Search Results (17)

Mineral nutrition is essential for plant growth and the interaction of plants with biotic and abiotic stresses. Mango twig-tip dieback (MTTD) is a new type of mango decline, but its impact on trees’ mineral nutrition is unknown. This study was conducted to determine the effect of MTTD infection on the nutrient status, balance, and resorption efficiency (RE) of mangoes. Leaf nutrient concentrations and deviation from the optimum percentage (DOP) indices of ‘Kensington Pride’ (KP) mango trees with low (LD) and high (HD) levels of MTTD infections were analyzed to compare the foliar nutrition status and nutrient balance between the LD and HD trees. Moreover, the nutrient resorption efficiency of MTTD-infected dried leaves (RE_D) was compared with the resorption efficiency of healthy (RE_S) leaves of KP mangoes. The concentrations of total Ca, Mg, Cu, Fe, Mn, and Zn were lower in the HD trees than in the LD trees. But the total K content was higher in the HD trees, and its DOP index was sufficient, while the total K concentration was of a low and deficient level in LD trees. Moreover, the DOP indices for total Ca, Mn, and Zn were less deficient in LD trees than in HD trees, and the overall nutrient imbalances were exacerbated in HD trees. The RE_D was significantly lower than RE_S for the total N, P, S, Cu, Fe, and Zn but significantly higher than RE_S for K. This study underscores the significant influence of MTTD on the mineral nutrition of KP mangoes, revealing distinct nutrient variations between trees with low and high MTTD infection levels. These findings have important implications for mango crop management, emphasizing the need for targeted nutrient interventions to address imbalances induced by MTTD and enhance the overall health and resistance of mango trees against MTTD infections. Full article

The ultimate analysis parameters, including carbon (C), hydrogen (H), nitrogen (N), and oxygen (O) content in biomass, were rarely found to be predicted by non-destructive tests to date. In this research, we developed partial least squares regression (PLSR) models to predict the ultimate analysis parameters of chip biomass using near-infrared (NIR) raw spectra of non-wood and wood samples from fast-growing tree and agricultural residue and nine different traditional spectral preprocessing techniques. These techniques include first derivative (sd1), second derivative (sd2), constant offset, standard normal variate (SNV), multiplicative scatter correction (MSC), vector normalization, min-max normalization, mean centering, sd1 + vector normalization, and sd1 + MSC. Additionally, we employed a genetic algorithm (GA), successive projection algorithm (SPA), multi-preprocessing (MP) 5-range, and MP 3-range to develop a PLSR model for rapid prediction. A dataset consisting of 120 chip biomass samples was utilized for model development in which the samples were non-wood samples of 65–67% and wood samples of 33–35%, and the model performance was evaluated and compared. The selection of the optimum performing model was mainly based on criteria such as the coefficient of determination in the prediction set (R²_P), root mean square error of the prediction set (RMSEP), and the ratio of prediction to deviation (RPD). The optimal model for weight percentage (wt.%) of C was obtained using GA–PLSR, yielding R²_P, RMSEP, and RPD values of 0.6954, 1.1252 wt.%, and 1.8, respectively. Similarly, for wt.% of O, the most effective model was obtained using the multi-preprocessing PLSR–5 range method with R²_P of 0.7150, RMSEP of 1.3088 wt.%, and RPD of 1.9. For wt.% of N, the optimal model was obtained using the MP PLSR-3 range method, resulting in R²_P, RMSEP, and RPD values of 0.6073, 0.1008 wt.%, and 1.6, respectively. However, wt.% of the H model provided R²_P, RMSEP, and RPD values of 0.5162, 0.2322 wt.%, and 1.5, respectively. Notably, the limit of quantification (LOQ) values for C, H, and O were lower than the minimum reference values used during model development, indicating a high level of sensitivity. However, the LOQ for N exceeded the minimum reference value, implying the samples to be predicted by the model must be in the range of reference range in the calibration set. By scatter plot analysis, the effect of combined non-wood and wood spectra of biomass chips on rapid prediction of ultimate analysis parameters using NIR spectroscopy was investigated. To include different species in a model, the species have to be not only in the different values of the constituents to make a wider range for a robust model, but also must provide their trend line characteristics in the scatter plot, i.e., correlation coefficient (R), slope, and intercept (same slope and slope approached to 1, and intercept is same (no gap) and approached zero, high R approached to 1). The effect of the R, slope, and intercept to obtain the better-optimized model was studied. The results show that the different species affected the model performance of each parameter prediction in a different manner, and by scatter plot analysis, which of these species were affecting the model negatively and how the model could be improved was indicated. This is the first time the effect has been studied by the principle of a scatter plot. Full article

Stereolithography (SLA) has become an essential photocuring 3D printing process for producing parts of complex shapes from photosensitive resin exposed to UV light. The selection of the best printing parameters for good accuracy and surface quality can be further complicated by the geometric complexity of the models. This work introduces multiobjective optimization of SLA printing of 3D dental bridges based on simple CAD objects. The effect of the best combination of a low-cost resin 3D printer’s machine parameter settings, namely normal exposure time, bottom exposure time and bottom layers for less dimensional deviation and surface roughness, was studied. A multiobjective optimization method was utilized, combining the Taguchi method with response surface methodology and the desirability function technique. The predicted optimal values for the cube’s dimensional deviation and surface roughness were 0.0517 mm and 2.8079 µm, respectively. The experiments’ validation of the findings confirmed the results, which were determined to be 0.0560 and 0.064667 mm and 2.770 and 2.6431 µm for the dimensional deviation and surface roughness for the cube and bridge, respectively. The percentages of prediction errors between the predicted optimum results and the printed response were 7.68% and 1.36% for dimensional deviation and surface roughness, respectively. This study demonstrates that the robust method used produced a dental bridge with good accuracy and a smooth surface. Full article

The analysis of the high-dimensional dataset when the number of explanatory variables is greater than the observations using classical regression approaches is not applicable and the results may be misleading. In this research, we proposed to analyze such data by introducing modern and up-to-date techniques such as support vector regression, symmetry functional regression, ridge, and lasso regression methods. In this study, we developed the support vector regression approach called generalized support vector regression to provide more efficient shrinkage estimation and variable selection in high-dimensional datasets. The generalized support vector regression can improve the performance of the support vector regression by employing an accurate algorithm for obtaining the optimum value of the penalty parameter using a cross-validation score, which is an asymptotically unbiased feasible estimator of the risk function. In this regard, using the proposed methods to analyze two real high-dimensional datasets (yeast gene data and riboflavin data) and a simulated dataset, the most efficient model is determined based on three criteria (correlation squared, mean squared error, and mean absolute error percentage deviation) according to the type of datasets. On the basis of the above criteria, the efficiency of the proposed estimators is evaluated. Full article

Thermoelectric generator (TEG) has important applications in automotive exhaust waste heat recovery. The Back propagation neural network (BP) can predict the electrical generating performance of TEG efficiently and accurately due to its advantage of being good at handing nonlinear data. However, BP algorithm is easy to fall into local optimum, and its training data usually have deviation since the data are obtained through the simulation software. Both of the problems will reduce the prediction accuracy. In order to further improve the prediction accuracy of BP algorithm, we use the genetic algorithm (GA) to optimize BP neural network by selection, crossover, and mutation operation. Meanwhile, we create a TEG for the heat waste recovery of automotive exhaust and test 84 groups of experimental data set to train the GA−BP prediction model to avoid the deviation caused by the simulation software. The results show that the prediction accuracy of the GA−BP model is better than that of the BP model. For the predicted values of output power and output voltage, the mean absolute percentage error (MAPE) increased to 2.83% and 2.28%, respectively, and the mean square error (MSE) is much smaller than the value before optimization, and the correlation coefficient (R²) of the network model is greater than 0.99. Full article

The balance of the mineral nutrition in mango leaves during the flowering period affects the flowering of mango trees and fruit production. Because the soil in karst areas has a slow and unbalanced supply rate of nutrients, mango orchards in a karst area generally have a low yield. There are few studies on the fertilization of mango orchards in karst areas, especially on the diagnosis of leaf mineral nutrition. In this study, mango orchards in the typical karst areas of Guangxi province, one of the main mango-producing areas in China, were selected from the low-yielding and medium-yielding mango orchards. Surface soil samples and leaf samples from mango orchards in full bloom were collected to test for macronutrients and micronutrients. The Diagnosis and Recommendation Integrated System (DRIS) graphical method, the DRIS method, the Modified DRIS (M-DRIS), and the Deviation from Optimum Percentage (DOP) index diagnostic methods were applied to the leaves. The results showed that the DRIS graphical analysis yielded appropriate ratios of N, P, K, Mg, S, Fe, Mn, Cu with the corresponding three elements, Ca, Zn, and B, which can be used as reference diagnostic criteria. Based on the values of the DRIS diagnostic criteria for high-yielding orchards, the critical ranges of the suitable values of the mineral nutrients in the Tainong mango leaves during flowering were determined as N (14.87–17.27 g/kg), P (0.69–0.89 g/kg), K (4.45–6.90 g/kg), Ca (9.51–16.55 g/kg), Mg (1.44–2.20 g/kg), S (0.75–1.06 g/kg), Fe (0.10–0.13 g/kg), Mn (0.61–1.02 g/kg), Cu (5.41–8.89 mg/kg), Zn (7.91–18.95 mg/kg), and B (8.38–16.23 mg/kg). The results of the DRIS, M-DRIS, and DOP index methods were analyzed to determine the order of the fertilizer requirements for the low-yielding orchards: Mg > Fe > S > Zn > B > Cu > K > N > P > Mn > Ca, and for the medium-yielding orchards: Mg > Fe > B > Zn > S > Cu > N > Mn > K > P > Ca. The soil and leaf correlation analysis showed that the soil exchangeable Ca and effective Fe were significantly negatively correlated. Leaf Ca and Fe elements had a mutually antagonistic effect, leaf Mn-rich contents inhibited the uptake of the Mg and Fe elements, and low-yielding orchards had an excess of Mn and a deficiency of Mg. We found that there is lack of the Mg and Fe, a low content of the S and B, and an excess of the Ca and Mn in the mango orchards of the Baise karst area. We suggested that the DRIS graphical method is suitable for the diagnosis of three nutrient elements, and either the DRIS or M-DRIS index method can be chosen. The present research can be used for the precise fertilization of mango orchards in karst areas to improve the yield and quality of local mango orchards. Full article

The nurse scheduling problem (NSP) is an NP-Hard combinatorial optimization scheduling problem that allocates a set of shifts to the group of nurses concerning the schedule period subject to the constraints. The objective of the NSP is to create a schedule that satisfies both hard and soft constraints suggested by the healthcare management. This work explores the meta-heuristic approach to an artificial bee colony algorithm with the Nelder–Mead method (NM-ABC) to perform efficient nurse scheduling. Nelder–Mead (NM) method is used as a local search in the onlooker bee phase of ABC to enhance the intensification process of ABC. Thus, the author proposed an improvised solution strategy at the onlooker bee phase with the benefits of the NM method. The proposed algorithm NM-ABC is evaluated using the standard dataset NSPLib, and the experiments are performed on various-sized NSP instances. The performance of the NM-ABC is measured using eight performance metrics: best time, standard deviation, least error rate, success percentage, cost reduction, gap, and feasibility analysis. The results of our experiment reveal that the proposed NM-ABC algorithm attains highly significant achievements compared to other existing algorithms. The cost of our algorithm is reduced by 0.66%, and the gap percentage to move towards the optimum value is 94.30%. Instances have been successfully solved to obtain the best deal with the known optimal value recorded in NSPLib. Full article

Phytoplankton communities, which can be easily observed by optical sensors deployed on various types of platforms over diverse temporal and spatial scales, are crucial to marine ecosystems and biogeochemical cycles, and accurate pigment concentrations make it possible to effectively derive information from them. To date, there is no practical approach, however, to retrieving concentrations of detailed pigments from phytoplankton absorption coefficients (${\mathrm{a}}_{\mathrm{ph}}$) with acceptable accuracy and robustness in global oceans. In this study, a novel method, which is a stepwise regression method improved by early stopping (the ES-SR method) based on the derivative of hyperspectral ${\mathrm{a}}_{\mathrm{ph}}$, was proposed to retrieve pigment concentrations. This method was developed from an extensive global dataset collected from layers at different depths and contains phytoplankton pigment concentrations and ${\mathrm{a}}_{\mathrm{ph}}$. In the case of the logarithm, strong correlations were found between phytoplankton pigment concentrations and the absolute values of the second derivative (${\mathrm{a}}_{\mathrm{ph}}^{″}$)/the fourth derivative (${\mathrm{a}}_{\mathrm{ph}}^{\left(4\right)}$) of ${\mathrm{a}}_{\mathrm{ph}}$. According to these correlations, the ES-SR method is effective in obtaining the characteristic wavelengths of phytoplankton pigments for pigment concentration inversion. Compared with the Gaussian decomposition method and principal component regression method, which are based on the derivatives, the ES-SR method implemented on ${\mathrm{a}}_{\mathrm{ph}}^{″}$ is the optimum approach with the greatest accuracy for each phytoplankton pigment. More than half of the determination coefficient values ($R^2{}_{\log }$) for all pigments, which were retrieved by performing the ES-SR method on ${\mathrm{a}}_{\mathrm{ph}}^{″}$, exceeded 0.7. The values retrieved for all pigments fit well to the one-to-one line with acceptable root mean square error (${\mathrm{RMSE}}_{\log }$: 0.146–0.508) and median absolute percentage error (${\mathrm{MPE}}_{\log }$: 8.2–28.5%) values. Furthermore, the poor correlations between the deviations from the values retrieved by the ES-SR method and impact factors related to pigment composition and cell size class show that this method has advantageous robustness. Therefore, the ES-SR method has the potential to effectively monitor phytoplankton community information from hyperspectral optical data in global oceans. Full article

Barley (Hordeum vulgare L.) is a salt-tolerant crop with considerable economic value in salinity-affected arid and semiarid areas. In the laboratory experiment, the halothermal time (HaloTT) model was used to examine barley seed germination (SG) at six constant cardinal temperatures (Ts) of 15, 20, 25, 30, 35, and 40 °C under five different water potentials (ψs) of 0, −0.5, −1.5, −1.0, and −2.0 MPa. Results showed that at optimum moisture (0 MPa), the highest germination percentage (GP) was recorded at 20 °C and the lowest at 40 °C. Moreover, GP increased with the accelerated aging period (AAP) and significantly (p ≤ 0.05) decreased with high T. In addition, with a decrease of ψ from 0 to −0.5, −1, 1.5, and −2.0 MPa, GP decreased by 93.33, 76.67, 46.67, and 33.33%, respectively, in comparison with 0 MPa. The maximum halftime constant (θHalo) and coefficient of determination (R2) values were recorded at 20 °C and 30 °C, respectively. The optimum temperature (T_o) for barley is 20 °C, base Ψ of 50th percentile (Ψb (50)) is −0.23 Mpa, and standard deviation of Ψb (σΨb) is 0.21 MPa. The cardinal Ts for germination is 15 °C (T_b), 20 °C (T_o), and 40 °C (T_c). The GP, germination rate index (GRI), germination index (GI), coefficient of the velocity of germination (CVG), germination energy (GE), seed vigor index I and II (SVI-I & II), Timson germination index (GI), and root shoot ratio (RSR) were recorded maximum at 0 MPa at 20 °C and minimum at −2.0 MPa at 40 °C. Mean germination time (MGT) and time to 50% germination (T 50%) were maximum at −2 MPa at 40 °C, and minimum at 20 °C, respectively. In conclusion, the HaloTT model accurately predicted the germination time course of barley in response to T, Ψ, or NaCl. Therefore, barley can be regarded as a salt-tolerant plant and suitable for cultivation in arid and semi-arid regions due to its high resistance to salinity. Full article

A method for the extraction of phenolic compounds from grape seeds and skins using microwave-assisted extraction (MAE) was developed. Optimization of the effects of the extraction parameters in terms of the results of extraction was obtained using the response surface methodology. The parameters studied were extraction solvent (methanol, ethanol, acetone, and water), percentage of methanol in water, quantity of sample in relation to volume of extraction solvent (solid:liquid, 10–50 mg mL⁻¹), power (100–500 W), magnetic stirring speed (0–100%), and extraction time (5–20 min). Finally, the repeatability and the intermediate precision of the method were determined. The best conditions proved to be: 65% methanol in water as an optimum extraction solvent; 0.5 g of grape skin or seed in a volume of 25 mL; a power of 500 W with the maximum stirring speed (100%); and an extraction time of 5 min. The phenolic compounds proved to be stable in the optimized extraction conditions. The resulting repeatability and the intermediate precision of the optimized method showed a relative standard deviation below 7%. The new method applied on Napoleon grape allowed for the determination of catechin (453.2 (mg kg⁻¹)), epicatechin (306.3 mg kg⁻¹), caftaric acid (22.37 mg caffeic acid equivalents kg⁻¹), dihydrokaempferol-glycoside (11.13 mg kaempferol equivalents kg⁻¹), quercetin (18.28 mg kg⁻¹), quercetin-3-glucoside (20.09 mg quercetin equivalents kg⁻¹), and kaempferol-3-glucoside (11.10 mg kaempferol equivalents kg⁻¹). Full article

Optimizing processing conditions to achieve a critical quality attribute (CQA) is an integral part of pharmaceutical quality by design (QbD). It identifies combinations of material and processing parameters ensuring that processing conditions achieve a targeted CQA. Optimum processing conditions are formulation and equipment-dependent. Therefore, it is challenging to translate a process design between formulations, pilot-scale and production-scale equipment. In this study, an empirical model was developed to determine optimum processing conditions for direct compression formulations with varying flow properties, across pilot- and production-scale tablet presses. The CQA of interest was tablet weight variability, expressed as percentage relative standard deviation. An experimental design was executed for three model placebo blends with varying flow properties. These blends were compacted on one pilot-scale and two production-scale presses. The process model developed enabled the optimization of processing parameters for each formulation, on each press, with respect to a target tablet weight variability of <1%RSD. The model developed was successfully validated using data for additional placebo and active formulations. Validation formulations were benchmarked to formulations used for model development, employing permeability index values to indicate blend flow. Full article

The most important item when indicating the mechanical properties of offshore elastomeric bearings is the shear modulus, and the method of measuring this is shown in EN 1337-3, a regulation related to offshore elastomeric bearings. In this work, we conducted an experimental and numerical study on an offshore elastomeric bearing to find its shear modulus. Shear modulus tests were conducted according to the procedure specified in EN 1337-3 Annex F, while simulations were performed using the finite element analysis (FEA) software, ANSYS. The main objective of this research work is to determine optimum analysis conditions for the simulation method that considers a nonlinear model for the elastomer material and predicts the experimental results accurately. We considered the Mooney–Rivlin (M-R) model that has two-parameter (2P), five-parameter (5P), and nine-parameter (9P) forms, depending on the number of terms in the series. We observed that the load-displacement graph is linear, and the percentage error between the results obtained with 2P and 5P M-R models is around 2.23% in the compression and 0.38% in the shear. The simulation results from 2P M-R model showed a good agreement with the experimental results with the correlation coefficient (R2) being 0.999 with an average error of about 2%. However, the deviation between the experimental and simulation results from the 9P M-R model is very high, with about 7%. Based on this study, we can say that the 2P M-R model can accurately predict the nonlinear behavior of hyperelastic material used in elastomer bearing. In addition, the shear modulus of elastic bearings for Class 3 Shore hardness was verified by comparing the numerical simulation values with those presented in EN 1337-3 Annex D. Full article

Carbon nanotubes (CNTs) have the potential to act as a catalyst support in many sciences and engineering fields due to their outstanding properties. The CNT-coated monolith was synthesized over a highly active Ni catalyst using direct liquid injection chemical vapor deposition (CVD). The aim was to study the optimum condition for synthesizing CNT-coated monoliths. The Taguchi method with L₉ (3⁴) orthogonal array design was employed to optimize the experimental conditions of CNT-coated monoliths. The design response was the percentage of carbon yield expressed by the signal-to-noise (S/N) value. The parameters including the mass ratio of Ni to citric acid (Ni:CA) (A), the injection rate of carbon source (B), time of reaction (C), and operating temperature (D) were selected at three levels. The results showed that the optimum conditions for CNT-coated monolith were established at A₁B₂C₁D₂ and the most influential parameter was D followed by B, C, and A. The ANOVA analysis showed the design was significant with R-squared and standard deviation of the factorial model equal to 0.9982 and 0.22, respectively. A confirmation test was conducted to confirm the optimum condition with the actual values of the average percentage of carbon yield deviated 1.4% from the predicted ones. The CNT-coated monoliths were characterized by various techniques such as field emission scanning electron microscopy (FESEM), energy dispersive X-ray spectroscopy (EDX), X-ray diffraction (XRD), and Raman spectroscopy. Full article

Biodiesel as an alternative to diesel fuel produced from vegetable oils or animal fats has attracted more and more attention because it is renewable and environmentally friendly. Compared to conventional diesel fuel, biodiesel has slightly lower performance in engine combustion due to the lower calorific value that leads to lower power generated. This study investigates the effect of multi-walled carbon nanotubes (MWCNTs) as an additive to the rice bran methyl ester (RBME). Artificial neural network (ANN) and response surface methodology (RSM) was used for predicting the calorific value. The interaction effects of parameters such as dosage of MWCNTs, size of MWCNTs and reaction time on the calorific value of RBME were studied. Comparison of RSM and ANN performance was evaluated based on the correlation coefficient (R²), the root mean square error (RMSE), the mean absolute percentage error (MAPE), and the average absolute deviation (AAD) showed that the ANN model had better performance (R² = 0.9808, RMSE = 0.0164, MAPE = 0.0017, AAD = 0.173) compare to RSM (R² = 0.9746, RMSE = 0.0170, MAPE = 0.0028, AAD = 0.279). The optimum predicted of RBME calorific value that is generated using the cuckoo search (CS) via lévy flight optimization algorithm is 41.78 (MJ/kg). The optimum value was obtained using 64 ppm of < 7 nm MWCNTs blending for 60 min. The predicted calorific value was validated experimentally as 41.05 MJ/kg. Furthermore, the experimental results have shown that the addition of MWCNTs was significantly increased the calorific value from 36.87 MJ/kg to 41.05 MJ/kg (11.6%). Also, the addition of MWCNTs decreased flashpoint (−18.3%) and acid value (−0.52%). As a conclusion, adding MWCNTs as an additive had improved the physicochemical properties characteristics of RBME. To our best knowledge, no research has yet been performed on the effect of multi-walled carbon nanotubes-additive in physicochemical property of rice brand methyl ester application so far. Full article

Prior to delivery of data, eye tracker software may apply filtering to correct for noise. Although filtering produces much better precision of data, it may add to the time it takes for the reporting of gaze data to stabilise after a saccade due to the usage of a sliding window. The effect of various filters and parameter settings on accuracy, precision and filter related latency is examined. A cost function can be used to obtain the optimal parameters (filter, length of window, metric and threshold for removal of samples and removal percentage). It was found that for any of the FIR filters, the standard deviation of samples can be used to remove 95% of samples in the window so than an optimum combination of filter related latency and precision can be obtained. It was also confirmed that for unfiltered data, the shape of noise, signified by RMS/STD, is around $\sqrt{2}$ as expected for white noise, whereas lower RMS/STD values were observed for all filters Full article