Estimating the size of plants by using two parallel views

Determination of lower flammability limits of C–H–O compounds in air and study of initial temperature dependence

For peanut yield monitoring it has been demonstrated that determination of peanut moisture content may be useful in reducing yield prediction error. There are no commercially available technologies that provide accurate means of measuring moisture content of whole pod peanuts. In this study, two hand held grain moisture meters were evaluated for use in determination of shelled and whole pod peanut moisture content. The meter readings were compared to oven-dried moisture contents using ASAE S410.2. Whole pod moisture contents were measured on 38 samples of virginia and 10 samples of runner type peanuts using a Dickey John M3G handheld moisture meter. A regression model was developed to adjust the meter readings as a function of the oven dried moisture contents. The results from the whole pod moisture tests demonstrated an average absolute prediction error of 1.9 %MC, or 10.3% average absolute error. Shelled moisture contents were measured on 31 samples of virginia type peanuts using a Dickey John mini GAC plus grain moisture tester and a regression model was developed as described above. The results from the shelled moisture tests demonstrated an average absolute prediction error of 1.3 %MC, or 7.5% average absolute error. The kernel moisture samples were subsamples of larger samples for which oven dried moisture whole pod content was also determined. This allowed comparison of whole pod to kernel moisture content for the 31 samples. The results of the whole pod tests are suggestive that dielectric methods already used for determining grain moisture content may be viable for on-the-go non-destructive moisture determination on peanut combines, but that the accuracy will be less than that experienced when using the same devices for measuring grain moisture.

This study aimed to demonstrate how the level of accuracy in intraocular lens (IOL) power calculation can be improved with optical biometry using partial optical coherence interferometry (PCI) (Zeiss IOLMaster) and current anterior chamber depth (ACD) prediction algorithms. Intraocular lens power in 461 consecutive cataract operations was calculated using both PCI and ultrasound and the accuracy of the results of each technique were compared. To illustrate the importance of ACD prediction per se, predictions were calculated using both a recently published 5-variable method and the Haigis 2-variable method and the results compared. All calculations were optimized in retrospect to account for systematic errors, including IOL constants and other off-set errors. The average absolute IOL prediction error (observed minus expected refraction) was 0.65 dioptres with ultrasound and 0.43 D with PCI using the 5-variable ACD prediction method (p < 0.00001). The number of predictions within +/- 0.5 D, +/- 1.0 D and +/- 2.0 D of the expected outcome was 62.5%, 92.4% and 99.9% with PCI, compared with 45.5%, 77.3% and 98.4% with ultrasound, respectively (p < 0.00001). The 2-variable ACD method resulted in an average error in PCI predictions of 0.46 D, which was significantly higher than the error in the 5-variable method (p < 0.001). The accuracy of IOL power calculation can be significantly improved using calibrated axial length readings obtained with PCI and modern IOL power calculation formulas incorporating the latest generation ACD prediction algorithms.

RELEVANCE. The effective functioning of urban water supply systems plays an important role in maintaining the normal life of cities and towns. Of particular importance is the management of pressure and the optimization of hydraulic regimes in water-wired networks, since they directly affect the reliability of water supply and the efficient use of resources. However, the determination of the optimal parameters and methods of regulation in each specific case requires careful research and analysis. In the context of a constant increase in requirements for automation of water supply and growing investments in its infrastructure, the problem of a correct feasibility study for such investments is of particular relevance.PURPOSE. For smooth regulation of the pressure characteristic of the pump in the conditions of the need to maintain a given pressure of the water supply network, frequency converters are widely used. In the context of the significance of an accurate assessment of investment costs for the automation of pumping units, it becomes necessary to obtain equations that take into account the features of the operation of the water supply network at the pre-design stage. In this regard, the authors of the article set themselves the goal of studying the degree of influence of the static component of the water supply network on the change in the power consumption of the pumping unit with frequency regulation and testing the obtained dependencies on real statistics of the water and energy consumption modes of the Sozh water intake of the Gomel water utility.METHODS. To solve the tasks, classical formulas of similarity of the pumping unit were used, reflecting the relationship between flow, pressure and power consumption. The mean squared error, the mean absolute error and the average absolute error in percentages were used as the evaluation metric for verifying the electric power supply model of the pumping unit.RESULTS. The study demonstrates a significant improvement in the accuracy of power consumption modeling when using a modified coefficient reflecting the degree of power change when the frequency of the supply network changes. When using this approach, the standard error is reduced more than twice, from 0.35 to 0.167, the average absolute error is reduced from 0.347 to 0.165, and the average absolute percentage error is reduced from 0.20% to 0.08%.CONCLUSION The conducted research confirms the effectiveness of the use of frequency control of pumping units, which provides a nonlinear change in electrical power, and demonstrates the possibility of more accurate forecasting of electricity consumption, taking into account the specifics of the water supply network. The results of this work can be useful for projects to optimize urban water supply systems, providing more accurate planning and use of resources.

Airborne laser scanning (ALS) allows for extensive coverage, but the accuracy of tree detection and form can be limited. Although terrestrial laser scanning (TLS) can improve on ALS accuracy, it is rather expensive and area coverage is limited. Multi-view stereopsis (MVS) techniques combining computer vision and photogrammetry may offer some of the coverage benefits of ALS and the improved accuracy of TLS; MVS combines computer vision research and automatic analysis of digital images from common commercial digital cameras with various algorithms to reconstruct three-dimensional (3D) objects with realistic shape and appearance. Despite the relative accuracy (relative geometrical distortion) of the reconstructions available in the processing software, the absolute accuracy is uncertain and difficult to evaluate. We evaluated the data collected by a common digital camera through the processing software (Agisoft PhotoScan ©) for photogrammetry by comparing those by direct measurement of the 3D magnetic motion tracker. Our analyses indicated that the error is mostly concentrated in the portions of the tree where visibility is lower, i.e., the bottom and upper parts of the stem. For each reference point from the digitizer we determined how many cameras could view this point. With a greater number of cameras we found increasing accuracy of the measured object space point positions (as expected), with a significant positive change in the trend beyond five cameras; when more than five cameras could view this point, the accuracy began to increase more abruptly, but eight cameras or more provided no increases in accuracy. This method allows for the retrieval of larger datasets from the measurements, which could improve the accuracy of estimates of 3D structure of trees at potentially reduced costs.

The goal of this research project was to further the development of an electromechanically controlled variable-orifice nozzle by creating an electronic control system and then evaluating that system based on step and ramp inputs. The control system was developed in a programming environment that combined an electronic data acquisition system and actuator with pressure and flow sensors. A proportional, variable-gain (based on system pressure) control system was developed to adjust nozzle flow rates to meet target application rates. The constraints were to achieve settling time of less than 1.0 s, overshoot of less than 10% of maximum flow (or minimum flow), and average absolute steady-state error of less than 2%. After several trials, the resulting control system achieved these objectives for full steps from maximum and minimum flow rates (and vice versa) at carrier pressures from 140 to 410 kPa. Ramp response analyses revealed the maximum flow rate change (mL s -2 ) of the nozzle control system. Operation was considered successful if the average absolute error was less than 5% and the average absolute error +2Iƒ did not exceed 10% of the desired flow, thereby ensuring that the nozzle operated within specifications 95% of the time. An additional goal was to maintain nozzle response lag times of less than 1.0 s based on input rate changes in the form of ramp signal input frequencies. Lag times were found to be less than 0.5 s (±0.05 s) over the carrier pressure range at input frequencies of up to 0.2 Hz. Further, these results indicated that for each carrier pressure, a maximum rate change frequency of 0.07 Hz ensured that system errors were within the design requirements. Lag times at this frequency were less than 0.38 s for all carrier pressures tested. The range of rate change achieved by the nozzle control system ranged from 2.97 to 6.39 mL s -2 for carrier pressures of 140 to 414 kPa, respectively. Thus, as operating pressure increased, the nozzle was capable of compensating for greater changes in the desired flow rate. While the turndown ratios (~2.4:1) over the range of carrier pressures were essentially stable, flow rates increased with carrier pressure.

Forecasting passenger flow at metro transit stations is a useful method for optimizing the organization of passenger transportation and enhancing operational safety and transportation efficiency. Aiming at the problem that the traditional ARIMA model has poor performance in predicting passenger flow, a hybrid prediction method based on ARIMA-Kalman filtering is proposed. In this regard, ARIMA model training experimental samples are integrated with Kalman filter to create a prediction recursion equation, which is then utilized to estimate passenger flow. The simulation experiment results based on the inbound passenger flow data of Nanjing metro station show that compared with the single ARIMA model, the root mean square error of the prediction results of the proposed ARIMA-Kalman filter hybrid algorithm is reduced by 257.106, and the mean absolute error decreased by 145. 675, the mean absolute percentage error dropped by 5. 655%, proving that the proposed hybrid algorithm has higher prediction accuracy. The experiment results based on the passenger flow data of Nanjing metro station show that compared to a single ARIMA model, the proposed ARIMA Kalman filtering hybrid algorithm reduces the root mean square error of the prediction results by 257.106, the average absolute error by 145.675, and the average absolute percentage error by 5.655%. It has been proven that the proposed hybrid algorithm has higher prediction accuracy.

Prediction of daily reference evapotranspiration (ET 0) is the basis of real-time irrigation scheduling. A multiple regression method for ET 0 prediction based on its seasonal variation pattern and public weather forecast data was presented for application in East China. The forecasted maximum temperature (T max), minimum temperature (T min) and weather condition index (WCI) were adopted to calculate the correction coefficient by multilinear regression under five time-division regimes (10 days, monthly, seasonal, semi-annual and annual). The multiple regression method was tested for its feasibility for ET 0 prediction using forecasted weather data as the input, and the monthly regime was selected as the most suitable. Average absolute error (AAE) and root mean square error (RMSE) were 0.395 and 0.522 mm d−1, respectively. ET 0 prediction errors increased linearly with the increase in temperature prediction error. A temperature error within 3 K is likely to result in acceptable ET 0 predictions, with AAE and average absolute relative error (AARE) <0.142 mm d−1 and 5.8%, respectively. However, one rank error in WCI results in a much larger error in ET 0 prediction due to the high sensitivity of the correction coefficient to WCI and the large relative error in WCI caused by one rank deviation. Improving the accuracy of weather forecasts, especially for WCI prediction, is helpful in obtaining better estimations of ET 0 based on public weather data.

Given that nursery is a peculiar environment, the amount of nutrients removed by nursery trees represents a fundamental acquisition to optimise fertilisation strategies, with economic and environmental implications. In this context, we determined nutrient removal by apple, pear and cherry nursery trees at the end of the nursery growing cycle. We randomly removed 5 leafless apple (Golden Delicious/EMLA M9; density of 30,000 trees ha–1), pear (Santa Maria/Adams; density of 30,000 trees ha–1) and cherry (AlexTM/Gisela 6®; density of 40,000 trees ha–1) trees from a commercial nursery. Trees were divided into roots (below the root collar), rootstock (above-ground wood between root collar and grafting point) and variety (1-year-old wood above the grafting point). For each organ we determined biomass, macro- (N, P, K, Ca, Mg, S,) and micro- (Fe, Mn, Zn, Cu, and B) nutrient concentration. Pear trees were the most developed (650 g (dw) tree–1, equal to 1.75 and 2.78 folds than apple and cherry trees, respectively) whereas, independently of the species, variety mostly contributed (&gt;50%) to the total tree biomass, followed by roots and then above-ground rootstock. However, the dry biomass and nutrient amount measured in rootstocks (including roots) represent the cumulative amount of 2 and 3 seasons, for Gisela® 6 (tissue culture) and pome fruit species (generated by mound layering), respectively. Macro and micronutrients were mostly concentrated in roots, followed by variety and rootstock, irrespective of the species. Independently of the tissue, macronutrients concentration hierarchy was N&gt;Ca&gt;K&gt; P&gt;Mg&gt;S. Removed N by whole tree accounted for 6.58, 3.53 and 2.49 g tree–1 for pear, apple and cherry, respectively, corresponding to almost 200, 107 and 100 kg N ha–1, respectively. High amounts of K and Ca were used by pear (130-140 kg ha–1) and apple trees (~50 and 130 kg ha–1 of K and Ca, respectively), while ~25 kg K ha–1 and 55 kg Ca ha–1 were calculated for cherry nursery trees. Among micronutrients, Fe was the most required (~3 kg ha–1) independently of the species. B removal ranged between 1.2 and 2.4 kg ha–1 (80, 40 and 30 mg tree–1 for pear, apple and cherry, respectively), whereas Mn, Cu and Zn accounted for few hundred g ha–1, irrespective of the species. Given that nutrient concentration among tissues resulted within the same order of magnitude, irrespective of the species, differences in removal were mainly driven by the tree biomass as proved by the significant correlations between plant dry biomass with most of the nutrients we observed.

Empirical Orthogonal Function (EOF) analysis is used in this study to generate main eigenvector fields of historical temperature for the China Seas (here referring to Chinese marine territories) and adjacent waters from 1930 to 2002 (510 143 profiles). A good temperature profile is reconstructed based on several subsurface in situ temperature observations and the thermocline was estimated using the model. The results show that: 1) For the study area, the former four principal components can explain 95% of the overall variance, and the vertical distribution of temperature is most stable using the in situ temperature observations near the surface. 2) The model verifications based on the observed CTD data from the East China Sea (ECS), South China Sea (SCS) and the areas around Taiwan Island show that the reconstructed profiles have high correlation with the observed ones with the confidence level >95%, especially to describe the characteristics of the thermocline well. The average errors between the reconstructed and observed profiles in these three areas are 0.69°C, 0.52°C and 1.18°C respectively. It also shows the model RMS error is less than or close to the climatological error. The statistical model can be used to well estimate the temperature profile vertical structure. 3) Comparing the thermocline characteristics between the reconstructed and observed profiles, the results in the ECS show that the average absolute errors are 1.5m, 1.4 m and 0.17°C/m, and the average relative errors are 24.7%, 8.9% and 22.6% for the upper, lower thermocline boundaries and the gradient, respectively. Although the relative errors are obvious, the absolute error is small. In the SCS, the average absolute errors are 4.1 m, 27.7 m and 0.007°C/m, and the average relative errors are 16.1%, 16.8% and 9.5% for the upper, lower thermocline boundaries and the gradient, respectively. The average relative errors are all <20%. Although the average absolute error of the lower thermocline boundary is considerable, but contrast to the spatial scale of average depth of the lower thermocline boundary (165 m), the average relative error is small (16.8%). Therefore the model can be used to well estimate the thermocline.

In this paper, a fuzzy-logic-based energy management system and a multi-port quasi-z-source converter that utilizes hybrid renewable energy sources are proposed. The system ensures that each energy source module can be used individually by employing fuzzy logic to define the power modes. This approach also helps to prevent switching losses resulting from the extra switching of the source modules. In addition, the proposed energy management does not have a mathematical model, so its applicability is simple, and it is suitable for different multiple-input topologies. The Mamdani fuzzy inference system can be designed to capture the nonlinear behavior of the system owing to linguistic rules. Moreover, the switching losses of the multiport modules were significantly reduced by adopting the quasi-z-source network to the end of the multiport converter. Furthermore, different errors, such as the root mean square error (RMSE), average squared error (ASE), average absolute error (AAE), average time-weighted absolute error (ATWAE), tracking error (TE), and unscaled mean bounded relative absolute error (UMBRAE), were applied to evaluate the fuzzy logic performance from different perspectives. The simulation results were obtained using MATLAB Simulink, and the experimental results were obtained by connecting the circuit to MATLAB Simulink using an Arduino Due.

IntroductionWe hypothesize robotic-assisted THA can achieve precise cup positioning. The aim of this study is to figure out the accuracy of cup placement of severe DDH cases by using robotic-assisted THAMethodsThis was a prospective cohort study. The study analyzed a consecutive series of 53 hips with robotic-assisted THA between Aug 2018 and Sep 2019. Fifteen patients were DDH cases, and classified Crowe type II- 7 patients, type III- 7 patients, and type IV- 1 patient. All patients underwent robotic assisted-THA (MAKO Rio Robot, Ft. Lauderdale, FL) for osteoarthritis via the Hardinge or posterior approach in the lateral position. TTo analyze the accuracy of intraoperative navigation records for cup inclination and anteversion, we compared the intraoperative cup angles using the navigation records with the postoperative angles using postoperative CT data.ResultsReproducibility of robotic-assisted THA for preoperative planIn non DDH cases, the average measurement absolute error (postoperative CT-preoperative target) was 1.9 ± 2.1° (inclination) and 2.0 ± 2.5° (anteversion) (Figure 1). In DDH cases, the average measurement absolute error (postoperative CT- preoperative target) was 2.2 ± 3.1° (inclination) and 2.6 ± 2.4° (anteversion) (Figure 1). There were no differences of the average measurement absolute error between DDH and non DDH cases.Accuracy of navigation record for cup inclination and anteversion anglesIn non DDH cases, the average measurement absolute error (postoperative CT-navigation record) was 2.3 ± 1.9° (inclination) and 2.2 ± 1.5° (anteversion) (Figure 1). In DDH cases, the average measurement absolute error (postoperative CT-navigation record) was 2.0 ± 2.5° (inclination) and 2.3 ± 2.7° (anteversion) (Figure 1). There were no differences of the average measurement absolute error between DDH and non DDH cases. Scatter plots demonstrated the measurement error of both inclination and anteversion within 5° was 89.5% in non DDH cases (Figure 2a) and 80% in DDH cases (Figure 2b).DiscussionIn this study, we demonstrated that no differences of the average measurement absolute navigation record error or the absolute difference between target angle and postoperative cup alignment were found between severe DDH and non-DDH cases. Our results indicate that this robotic-assisted system has high accuracy for cup placement even in severe DDH case.

In this work the aqueous solubilities of 145 drug-like compounds were predicted from their theoretical derived molecular descriptors. Descriptors which were selected by stepwise multiple subset selection methods are; 1st-order solvation connectivity index, average span R, overall hydrogen bond basicity, and percent of hydrophilic surface area. These descriptors can encode features of molecules which are effected on dispersion, hydrophobic and steric interactions between solute and solvent molecules. To develop quantitative structure–activity relationship (QSAR) models, the methods of multiple linear regressions, least-squares support vector machine, and artificial neural network (ANN) were used by applying the selected descriptors as their inputs. The obtained statistical parameters of these models revealed that ANN model was superior to other methods. The standard error (SE), average error (AE), and average absolute error (AAE) for ANN model are: SE = 0.714, AE = −0.178, and AAE = 0.546, while these values for internal test set are: SE = 0.830, AE = −0.056, and AAE = 0.630 and for external test set are: SE = 0.762, AE = −0.431, and AAE = 0.626, respectively. Moreover the leave-many-out cross validation test was used to further investigate the prediction power and robustness of model, which lead to RL10O2 = 0.816 and SPRESS = 0.32 for ANN model, which revealed the reliability of this model.

Naphthaleneacetic acid (NAA) was applied in the spring to pruned apple (Malus domestica Borkh.) and pear (Pyrus communis L.) trees for control of sprouting on trunks and scaffold limbs. After one growing season, sprouting was completely controlled on trunks and scaffold limbs of ‘Delicious’ apple and ‘Bartlett’ and ‘d’Anjou’ pear trees. After two growing seasons, about 80 to 90% of the sprouts were inhibited on the scaffold limbs of ‘Delicious’ and ‘Winesap’ apple trees. NAA controlled sprouting at 1% with no added benefit at 2%; 0.5% NAA was least effective.

The relationship between fruit tree condition, leaf phosphorus (P), and available soil-P content has been inadequately addressed as evidenced by different results in various parts of the world. This study attempts to provide additional information on P deficiency symptoms of apple and pear trees and its relation to leaf P and available soil-P content. This study also endeavors to establish and further verify the minimum available soil-P and leaf P concentrations required for healthy apple and pear trees, particularly in the Pacific Northwest. Purplish-red leaf margins indicating high anthocyanin production due to phosphorus (P) deficiency were observed in leaves of apple (Malus domestica) and pear (Pyrus communis) trees that were grown in low-P soils in the greenhouse or in the orchard. In one of the greenhouse apple experiments, as leaf P concentrations increased in ‘Delicious’ apple leaves from 0.083 to 0.153%, the incidence of purplish-red leaf margins decreased from 95 to 4%, respectively, (r2=−0.908). Observations of purple leaf margins from apple and pear trees in Pacific Northwest orchards were less obvious but positive trends were established between available soil P and leaf P. In general, the data suggests that available soil P (NaHCO3 method) and leaf P concentrations should be above 13 ppm and 0.13% P, respectively, for healthy apple and pear trees in the orchard for conditions in the Pacific Northwest.