Particle Swarm Optimization (PSO) Model for Hydroponics pH Control System

Nutrients are essential to optimise plant growth. However, adding fertiliser changes the pH of the nutrition solution. This would impact plant growth as each plant types requires a specific pH range to thrive. Due to the nonlinearity characteristics, pH neutralisation adjustment is difficult but essential. In addition, alkaline solutions are not completely dissociated due to the presence of acid. For these reasons, a mathematical model to estimate the solution's pH would help improve the alkaline and acidic delivery accuracy. This study represents a pH water neutralisation behaviour using Particle Swarm Optimisation algorithm (PSO). The project begins with input and output data acquisition leading to the development of the PSO model. The model fit and residual distribution have also been analysed for this model. The model's performance was accepted based on a correlation test because the lag signal exceeded 95% of the confidence interval. The model also recorded a very minimal error, and this proved that a good agreement is established between the predicted and actual pH values.

Development and evaluation of the refined zenith tropospheric delay (ZTD) models

The tropospheric delay is a significant error source in Global Navigation Satellite System (GNSS) positioning and navigation. It is usually projected into zenith direction by using a mapping function. It is particularly important to establish a model that can provide stable and accurate Zenith Tropospheric Delay (ZTD). Because of the regional accuracy difference and poor stability of the traditional ZTD models, this paper proposed two methods to refine the Hopfield and Saastamoinen ZTD models. One is by adding annual and semi-annual periodic terms and the other is based on Back-Propagation Artificial Neutral Network (BP-ANN). Using 5-year data from 2011 to 2015 collected at 67 GNSS reference stations in China and its surrounding regions, the four refined models were constructed. The tropospheric products at these GNSS stations were derived from the site-wise Vienna Mapping Function 1 (VMP1). The spatial analysis, temporal analysis, and residual distribution analysis for all the six models were conducted using the data from 2016 to 2017. The results show that the refined models can effectively improve the accuracy compared with the traditional models. For the Hopfield model, the improvement for the Root Mean Square Error (RMSE) and bias reached 24.5/49.7 and 34.0/52.8 mm, respectively. These values became 8.8/26.7 and 14.7/28.8 mm when the Saastamoinen model was refined using the two methods. This exploration is conducive to GNSS navigation and positioning and GNSS meteorology by providing more accurate tropospheric prior information.

Dissipation and Distribution of Picarbutrazox Residue Following Spraying with an Unmanned Aerial Vehicle on Chinese Cabbage (Brassica campestris var. pekinensis)

We assessed the residual distribution and temporal trend of picarbutrazox sprayed by agricultural multicopters on Chinese cabbage and considered fortification levels and flying speeds. In plot 2, 14 days after the last spraying, the residues decreased by ~91.3% compared with those in the samples on day 0. The residues in the crops decreased by ~40.8% of the initial concentration owing to growth (dilution effect) and by ~50.6% after excluding the dilution effect. As the flight speed increased, picarbutrazox residues decreased (p < 0.05, least significant deviation [LSD]). At 2 m s−1 flight speed, the residual distribution differed from the dilution rate of the spraying solution. The average range of picarbutrazox residues at all sampling points was 0.007 to 0.486, below the limit of quantitation −0.395, 0.005–0.316, and 0.005–0.289 mg kg−1 in plots 1, 2, 3, and 4, respectively, showing significant differences (p < 0.05, LSD). These results indicated that the residual distribution of picarbutrazox sprayed by using a multicopter on the Chinese cabbages was not uniform. However, the residues were less than the maximum residue limit in all plots. Accordingly, picarbutrazox was considered to have a low risk to human health if it was sprayed on cabbage according to the recommended spraying conditions.

A Comparison of Priors When Using Bayesian Regression to Estimate Oral Reading Fluency Slopes

Bayesian regression has emerged as a viable alternative for the estimation of curriculum-based measurement (CBM) growth slopes. Preliminary findings suggest such methods may yield improved efficiency relative to other linear estimators and can be embedded into data management programs for high-frequency use. However, additional research is needed, as Bayesian estimators require multiple specifications of the prior distributions. The current study evaluates the accuracy of several combinations of prior values, including three distributions of the residuals, two values of the expected growth rate, and three possible values for the precision of slope when using Bayesian simple linear regression to estimate fluency growth slopes for reading CBM. We also included traditional ordinary least squares (OLS) as a baseline contrast. Findings suggest that the prior specification for the residual distribution had, on average, a trivial effect on the accuracy of the slope. However, specifications for growth rate and precision of slope were influential, and virtually all variants of Bayesian regression evaluated were superior to OLS. Converging evidence from both simulated and observed data now suggests Bayesian methods outperform OLS for estimating CBM growth slopes and should be strongly considered in research and practice.

Deciphering human decision rules in motion discrimination

We investigated the eight decision rules for a same-different task, as summarized in Petrov (Psychonomic Bulletin & Review, 16(6), 1011–1025, 2009). These rules, including the differencing (DF) rule and the optimal independence rule, are all based on the standard model in signal detection theory. Each rule receives two stimulus values as inputs and uses one or two decision criteria. We proved that the false alarm rate p(F) ≤ 1/2 for four of the rules. We also conducted a same-different rating experiment on motion discrimination (n = 54), with 4∘ or 8∘ directional difference. We found that the human receiver operating characteristic (ROC) spanned its full range [0,1] in p(F), thus rejecting these four rules. The slope of the human Z-ROC was also < 1, further confirming that the independence rule was not used. We subsequently fitted in the four-dimensional (pAA, pAB, pBA, pBB) space the human data to the remaining four rules—DF and likelihood ratio rules, each with one or two criteria, where pXY = p(responding “different” given stimulus sequence XY). We found that, using residual distribution analysis, only the two criteria DF rule (DF2) could account for the human data.

Determination of Pendimethalin Dynamic Residual Distribution in Crucian Carp Tissues and Associated Risk Assessment

Pendimethalin has been considered a moderately to extremely toxic compound for fish and aquatic organism. This study developed the determination of dynamic residual distribution for pendimethalin in crucian carp tissues (muscle, liver, kidney, gill, and blood) under semistatic exposure system by high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) method. The pendimethalin residues in various fish tissues increased initially and then decreased, and the residue amount of pendimethalin varied from tissue to tissue of crucian carp. Particularly, the pendimethalin accumulation in most fish tissues made significant decreases at two-time points. Pendimethalin was initially absorbed and enriched by fish body, and then partial pendimethalin was discharged into the outside environment through the metabolism function of crucian carp. The residue levels of pendimethalin distributed in crucian carp were ranked in the following decreasing order: liver > kidney > gill or muscle > blood, attributed to the fact that pendimethalin tends to accumulate in lipid-rich tissues of fish. Risk assessment results indicated that the chronic risk from dietary exposure to pendimethalin through crucian carp consumption for Chinese residents was acceptable, along with a lower estimated exposure dose (EED) than acceptable daily intake (ADI) and risk quotient (RQ) < 1. This study performed the first analysis for pendimethalin residual distribution in crucian carp tissues under semistatic exposure condition and provided a reference for pollution control and risk assessment of pendimethalin aimed at aquatic products.

Nonlinear Model Selection for Fruit and Kernel Development as a Function of Heat in Pistachio

Knowing a tree crop’s seasonal growth and development as a function of heat accumulation can facilitate scheduling of irrigation, pesticide applications, and harvest. Our objective was to compare the goodness of fit of applied models and determine which provides the best description of pistachio nut growth as a function of thermal unit accumulation. Three fruit growth traits of pistachio—pericarp (hull) + endocarp (shell) size, endocarp thickening and hardening, and embryo (kernel) size—exhibited clear nonlinear dependence on heat accumulation. We tested three nonlinear models—Michaelis–Menten, three-parameter logistic, and Gompertz—fitted to fruit development data to create a tool to forecast pest susceptibility and harvest timing. Observation of development began at full bloom and ended at harvest. Data were collected from six pistachio cultivars in one experimental and eight commercial orchards over 3 years. Analyses of residual distribution, parameter standard errors, coefficient of determination (R2) and the Akaike information criterion (AIC) all demonstrated the Gompertz function was the best model. Cultivars differed significantly in all the three parameters (Asym, b, and c) for all three traits with the Gompertz model, demonstrating the Gompertz model can adjust to incorporate cultivar differences. The growth curve of the three traits together provided integrated information on nut biomass accumulation that facilitates predicting the critical timing for multiple orchard management practices.