a pseudo second order kinetic model. Isothermal studies have shown that adsorption is in good agreement with the Langmuir isotherm model, as evidenced by higher values of correlation coefficients. Based on the data of thermodynamic studies, it was concluded that MO adsorption on PANI, POT, and POA is spontaneous and endothermic.

ANTIOXIDANT POTENTIAL OF CORNELIAN CHERRY (Cornus mas L.) FROM MONTENEGRO

Abstract: In this work, total phenols, flavonoids, tannins, anthocyanins, the antioxidant capacity (by the DPPH and FRAP method) were determined in the juice and pomice of cornelian cherry from the area of Montenegro. Also, content of microelements and macroelements in the juice, pomice and fruit of cornelian cherry were investigated. In the studied cornelian cherry samples, the content of phenols ranged from 159.57 to 244.93 mg GAE/100 g, flavonoids from 71.74 to 165.5 mg QE/100 g, tannins from 0.33 to 0.81 %, total anthocyanins 0.057-0.310 %. HPLC analysis identified anthocyanins: delphinidin-3-O-glucoside, cyanidin-3-O-galactoside, cyanidin-3-O-glucoside, pelargonin-3-O-glucoside and its derivative. The most abundant essential microelement in the studied cornelian cherry samples was Fe (23.44-37.69 mg/kg), followed by Zn (3.14-6.25 mg/kg), Cu (1.50-1.80 mg/kg) and Mn (0.58-0.78 mg/kg). The most abundant essential macroelement in the examined samples was K (2427.8-3276.2 mg/kg), followed by Ca (361.5-475.7), Mg (95.41-146.5 mg/kg) and Na (8.347-16.59 mg/kg). Pb, Cd and Ni were not detected in the tested samples. A high degree of correlation was obtained between the content of total flavonoids and antioxidant value measured by the FRAP method (R = 0.96), as well as between the content of total anthocyanins and antioxidant activity measured by the DPPH method (R = 0.89). Of individual anthocyanins, high correlation values were shown by cyanidin-3-O-galactoside (R = 0.96), cyanidin-3-O-glucoside (R = 0.95) and pelargonin-3-O-glucoside (R = 0.94) with antioxidant activity measured by the DPPH method.

Evaluate the use of Mehlich-III compared to Mehlich-I as a soil test in Northeast Florida

Soil laboratories throughout the United States have widely integrated the Mehlich-III (M-III), derived from Mehlich-I (M-I), methodologies to extract soil nutrients. However, there have been concerns regarding its precision. The University of Florida's Hastings Agricultural and Extension Center (HAEC) conducted a soil sampling during the spring of 2021. This research aimed to rigorously compare the extraction efficiencies of M-I and M-III, specifically focusing on critical soil nutrients like phosphorus (P), potassium (K), magnesium (Mg), calcium (Ca), boron (B), copper (Cu), iron (Fe), manganese (Mn), and zinc (Zn). From forty-six meticulously chosen fields, 276 composite soil samples derived from two distinct depth strata (0–15 ​cm and 15–50 ​cm) were analyzed using both methods. Our findings highlighted the superior extraction prowess of M-III, which showcased an efficiency almost threefold compared to M-I. Intriguingly, the correlation between M-I and M-III extraction efficiencies weakened with escalating soil nutrient concentrations. The coefficient of determination (r2) between M-I and M-III had a high correlation value and was statistically significant (p<0.05) for all macro- and micronutrients. We found that the correlation between M-I and M-III became more robust with an increase in sample size and a higher correlation coefficient. It is noteworthy to mention that the M-III method has the propensity to provide slightly inflated soil nutrient estimations. Since soil labs worldwide use M-III equations derived from M-I, this study proposes that soil labs reinvestigate and possibly adopt new correlation equations to estimate soil nutrients using M-III.

Enhancement of adsorption capacity of Methylene blue, Malachite green, and Rhodamine B onto KOH activated carbon derived from oil palm empty fruit bunches

Enhancement adsorption capacity of Methylene blue (MeB), Malachite green (MaG), and Rhodamine B (RhoB) onto activated carbon derived from oil palm empty fruit bunches (Biochar). The activated carbon was produced by pyrolysis with impregnation in KOH solution. To enhance its efficiency, the activation process was performed twice, yielding the adsorbents 1KAC and 2KAC. The adsorption capacity and equilibrium of the three dyes onto the biochar and activated carbon samples were determined by examining the removal efficiency, capacity, kinetics and isotherm models. The pseudo-second-order model performed better than the pseudo-first-order model in terms of adsorption kinetics. The Langmuir model fits well for all three dye molecules with high correlation coefficient values, suggesting that the activated carbon surface is homogeneous and characterized by a finite number of identical sites. The maximum adsorption capacity was 1123.57, 1315.36, and 982.87 mg/g for MeB, MaG, and RhoB, respectively. This founding indicated a high potential adsorbent more than among KOH activated carbon reported in the literature. The study provides valuable insights into the use of activated carbon derived from oil palm empty fruit bunches for the removal of dyes from wastewater.