Coefficient Of Determination R2 Research Articles

Application of artificial intelligence based on the fuzzy control algorithm in enterprise innovation

Artificial Intelligence (AI) has gained immense popularity in recent years as many enterprises have realized their potential to change the way of conducting business innovatively. The new concepts, items, or procedures are developed and implemented within a business or organization to enhance productivity, effectiveness, and competitiveness, and this is called Enterprise Innovation (EI). AI techniques are required to make decisions more effectively in challenging and dynamic situations, like EI, as result of competitive marketplace. Hence, an intelligent, innovative strategy with Q-learning and Takagi Sugeno Fuzzy Control (Q-TSFC) algorithm has been proposed as it combines adaptive learning and Fuzzy Logic (FL) that humans understand to improve decision-making in enterprise innovation. Q-learning seeks to maximize the enterprise's profit by utilizing the newly acquired knowledge, exploring activities, and adaptive learning based on the optimal ε greedy policy that results with rewards and the experiences. To develop a framework for making decisions and connections between input from the learned Q values and output decisions using enterprise expertise and linguistic conventions. The objective is to handle language uncertainty and imprecision in market trend. So, it leads to right decisions even without accurate numerical facts. The proposed approach is validated by evaluating metrics like cost savings, customer satisfaction, and innovation performance efficiency in the competitive edge in the market. With this proposed Q-TSFC algorithm, the obtained results are 96.5% customer satisfaction ratio, 96% enterprise performance efficiency, cost savings of about 48% profitable value, and the coefficient of determination R2 is 0.83, respectively.

Development and evaluation of relative QA dosimeter for electron beam based on CsPbBr3

Quality assurance (QA) of electron beams must be performed regularly to ensure accurate radiation therapy. Therefore, a QA dosimeter for electron beams is required for accurate electron beam QA measurement. In this study, the electron beam detection performance of CsPbBr3 material was measured. And it was evaluated whether it could be applied as a QA dosimeter for electron beams. To evaluate feasibility, reproducibility, linearity, and PDD were measured, and evaluated at 6 MeV, 9 MeV, and 12 MeV energies of the linear accelerator. The reproducibility evaluation results showed relative standard deviations of 1.06% at 6 MeV, 1.39% at 9 MeV, and 1.49% at 12 MeV. Linearity result evaluation, the coefficient of determination R2 was presented as 0.9996, 0.9994, and 0.9992 at energies of 6, 9, and 12 MeV, respectively. When PDD was measured using the manufactured dosimeter, Dmax points for each energy band of 6, 9, and 12 MeV were shown. The CsPbBr3 relative dosimeter produced in this study shows detection performance that satisfies the evaluation criteria of reproducibility, linearity, and PDD. These results mean that the CsPbBr3 dosimeter is feasible as a QA dosimeter dedicated to electron beams.

Ensemble machine learning reveals key features for diabetes duration from electronic health records.

Diabetes is a metabolic disorder that affects more than 420 million of people worldwide, and it is caused by the presence of a high level of sugar in blood for a long period. Diabetes can have serious long-term health consequences, such as cardiovascular diseases, strokes, chronic kidney diseases, foot ulcers, retinopathy, and others. Even if common, this disease is uneasy to spot, because it often comes with no symptoms. Especially for diabetes type 2, that happens mainly in the adults, knowing how long the diabetes has been present for a patient can have a strong impact on the treatment they can receive. This information, although pivotal, might be absent: for some patients, in fact, the year when they received the diabetes diagnosis might be well-known, but the year of the disease unset might be unknown. In this context, machine learning applied to electronic health records can be an effective tool to predict the past duration of diabetes for a patient. In this study, we applied a regression analysis based on several computational intelligence methods to a dataset of electronic health records of 73 patients with diabetes type 1 with 20 variables and another dataset of records of 400 patients of diabetes type 2 with 49 variables. Among the algorithms applied, Random Forests was able to outperform the other ones and to efficiently predict diabetes duration for both the cohorts, with the regression performances measured through the coefficient of determination R2. Afterwards, we applied the same method for feature ranking, and we detected the most relevant factors of the clinical records correlated with past diabetes duration: age, insulin intake, and body-mass index. Our study discoveries can have profound impact on clinical practice: when the information about the duration of diabetes of patient is missing, medical doctors can use our tool and focus on age, insulin intake, and body-mass index to infer this important aspect. Regarding limitations, unfortunately we were unable to find additional dataset of EHRs of patients with diabetes having the same variables of the two analyzed here, so we could not verify our findings on a validation cohort.

Correlation between Turbidity and Inherent Optical Properties as an Initial Recognition for Backscattering Coefficient Estimation

Seawater turbidity is a common water quality indicator measured in situ and estimated from space on a regular basis. However, it is rarely correlated with the inherent optical properties of seawater, which convey information about seawater composition. In this study, we show a simple application of the turbidimeter’s weighting function in the estimation of the backscattering coefficient of a model inorganic suspension in seawater. First, we introduce a method to measure the instrument’s weighting function which describes the sensor’s angular response in terms of scattering angles. The determination of the sensor-specific weighting function led us to characterize its angular sensitivity to the presence of suspended particles. The highest sensitivity for the Seapoint turbidimeter is in the range of 114°–128° (containing 25% of the total signal). Next, we describe the correlations between turbidity and the scattering and backscattering coefficients on the example of the model of inorganic particle suspension using the calculations based on Mie theory. The correlations are analyzed for narrow size fractions of the particle size distribution of silica in the range of 0.59–190 µm. We established that there is a good linear correlation (characterized by the coefficient of determination r2 = 0.979) between the part of the scattering coefficient measured by the turbidimeter and the backscattering coefficient of all size fractions of the model inorganic suspension.

Sub-ppb detection limit of Cr, Pb, and Cu in water by surface-enhanced LIBS with femtosecond laser

Water pollution caused by heavy metal contamination significantly threatens human health. Detecting trace levels of heavy metals in water is of utmost importance. Surface-enhanced laser-induced breakdown spectroscopy (SE-LIBS) has become a reliable detection method. In this study, we improved the analytical sensitivity of trace heavy metals in water by introducing femtosecond lasers into the SE-LIBS technique, replacing the traditional nanosecond lasers. An aluminum plate was utilized as a substrate to transform the liquid into a solid, overcoming the inadequacy in sensitivity and precision of LIBS when applied to water samples. We developed individual calibration curves for Cr, Pb, and Cu to determine their respective limit of detection (LOD). The LODs obtained were 0.3, 2.5, and 0.08 ng/mL for Cr, Pb, and Cu, respectively. These LODs were lower than traditional nanosecond SE-LIBS, and even lower than the LODs obtained by combining other LIBS enhancement techniques and SE-LIBS. We also determined good accuracy through the coefficient of determination R2, which was 0.97, 0.99, and 0.97, respectively, for Cr, Pb, and Cu. These results provide valuable and insightful information for the sensitive analysis of trace heavy metals in water using SE-LIBS with a femtosecond laser.

THE INFLUENCE OF TRAINING AND COMPENSATION ON EMPLOYEE PERFORMANCE IN WILD TIGER PRAWN FARMS IN TANGGAMUS

Human resources play a crucial role in the success of a company. Therefore, it is essential for companies to provide effective training and fair compensation to enhance employee performance and ensure the smooth functioning of the organization. The objective of this study is to examine the impact of training and compensation on employee performance in windu alam tiger prawn farm. The research sample consisted of 30 employees, and the entire population was included using Non-Probability Saturation sampling technique. Data collection was done through a questionnaire system, and the analysis was conducted using multiple linear regression with the assistance of SPSS version 25. Various tests such as validity, reliability, normality, multicollinearity, and heteroscedasticity were performed to analyze the data. The hypothesis testing included simultaneous, partial, and R2 determination coefficient tests. The findings revealed that Training (X1) has a positive influence on Employee Performance (Y), and Compensation (X2) also has a positive impact on Employee Performance (Y). Moreover, the study concluded that both Training and Compensation have a positive effect on employee performance. This research holds both theoretical and practical implications, contributing to the field of human resource management and providing valuable insights for companies in making informed decisions.

Quantitative analysis of pyrolysis characteristics and chemical components of tobacco materials based on machine learning.

To investigate the quantitative relationship between the pyrolysis characteristics and chemical components of tobacco materials, various machine learning methods were used to establish a quantitative analysis model of tobacco. The model relates the thermal weight loss rate to 19 chemical components, and identifies the characteristic temperature intervals of the pyrolysis process that significantly relate to the chemical components. The results showed that: 1) Among various machine learning methods, partial least squares (PLS), support vector regression (SVR) and Gaussian process regression (GPR) demonstrated superior regression performance on thermogravimetric data and chemical components. 2) The PLS model showed the best performance on fitting and prediction effects, and has good generalization ability to predict the 19 chemical components. For most components, the determination coefficients R 2 are above 0.85. While the performance of SVR and GPR models was comparable, the R 2 for most chemical components were below 0.75. 3) The significant temperature intervals for various chemical components were different, and most of the affected temperature intervals were within 130°C-400°C. The results can provide a reference for the materials selection of cigarette and reveal the possible interactions of various chemical components of tobacco materials in the pyrolysis process.

Reclamation of iron and copper from BCL slag in Botswana

High-grade copper ores have been depleted over the years, making it a challenge in the mining industry. This investigation focused on a methodology to recover iron (Fe) and copper (Cu) from a copper/nickel slag obtained from the Bamangwato Concession Limited (BCL) mine in Botswana. In this modified flotation approach, the Response Surface Methodology (RSM) was used in conjunction with the Central Composite Design (CCD) and Analysis of Variance (ANOVA) to obtain the best optimal flotation conditions for the recovery of iron and copper. Using the RSM – CCD methodology, the optimal predicted responses were illustrated by a coefficient of determination R2 = 0. 9839 for recovery for Fe and 0.9655 for recovery for Cu. The recovery of copper increased with the increasing dosage of Na2S and collector dosage, while the increase of pH, had a decrease in recovery of copper due to the decline in the stability of the froth, which led to the resistance to form stable bubbles for efficient recovery of copper. Selective flotation of copper and iron was achieved by varying the Na2S dosage to achieve maximum recovery. Under these flotation conditions of PAX (800 g/t), pH (8), −75 μm, sulfurizing agent (Na2S, 1000 g/t), flotation time of 8 min, pH regulator of NaOH and H2SO4 and Methyl Isobutyl Carbinol (MIBC) from the experimental runs merited a grade upgrade of Cu in froth concentrate from 0.581 mass% to 0.884 mass%. An enrichment ratio of 2 was realised, with the recovery of Cu being 62%, whereas Fe in the froth concentrate increased from 69.8 mass% to 71.8 mass%. The main aim was to upgrade the grade and recovery of copper and iron to enhance the recovery for copper and iron in the next experimental stage of leaching.

THE DIVERSITY AND ABUNDANCE OF CHAETODONTIDAE FAMILY AND ITS RELATIONSHIP WITH LIVE CORAL COVER IN SPERMONDE ISLANDS, MAKASSAR

Coral fish are one of the marine biotas that are highly sensitive to changes in coral reef ecosystems. Therefore, the presence or absence of specific reef fish species can describe the condition of coral reefs. The objective of the current study is to investigate the relationship between the biodiversity and abundance of Chaetodontidae fish and the live coral cover at 13 locations in the Spermonde area, Makassar City. Reef fish were counted using the Underwater Visual Census method. Coral cover percentages were determined using the Underwater Photo transects method. The results found 348 individuals of the Chaetodontidae family, comprising 21 species from four genera. The mean live coral cover categories in Spermonde were in pretty good condition (27.83%). Chaetodontidae in Spermonde Island was not diverse (mean H = 1.74) and not abundant (mean abundant = 721 ind./ha). There was a weak correlation between the diversity of Chaetodontidae and live coral cover, and also weak correlation between the abundance of Chaetodontidae and live coral cover in Spermonde Island, with R2 determination coefficient linear regression were 0.1697 and 0.0471, respectively.

Quantitative characterization of tensile stress in electroplated nickel coatings with a magnetic incremental permeability sensor

Quantitative characterization of tensile stress in electroplated nickel coatings on the copper substrate with the thickness range of 2.809 µm to 35.654 µm in the elastic stage was experimentally performed with a magnetic incremental permeability (MIP) sensor in the study. Firstly, a differential mutual-inductance sensor based on the parallel excitation of bifilar co-directional coil was proposed. With the proposed sensor, multiple signals including high-frequency eddy current (HFEC), tangential magnetic field strength, and magnetic incremental permeability butterfly curve (MIPBC) could be measured synchronously and eight typical magnetic parameters were obtained from experimental signals. Then, four parameters including the peak height of MIPBC, amplitude at cross point of MIPBC, difference frequency of HFEC, and sum frequency component of HFEC were selected to quantitatively characterize tensile stress. Experimental results showed that the four parameters depended on tensile stress and showed a parabolic trend. The parameters of the parabolic equation varied with nickel coating thickness. Finally, a polynomial surface fitting method based on the combination of the factors of thickness and tensile stress were derived with the selected four parameters and the coefficient of determination R2 was higher than 0.9. The further comparison between theoretical results and predicted stress confirmed that the proposed MIP sensor could realize the quantitative detection of tensile stress in different nickel coatings.

3D-DXA Based Finite Element Modelling for Femur Strength Prediction: Evaluation Against QCT.

Osteoporosis is characterised by the loss of bone density resulting in an increased risk of fragility fractures. The clinical gold standard for diagnosing osteoporosis is based on the areal bone mineral density (aBMD) used as a surrogate for bone strength, in combination with clinical risk factors. Finite element (FE) analyses based on quantitative computed tomography (QCT) have been shown to estimate bone strength better than aBMD. However, their application in the osteoporosis clinics is limited due to exposure of patients to increased X-rays radiation dose. Statistical modelling methods (3D-DXA) enabling the estimation of 3D femur shape and volumetric bone density from dual energy X-ray absorptiometry (DXA) scan have been shown to improve osteoporosis management. The current study used 3D-DXA based FE analyses to estimate femur strength from the routine clinical DXA scans and compared its results against 151 QCT based FE analyses, in a clinical cohort of 157 subjects. The linear regression between the femur strength predicted by QCT-FE and 3D-DXA-FE models correlated highly (coefficient of determination R2 = 0.86) with a root mean square error (RMSE) of 397 N. In conclusion, the current study presented a 3D-DXA-FE modelling tool providing accurate femur strength estimates noninvasively, compared to QCT-FE models.

Machine learning-based techniques for land subsidence simulation in an urban area

Understanding and mitigating land subsidence (LS) is critical for sustainable urban planning and infrastructure management. We introduce a comprehensive analysis of LS forecasting utilizing two advanced machine learning models: the eXtreme Gradient Boosting Regressor (XGBR) and Long Short-Term Memory (LSTM). Our findings highlight groundwater level (GWL) and building concentration (BC) as pivotal factors influencing LS. Through the use of Taylor diagram, we demonstrate a strong correlation between both XGBR and LSTM models and the subsidence data, affirming their predictive accuracy. Notably, we applied delta-rate (Δr) calculus to simulate a scenario with an 80% reduction in GWL and BC impact, revealing a potential substantial decrease in LS by 2040. This projection emphasizes the effectiveness of strategic urban and environmental policy interventions. The model performances, indicated by coefficients of determination R2 (0.90 for XGBR, 0.84 for LSTM), root-mean-squared error RMSE (0.37 for XGBR, 0.50 for LSTM), and mean-absolute-error MAE (0.34 for XGBR, 0.67 for LSTM), confirm their reliability. This research sets a precedent for incorporating dynamic environmental factors and adapting to real-time data in future studies. Our approach facilitates proactive LS management through data-driven strategies, offering valuable insights for policymakers and laying the foundation for sustainable urban development and resource management practices.

Near-infrared quality monitoring modeling with multi-scale CNN and temperature adaptive correction

Considering the impact of temperature on near-infrared spectroscopy (NIRS), a temperature adaptive correction neural network model called TI-CNN has been developed. This model takes into account the temperature influence. First, a multi-scale CNN model is established to extract the global features of NIRS. Then, by establishing the quantitative relationship between the sample and the modeling error of the neural network, the influence of temperature on the spectral features. Based on the degree of influence, the weight is adaptively adjusted to quantify the effect of temperature on near-infrared spectral features. Finally, to verify the model performance, EPO-PLSR, OSC-PLSR, CNN, and TA-CNN were used for comparison on the test set. The results showed that traditional temperature correction models performed poorly, with a coefficient of determination R2< 0.80. However, the neural network models based on multi-scale CNN, R2> 0.80. Of these, the TI-CNN based on multi-scale CNN and temperature adaptive correction, demonstrated the best performance with R2> 0.95. Therefore, the method proposed in this paper demonstrates superior performance compared to existing methods. It utilizes a multi-scale CNN to extract near-infrared spectral features and also incorporates temperature to adaptively correct the spectral features, resulting in superior model performance across various temperature conditions.

Quantifying Spatio-temporal dynamics of land surface temperature for the rapidly growing city of Bahir Dar, Northwest Ethiopia using multidated Landsat data

Urban heat island (UHI) refers to the occurrence of very high temperature in urban areas as compared to the surrounding rural areas. Land surface temperature (LST) is a key parameter in urban environmental analysis and varies with different land use/covers. The present study aims to explore the spatiotemporal dynamics of land surface temperature of Bahir Dar City and its surroundings using multidated Landsat 5 TM and Landsat 8 OLI/TIRS data of 1984, 1994, 2009, and 2019. The thermal infrared data has been corrected and converted to top of the atmospheric radiance and later to temperature in °C from Kelvin scale Using ArcMap software. The time series data pertaining to 1984, 1994, 2009, and 2019, showed a gradual increase in maximum LST and its expanse. Out of the total area of 213.55 km2, low (‹17 °C) to medium (‹23 °C) LSTprevailed in 65.88% (140.7 km2) of the area, whereas high (›29 °C) LST existed in 34.12% (72.85 km2). Though the low, medium, and high LST classes increased through 1984, 1994, 2009, and 2019, and medium and high-temperature classes became dominant in the expanse. The temporal variation of the Normalized Difference Vegetation Index (NDVI), as an indicator of urban heat intensity, showed the highest NDVI of 0.68 in 1994 and the lowest NDVI value of 0.54 in 2019. LST has a negative statistical correlation with NDVI with a coefficient of determination R2 = 0.99, 0.99, 0,94 & 0.98 for 1984, 1994, 2009, and 2019, respectively. NDVI and NDBI have very strong correlation with LST (R2>94%), which is quite different from previous studies in other study areas.

Application of a bar adsorptive microextraction based methodology for doping control of alkylamine stimulants in urine matrices

To comply with ‘World Anti-Doping Agency’ (WADA) guidelines, doping control laboratories must continuously adjust their analytical procedures. Therefore, sample preparation continues to play a critical step in modern analytical strategies, namely by replacing the tedious, time and solvent consuming commonly employed (e.g. liquid-liquid extraction). The present contribution proposes, for the first time in doping control, bar adsorptive microextraction (BAμE) as an alternative analytical technique for the qualitative determination of six alkyl amine stimulants (AAs; 1,3-dimethylbutylamine, 1,4-dimethylpentylamine, heptaminol, isometheptene, octodrine and tuaminoheptane) in urine matrices followed by derivatization prior to gas chromatography coupled to mass spectrometry, operating in the selected ion monitoring mode acquisition (GC–MS(SIM)). After selecting the most selective coating phase, i.e., a mixed-mode reversed phase/strong anion exchange sorbent (P2), assays performed under optimized experimental conditions [microextraction - BAμE(P2), 1 h (1,000 rpm), pH 11, 10 % NaCl; back-extraction - methanol (150 μL), 30 min, under sonication], allowed remarkable recoveries ranging from 48.7 % (heptaminol, 200 ng/mL) to 83.1 % (1,4-dimethylpentylamine, 200 ng/mL). The validation assessment assays of the proposed methodology showed suitable limits of identification (5.0–35.0 ng/mL), appropriate linear dynamic ranges (5.0–200.0 ng/mL) and good determination coefficients (r2 > 0.9937), as well as excellent selectivity, robustness, accuracy and precision. To check whether the methodology is fit-for-purpose, four previously analysed proficiency urine samples were successfully tested, in which were unequivocally detected and identified some of the target AAs. The present methodology showed to be a remarkable alternative in comparison to other dedicated analytical approaches to screen AAs in urine matrices, since it is cost-effective, user- and eco-friendly, requiring low volume of urine sample (1 mL). The great potential of this analytical technology in doping control lies in a very effective microextraction combined with the minimization of potential interferents, presenting itself as an added value to be applied to other types of substances and complex matrices.

Study on friction characteristics between cotton stalk-residual film-external contact materials

Friction affects the crushing effect of materials. It is of great significance to explore the friction characteristics between cotton stalk-residual film-external contact materials for the analysis of the crushing mechanism of residual film. In view of this, the influence characteristics of inter residual films, residual films/external contact materials friction characteristics were explored. The inter-films static sliding friction coefficient fs and dynamic sliding friction coefficient fk are 0.425 and 0.372, the film/40Cr mean fs is 0.439. Meanwhile, the stalk/materials friction characteristics was analyzed. The response model of fs and static rolling stability Angle μe was constructed (Model coefficient p < 0.01, coefficient of determination R2 >0.93, calibration determination coefficient R2adj >0.86, coefficient of variation C.V >2.9%). The effects of different moisture content, sampling sites and external contact materials on the friction characteristics of inter-stalks and stalk/materials were explained.

Hydrodynamic force interaction of two fixed spheres in a wall-bounded linear shear flow

We examine the modulations of the drag coefficient Cd and lift coefficient Cl on a spherical particle caused by the presence of a neighboring sphere when positioned in close proximity to a wall. Data are generated through the utilization of a fast and accurate Particle-Resolved Direct Numerical Simulation (PR-DNS) method that relies on the Direction-Splitting algorithm and that was extensively validated in our prior study (Goyal and Wachs, 2023a, 2023b; Morente et al., 2023). We further validate the method in the flow configuration of the present study by comparing them with data published in the literature. We consider three Reynolds numbers Re=20, Re=50 and Re=100, which describe the flow conditions in the vicinity of the wall. The primary sphere is positioned at a distance δ from the wall, while the neighboring sphere is positioned at a constant distance from the primary sphere encompassing all possible angular positions θ. We report the modulations of Cd and Cl compared to a situation without any neighboring sphere as a function of δ, θ and Re. The inclusion of a wall intensifies the influence of the neighboring sphere on the primary sphere and significantly modifies the values of Cd and Cl. We attempt to relate the values of the modulations of Cd and Cl to the modulations in the contours of the pressure and of the spanwise vorticity on the surface of the primary sphere. We take advantage of the periodic nature of Cd and Cl as a function of the relative angular position θ of the neighboring sphere and propose a Fourier Predictive Model (FPM) to effectively represent the data using a finite number of modes in the cosine Fourier series. We show that our FPM is able to estimate the modulations of Cd and Cl excellently with a coefficient of determination R2≳0.99 in the parameter space investigated in our study.

Performance Assessment of a High-Frequency Radar Network for Detecting Surface Currents in the Pearl River Estuary

The performance of a high-frequency (HF) radar network situated within the Pearl River Estuary from 17 July to 13 August 2022 is described via a comparison with seven acoustic Doppler current profilers (ADCPs). The radar network consists of six OSMAR-S100 compact HF radars, with a transmitting frequency of 13–16 MHz and a direction-finding technique. Both the radial currents and vector velocities showed good agreement with the ADCP results (coefficient of determination r2: 0.42–0.78; RMS difference of radials: 11–21.6 cm s−1; bearing offset Δθ: −4.8°–16.1°; complex correlation coefficient γ: 0.62–0.96; and phase angle α: −24.3°–17.8°). For these radars, the Δθ values are not constant but vary with azimuthal angles. The relative positions between the HF radar and ADCPs, as well as factors such as the presence of island terrain obstructing the signal, significantly influence the errors. The results of spectral analysis also demonstrate a high level of consistency and the capability of HF radar to capture diurnal and semidiurnal tidal frequencies. The tidal characteristics and the Empirical Orthogonal Function (EOF) results measured by the HF radars also resemble the ADCPs and align with the characteristics of the estuarine current field.

Electroactive ferrites nanostructure driven electrochemical platform for selective detection of dopamine

This study proposes a new electrochemical platform based on electroactive vanadium ferrites (VFe2O4) for the highly selective detection of dopamine (DA). VFe2O4 were synthesized with the sol–gel auto-ignition method and characterized by X-ray diffraction (XRD), Scanning electron microscopy (SEM), impedance analyzer, and cyclic voltammetry. The characterization study revealed that the VFe2O4 have a single-cubic structure with agglomerated structure. The VFe2O4 showed the multi-ions-based increment in the dielectric constant. The VFe2O4 sensor was fabricated by drop-casting method on a glassy carbon (VFe2O4-GCE) electrode with the addition of Nafion and applied for electrochemical detection of DA (a common neurotransmitter) in pharmaceutical preparations and biological samples. The results of electrochemical analysis of DA in the concentration range of 0.05 – 12.3 µM by using VFe2O4-GCE showed a linear response (co-efficient of determination R2 = 0.995) with the limit of detection and quantification (LOD/LOQ) as 0.0087 and 0.029 μM, respectively.

Designing and Static Performance Testing of Auger Type Fertilizer Metering Device with PID Control

The rapid development of technology has brought a positive impact on the agricultural sector, primarily through the concept of precision agriculture. Precision fertilization is critical to improving agricultural quality and productivity by adapting the right actions to the right place at the right Time according to land variability. One approach that is considered successful is fertilization based on nutrient adequacy levels, which can provide optimal fertilization recommendations. The purpose of this study is to obtain an auger-type variable rate mating device that can control the dose of granular fertilizer. The results showed that the variable rate metering device auger type that had been tested could work quite well in controlling the dose of granular fertilizer. This can be seen from the static calibration of the auger-type metering device producing a linear correlation of each motor with a correlation equation y = 0.0041x – 0.025 with a coefficient of determination R2 = 0.9543 and the results of testing stair step response and step response both without fertilizer and with fertilizer show that the system can follow the set point commands given.