High Correlation Values Research Articles

Fatigue Life Assessment of API Steel Grade X65 Pipeline Using a Modified Basquin Parameter of the Magnetic Flux Leakage Signal.

This paper presents a modified fatigue life model of the Basquin equation using the stress parameter of the magnetic flux leakage signal. Most pipeline steels experience cyclic loading during service and the influence of the load history makes assessing fatigue behaviour more difficult. The magnetic flux leakage signal's response to a uniaxial cyclic test of API X65 steel was measured with eight levels of ultimate tensile stress loads. The influence of dH(y)/dx on fatigue failure was the main concern in this study, the aim being to represent localised stress parameters in the modified Basquin equation. Both fatigue lives, experimental and predicted from the modified Basquin equation, were validated through reliability analysis, producing a 60% value when approaching 1.8 × 105 cycles. The fatigue data from the experiment produced a higher mean-cycle-to-failure value than the prediction data, with slightly different values of 3.37 × 105 and 3.28 × 105. Additionally, the modified Basquin equation's predicted and the experimental fatigue lives were found to have a high R2 correlation value of 0.9022. The Pearson correlation also showed a good relationship between the fatigue lives, with an r value of 0.9801. Finally, the modified Basquin equation based on dH(y)/dx signals provided an accurate and alternative method for durability assessment.

Biosorption of Congo Red Dye from Aqueous Solution Using Adsorbent Prepared from Vangueria infausta Fruit Pericarp

The adsorption efficiency of acid-treated Vangueria infausta fruit pericarp (VIFP) biosorbent to uptake Congo red (CR) dye was investigated through batch experiments. Optimum conditions for the adsorption process were determined by varying the solution pH, biosorbent dosage, contact time, initial concentration of CR dye, and temperature. Results revealed that the data fits well with the Langmuir isotherm model, demonstrating the monolayer coverage of the CR dye molecule on the surface of the VIFP biosorbent. The higher value of correlation coefficient, [Formula: see text] for pseudo-second-order (0.9997) compared to [Formula: see text] of pseudo-first-order (0.9605) suggests that the pseudo-second-order kinetic model is an ideal fit in describing the kinetics of CR dye adsorption by VIFP biosorbent. The positive value of [Formula: see text] (41.42 kJ/mol) and [Formula: see text] (158.32 J/mol.K) depicts the endothermic nature and spontaneity of the biosorption of CR dye by VIFP. The values of [Formula: see text] obtained were negative, showing that the adsorption is thermodynamically favourable. Therefore, the acid-treated adsorbent from Vangueria infausta can efficiently be used in the removal of CR dye from aqueous solution.

Sulphur-doped carbon particles from almond shells as cheap adsorbent for efficient Cd(II) adsorption

One of the ways to increase the adsorption capacity of carbon-based materials is to add heteroatoms. However, the use of sulphur-doped carbons is quite limited. This study includes the production of activated carbon (AC) from almond shells by microwave heating and KOH chemical agent and sulphur doping by hydrothermal heating treatment of the obtained ACs with sulphuric acid for Cd(II) adsorption. The thermogravimetric/differential thermal analyser (TG-DTA), Fourier transforms infrared spectroscopy (FTIR), scanning electron microscope (SEM), energy dispersive spectroscopy (EDS) and nitrogen adsorption analyses were performed for characterization. For KOH-MA-S, the FTIR spectra peaks associated with the S O stretching vibration appeared at approximately 1153 cm −1 and 1024 cm −1 . The SEM image of the KOH + MA + S sample shows that a heterogeneous and layered structure is formed again due to the collapse or coalescence of the pores with the hydrothermal sulphuric acid activation on the carbonaceous surface. While there is almost no sulphur in the KOH-MA sample, there is approximately 9 % sulphur in the KOH-MA-S sample in the EDS results. The adsorption performance, kinetics, thermodynamics and mechanism for Cd(II) adsorption on sulphur doped ACs were evaluated. The isotherm and kinetic results showed that Cd(II) adsorption on sulphur-doped AC fits the Langmuir isotherm and pseudo-second-order kinetics with high correlation values. The adsorption capacity values (Qm) obtained for Cd(II) adsorption were 282.70 mg/g. • ACs based on almond shells by microwave heating with KOH activation were prepared. • Sulphur doped ACs by hydrothermal sulphuric acid treatment was prepared. • Cd(II) adsorption by sulphur doped AC was carried out. • Qm value for Cd(II) adsorption was 222.38 mg/g. • Adsorption mechanism of Cd(II) on sulphur doped AC was evaluated.

The Correlation between the Implementation of Patient-Centered Care (PCC) and the Fulfillment of Patient Rights at Regional General Hospital of Bireuen Regency

Introduction: Patient-Centered Care is an innovative healthcare model in which planning, implementation, and evaluation combine healthcare providers, patients, and families to provide better services. This study aimed to examine the relationship between implementing Patient-Centered Care and fulfilling patient rights in hospitals.Methods: This study used a cross-sectional research design. Data analysis was performed using the Chi-square test and Logistic Regression. The number of samples was as many as 104 people selected by proportional sampling.Results: All dimensions of Patient-Centered Care correlated with the fulfillment of patient rights (p<0.05). In addition, the logistic regression test showed that emotional support was the dimension with the highest correlation value (p = 0.012) with an odds ratio of 7.947. This indicates that patients with the correct dimensions of emotional support are 7.947 times more likely to have their rights fulfilled than patients who do not receive emotional support.Conclusion: The results of this study conclude that to fulfill the rights of patients in hospitals, a targeted and structured implementation is needed in providing patient-centered services to improve the quality of health services. Health care workers are urged to encourage patients and their families to participate in the care process actively.

Bio-diesel synthesis from waste sunflower oil: Experimental and multilayer perceptron neural network modeling

Transesterification being the most widely utilized way for biodiesel synthesis, the present work focuses on a single-step transesterification reaction between second-used sunflower oil and methanol as a reactant mixture with potassium hydroxide as a base catalyst. The parameter study exhibits encouraging results in terms of biodiesel/ ester conversion of the second used sunflower oil. Optimum parameters like temperature (62.3 °C), the molar ratio of methanol/ oil (1:5), and potassium hydroxide (2.85 wt%) were estimated for maximum biodiesel conversion (97.06 %). An artificial intelligence (AI) based formalism known as multilayer perceptron neural network (MLPNN) is utilized to develop the prediction model for biodiesel conversion (ml) (y0). The input space of the MLPNN-based model comprises of four operating parameters, viz. temperature (oC), second used sunflower oil (ml), methanol (ml), and reaction time (min). The proposed model is trained with the error-back propagation methodology. The high correlation coefficient (CCTraining=0.978;CCTest=0.999) and low root mean square error (RMSETraining=80.392;RMSETest=27.891) values indicate the excellent accuracy of prediction and generalization of the developed model. As a result, the proposed model can be conveniently engaged for further designing of biodiesel synthesis experimentation.

Evaluation of factors affecting university students' satisfaction with e-learning systems used dur-ing Covid-19 crisis: A field study in Jordanian higher education institutions

E-learning results from the integration of technology and education and has become an effective learning medium today. E-learning courses and systems with various services are on the rise owing to its importance. E-learning systems should be evaluated to assure successful delivery, effective usage, and positive impacts on learners. A holistic model that identifies various levels of success on a vast range of success determinants was proposed. The model was empirically validated using data obtained from 724 e-learning student users in Jordan. Structural Equation Modelling (SEM) was used in data analyses. Results showed that perceived usefulness of information systems, user training, system quality, and management support have positive effects on user’s behavioral intention; whereas perceived ease of use has not. Also, SEM displayed that user behavioral intention has a positive effect on information systems use, use on student satisfaction, and the latter on student loyalty. Machine Learning (ML) methods produce high correlation values reaching up to 80% in predicting Behavior Intention (BI) from the input factors, and student loyalty from student satisfaction factors. This indicates that the ML are promising techniques to forecast the future targets based on the input independent features.

Novel Surrogate Measures Based on a Similarity Network for Neural Architecture Search

We propose two novel surrogate measures to predict the validation accuracy of the classification produced by a given neural architecture, thus eliminating the need to train it, in order to speed up neural architecture search (NAS). The surrogate measures are based on a solution similarity network, where distance between solutions is measured using the binary encoding of some graph sub-components of the neural architectures. These surrogate measures are implemented within local search and differential evolution algorithms and tested on NAS-Bench-101 and NAS-Bench-301 datasets. The results show that the performance of the similarity-network-based predictors, as measured by correlation between predicted and true accuracy values, are comparable to the state-of-the-art predictors in the literature, however they are significantly faster in achieving these high correlation values for NAS-Bench-101. Furthermore, in some cases, the use of these predictors significantly improves the search performance of the equivalent algorithm (differential evolution or local search) that does not use the predictor.

Establishment of empirical formulae for hydrodynamic derivatives of submarine considering design parameters

The hydrodynamic derivatives are necessary for assessing the dynamic characteristics, such as dynamic stability and maneuverability which are crucial in evaluating navigation safety and operational efficiency. Hence, it is required to compute the hydrodynamic derivatives precisely. This study nominates the new empirical formulae for predicting the hydrodynamic derivatives of a submarine. The proposed empirical formulae are derived from hydrodynamic forces and moments which are measured using Computational Fluid Dynamic (CFD) approach. Because the BB2 generic submarine is designed with an appropriate geometry and outstanding features, especially directionally stability, so, it is designated to establish the empirical formulae. The design parameters that have a significant impact on the maneuverability of the BB2 submarine, such as the length-to-diameter ratio, sail position, and sail height, are altered to fit different types of submarines for various purposes. Then, hydrodynamic derivatives of each change factor of the design parameter are taken using the least square method and assessed in relationship with the design parameters through correlation analysis. The high correlation values determine the independent variables which are the design parameters to form the empirical formulae for each hydrodynamic derivative based on multiple regression analysis. The applicability of the established empirical formulae is confirmed by applying them to calculate the hydrodynamic derivatives of BB2 and 2000 tons submarines, and comparing the calculation with the available data. The high precision implies that the established empirical formulae can be used to predict the hydrodynamic derivatives of similar profile submarines to the BB2 and 2000 ton submarines, and they can be extended to submarines in general at the designing phase.

Statistical Downscaling Menggunakan Pengelompokan Expectation-Maximization pada Data CFSRv2

General Circulation Model (GCM) is a numerical model that produce a number of data from various climate parameters that are used to estimate climate, one of which is precipitation. GCM is a global scale data and has high resolution. So, the GCM cannot consider the local-scale areas with a higher resolution than the GCM. Therefore, to be able to use the GCM for estimating the local rainfall, namely Statistical Downscaling (SD). SD is a technique used to get the relationship between global-scale (GCM) and local-scale data. SD use the GCM which consist of dependent variables that have multicollinearity. So in this research, the principal component regression (PCR) and partial least square regression (PLSR) will be used to reduce the multicollinearity. In addition, to reduce the RMSEP and increase the correlation value, a clustering technique will be applied before modeling, namely Expectation-Maximization (EM) clustering. This research use CFSRv2 data as GCM and local rainfall data at four rainfall stations in West Java (January 2011 to December 2017). Based on this research, PCR is a good modeling than PLSR and EM clustering get the lower RMSEP and higher correlation value than without clustering before modeling. The conclusion is PCR with EM clustering is a good method for estimating local rainfall using the SD technique especially rainfall in West Java and CFSRv2 data.

Influential biosorption of lead from aquatic solution using Escherichia coli/carbon nanofibers

The study was conducted for the biosorption of Pb(II) from aquatic solution treated with Escherichia coli (E. coli) waste biomass immobilized with carbon nanofibers. Physicochemical characteristic studies were conducted by applying FTIR, SEM, and XRD. The study for biosorption of Pb(II) were optimized by different experimental parameters, contact pH 2–7, temperature 20–60 °C, contact time 10–50 min, amount of E. coli/carbon nanofibers(CNFs) composite biosorbent 4–16 g/L, and initial Pb(II) solution 1–20 mg/L. The factorial statistical design was also selected to evaluate parameters performance and define the relationships among factors and optimum response. Based on the equilibrium study, high regression correlation coefficient value (R2 = 0.995) was obtained for the non-linear Langmuir model and was best fitted for biosorption of Pb(II) by E. coli/ CNFs composite with a sorption capacity of 166.3 mg g−1. Experimental data for the kinetic model was best fitted with a pseudo-second-order model with a high correlation coefficient, R2 = 0.996. The binding mechanism of Pb(II) to the composite was characterized by some physicochemical attractions such as the electrostatic force of interaction, metal ion chelation, and ion exchange. The study revealed that E. coli/CNFs composite was an effective biosorbent to remove Pb(II) ions from aquatic solution.

Improvement of sediment yield index model through incorporating rainfall erosivity.

Soil erosion and sediment yield in watersheds are comprehensively affected by land use/cover changes and climatic factors. The current sediment yield index (SYI) model incorporates parameters of area (A), delivery ratio (DR), and curve number (CN), which reflect the character of underlying surface conditions, while the impact of rainfall intensity on sediment yield could not be properly considered. This study aims to improve the current SYI model by introducing rainfall-related factors such as rainfall erosivity (R) and applying it to estimate the sediment yield of river basin. Taking the Dongjiang River basin, South China, as a case study, the performances of the improved simplified SYI model (SYI-CN + R) were compared and demonstrated at multi-spatiotemporal scales. The results showed that (1) compared with the SYI model which only has the parameter CN (SYI-CN), the model SYI-CN + R achieves better simulation performances at yearly (the efficiency coefficient (CE) is 81% in the whole basin and 62% in the sub-basin) and half-month (CE is 69% in the whole basin and 57% in the sub-basin) time scales. (2) On the basin scale, the simulation performance in the whole basin is better overall compared to that in the sub-basin, and the model SYI-CN + R at the half-month time scale is more suitable for the sediment yield simulation in the Dongjiang River basin, with higher value of correlation coefficient (CC) of 87% and 83% for the whole basin and the sub-basin, respectively. And (3) the values of CN and R have an obvious spatial gradient in the whole basin, showing an increasing trend from northeast to southwest as a whole, with larger values concentrated in the lower reaches and smaller values in the middle and upper reaches. This study extends the application and improves the performance of the SYI model, and provides a basis for soil and water conservation in a river basin with fewer observation data.

Pattern recognition: the effect of exercise performance modes on the effectiveness of teaching the vault to 8-year-old boys

Background and Study Aim. The study purpose was to determine the effect of exercise performance modes on the effectiveness of teaching the vault to 8-year-old boys. 
 Materials and methods. The study participants were 32 boys aged 8 who were divided into 4 groups of 8 pesons. The children and their parents were informed about all the features of the study and gave their consent to participate in the experiment. The pedagogical experiment investigated the effect of the number of sets (х1), the number of repetitions in a set (х2), and a 60-second rest time on the change in the gain in the level of proficiency of 8-year-old boys in straddle vault over the buck. A method of algorithmic instructions was used in the training. The participants proceeded to the next exercise after three successful attempts.
 Results. Statistical analysis of the significance of the discriminant functions showed that the first function explains 81.2% of the data variation and has a high canonical correlation value (r = 0.751). The first function has a high discriminative ability (λ = 0.332, p < 0.021) and can be used to classify the modes of physical exercise performance in the process of formation of motor skills.
 Conclusions. The use of multivariate statistics makes it possible to determine the structure of the training program, confirm the effectiveness of the selection of motor tasks, and classify the modes of physical exercise performance during the training process. The best mode of physical exercise performance in teaching the vault to 8-year-old boys is 12 sets of 3 repetitions with a rest time of 60 seconds.

Correlation of teacher extrinsic factors on teacher performance during online learning in Aceh Besar

Online learning that is carried out simultaneously affects the world of education. The extrinsic and intrinsic factors of the teacher's personality become one of the problem factors in the learning process. These factors also affect the performance of teachers during the online learning process. This study aims to see the correlation between various extrinsic factors of teachers and teacher performance in online learning. This research activity was carried out from June 2020 to March 2021. The objects in this study were the teachers who teach at SMA N 1 Krueng Baroena Jaya and SMP Abulyatama. Various kinds of problems occur during the online learning process. Extrinsic factors that have a very high correlation value are found in the teacher and student categories. The correlation that shows a positive relationship to teacher problems that affect teacher performance during the online learning process is on the factors of age, income, and employment status.

Using the Stiffness Indicators of 110 and 220 kV Electric Network Nodes to Estimate Short-Circuit Currents

The topical problem of evaluation, analysis and limitation of short-circuit currents in electrical networks by simple and accessible methods without resorting to rather complex conventional calculations is addressed. The article suggests a new procedure for estimating short-circuit currents at network nodes based on their stiffness degree determined by the values of electric power system (EPS) configuration generalized parameters. The node balance admittance defined as the difference between the nodal admittance matrix diagonal and non-diagonal elements is used as the node stiffness indicator. The problem solution adequacy is confirmed by examples of constructing stochastic models for estimating short-circuit currents at the 110 kV networks nodes in the regional EPS and 220 kV nodes in the Moscow power system. The obtained stochastic models feature high values of correlation coefficients. This opens the possibility to determine (predict) average and interval values of short-circuit currents at network nodes, as well as the probability of their going beyond the established boundaries, which is important for short-circuit current limitation measures, including enhancement of the EPS configuration reliability.

Modeling of monthly rainfall analysis in Aceh Besar District with Indonesia’s Sea-Surface Temperatures as the primary component

Abstract. There are various fields that crucially require information about rainfall, including agriculture. A major drop or rise in rainfall may significantly impact the agriculture in Indonesia. For this reason, research regarding on variables that effected rainfall are still being improved by applying variety of models in order to provide accurate and closely related to the original data on rainfall forecast. Precise and accurate rainfall forecast is essential to help extension workers and farmers to determine when to start planting their crops and to decide what kind of crops are suitable for a particular region. Data applied in this research are data on monthly Sea-Surface Temperature (SST) data which are limited by 10°N – 15°S and 90°E – 145°E with a 1° x 1° grid, as well as data on monthly rainfall in Aceh Besar district which were obtained by averaging the data from 10 rain post points in collaboration with the BMKG of Aceh Besar District between 2010 – 2019. Correlation analysis between Sea-Surface Temperature (SST) and rainfall resulted in correlation value that fulfill the requirement of “sufficient” to “high” in a number of regions for each month. The highest correlation value was recorded by 0.87 for the positive correlation in October, and it was recorded by -0.87 for the highest negative value of correlation in December. Under those circumstances, by utilizing the Principal Component Regression (PCR) method, a forecasting model is created for each of the region with the highest correlation which allows for the identification of the area that most impacted rainfall in district of Aceh Besar. Model validation obtained error value of 37.1 or roughly around 19.5%. The highest value difference was occurred in October 2014, measuring by the value difference of 96 mm. The results of monthly rainfall forecast in 2021 provide a pattern of rainfall that is similar with the observation data obtained in August to December. Research results indicate that sea-surface Temperature (SST) in regions of Indonesia can be utilized as the indicator in forecasting rainfall at a district level.

Investigation of the Anticancer Potential of 2-alkoxycarbonylallyl Esters Against Metastatic Murine Breast Cancer Line 4T1 Targeting the EGFR: A Combined Molecular Docking, QSAR, and Machine Learning Approach

Background:The search for novel and potent anticancer drugs is imperative. This present study aims to unravel the mechanisms of action of 2-alkoxyecarbonyl esters using robust model(s) that can accurately predict the bioactivity of novel compounds. Twenty-four potential anticancer 2- alkoxycarbonylallyl ester compounds obtained from the literature were employed in building a 3D-QSAR model.Objectives:The objective of this study is to determine the predictive ability of the GFA-based QSAR models and extreme machine learning models and compare them. The lead compounds and newly designed compounds were docked at the active site of a human epidermal growth factor receptor (EGFR) kinase domain to determine their binding modes and affinity.Methods:QikProp program and Spartan packages were employed for screening compounds for druglikeness and toxicity. QSAR models were equally used to predict the bioactivities of these molecules using the Material Studio package. Molecular docking of the molecules at the active site of an EGFR receptor, 1M17, was done using Auto dock tools.Results:The model of choice, with r2pred (0.857), satisfied the recommended standard for a stable and reliable model. The low value of r2, Q2 for several trials and cRp2 (0.779 ≥ 0.5) and the high value of correlation coefficient r2 for the training set (0.918) and test set (0.849) provide credence to the predictability of the model. The superior inhibition of EGFR displayed by the lead compounds (20 and 21) with binding energies of 6.70 and 7.00 kcalmol-1, respectively, is likely due to the presence of double bonds and α-ester groups. ADMET screening showed that these compounds are highly druggable. The designed compounds (A and B) displayed better inhibition of EGFR.Conclusion:The QSAR model used here performed better than the Random Forest Regression model for predicting the bioactivity of these anticancer compounds, while the designed compounds (A and B) performed better with higher binding affinity than the lead compounds. Implementing the developed model would be helpful in the search for novel anticancer agents.

COVID-19 research spread in Indonesia on social media

Introduction. The purpose of this study is to determine the impact of Indonesian COVID-19 research. One of them is productive author, productive citations, the most popular social media to disseminate research results, the most popular articles on social media, mapping or distribution of research results visualization, and the correlation of social media data with citations. 
 Data Collection Method. The research data was taken from the Scopus database and this research use method Bibliometric, Altmetric, Scientometric, and linear regression tests.
 Data Analysis. The data of the study was analyzed using program (altmetric.com), VOSViewer and SPSS software.
 Result and Discussion. Pranata, R. was the productive author in producing Indonesian COVID-19 research. The University of Indonesia is the most productive institution. The study "Clinical, laboratory, and imaging features of COVID-19: A systematic review and meta-analysis" received the most citations and shares on social media. Mendeley is the most popular social media platform for disseminating research. 
 Conclusion. Based on the above findings, the spread of COVID-19 research in Indonesia on the most popular social media is mendeley 17.701, followed by Twitter 2.971 and news mentions 177. According to linear regression tests, mendeley is social media platform with a high correlation value of 0.814.

Prediction of Pathological Tremor Signals Using Long Short-Term Memory Neural Networks.

Previous implementations of closed-loop peripheral electrical stimulation (PES) strategies have provided evidence about the effect of the stimulation timing on tremor reduction. However, these strategies have used traditional signal processing techniques that only consider phase prediction and might not model the non-stationary behavior of tremor. Here, we tested the use of long short-term memory (LSTM) neural networks to predict tremor signals using kinematic data recorded from Essential Tremor (ET) patients. A dataset comprising wrist flexion-extension data from 12 ET patients was pre-processed to feed the predictors. A total of 180 models resulting from the combination of network (neurons and layers of the LSTM networks, length of the input sequence and prediction horizon) and training parameters (learning rate) were trained, validated and tested. Predicted tremor signals using LSTM-based models presented high correlation values (from 0.709 to 0.998) with the expected values, with a phase delay between the predicted and real signals below 15 ms, which corresponds approximately to 7.5% of a tremor cycle. The prediction horizon was the parameter with a higher impact on the prediction performance. The proposed LSTM-based models were capable of predicting both phase and amplitude of tremor signals outperforming results from previous studies (32--56% decreased phase prediction error compared to the out-of-phase method), which might provide a more robust PES-based closed-loop control applied to PES-based tremor reduction.

ANALISIS FAKTOR-FAKTOR BAURAN PEMASARAN YANG MENENTUKAN KEPUTUSAN KONSUMEN DALAM MEMILIH MINIMARKET UNTUK BERBELANJA DI DESA UNGASAN

The increasing number of minimarkets in Ungasan Village causing the income of grocery store owners decreases, that makes grocery stall owners should implement a marketing mix strategy to be compete, by studying the behavior of minimarket consumers. This study aims to analyze the marketing mix factors that determine consumer decisions in choosing a minimarket to shop in Ungasan Village and find out the dominant factors so the results is expected to help grocery store owners in Ungasan Village. The stages of this study including preliminary survey, attribute identification based on marketing mix factors, questionnaire generating, reliability and validity testing, broadcasting the questionnaire to the consumers of grocery store, data gathering, data analyzing, and generating the results and the discussions. The study implemented by surveying 154 respondents, then from the results a factor analysis carried out using Microsoft Excel 2016 and SPSS 26. The results show that there are five factors betwixt accessibility factor, employee performance factor, promotion factor, physical evidence factor, and price factor. The accessibility factor is the dominant factor that determines consumer decisions in choosing a minimarket for shopping, with the attribute that has the highest correlation value is that the distance of the mini market is close to the dwelling (X8).

Removal of Heavy Metals (Pb2+, Zn2+, Cu2+, Hg2+, Co2+, Cr2+ and Cd2+) From Water by Trimethyl Phenyl Ammonium Cation (TMPA+) Based Organoclay: Experimental and Statistical Test

In this study, trimethyl phenyl ammonium cation (TMPA+) intercalated bentonite was used as an adsorbent for adsorptive removal of seven heavy metal ions namely: Pb (II), Zn (II), Cu (II), Hg (II), Cr (II), Co (II) and Cd (II) in aqueous solution via batch adsorption process. The developed adsorbent, ab initio has been characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FTIR). The operational parameters evaluated on the adsorption efficiency were solution pH, initial metal ions concentration, temperature, adsorbent dosage, and contact time. The adsorption of Pb (II), Zn (II), Cu (II), Hg (II), Cr (II), Co (II) and Cd (II) ions were found to have attained equilibrium positions in 120 min for the concentration range tested, respectively. Two generally applied linearized adsorption isotherm models; Langmuir and Freundlich tested, when compared, revealed that Langmuir isotherm fitted well to the experimental data judging from the higher correlation coefficient values (R2 ) and lower values of the error functions (chi-square (χ2), the sum of square error (ERRSQ/SSE) and the sum of absolute error (EABS)) with monolayer adsorption capacities of 17.66, 21.12, 14.64, 14.98, 16.60, 13.81 and 15.50 mg/g for Pb (II), Zn (II), Cu (II), Hg (II), Cr (II), Co (II) and Cd (II) ions, respectively. Adsorption kinetic models investigated by pseudo-first-order and pseudo-second-order showed the conformity of pseudo-second-order model to the process of adsorption as informed by the higher values R2 and Adj, R2 , maximum log-likelihood and smaller ERRSQ/SSE, χ2, Akaike information criterion (AIC), Bayesian information criterion (BIC), and Hannan-Quinn information criterion (HQIC). Result from kinetic data revealed the adsorption mechanism of Pb (II), Zn (II), Cu (II), Hg (II), Cr (II), Co (II) and Cd (II) ions onto Bt-TMPA as a chemical adsorption.