Optimization Design of a Pneumatic Wheat-Shooting Device Based on Numerical Simulation and Field Test in Rice–Wheat Rotation Areas

In rice–wheat rotation areas of China, traditional wheat seeders have severe blockage, low working efficiency and poor seeding quality. In this study, a pneumatic shooting technology was designed, consisting mainly of a nozzle, shell and acceleration tube. To improve the sowing depth of the pneumatic shooting device, the response-surface methodology of structure parameters and CFD simulation technology was adopted in this work. The effects of working pressure, acceleration-tube diameter and throat distance on the steady airflow length (SAL) and steady airflow velocity (SAV) were studied by airflow field analysis, and the response-surface method was introduced to obtain the optimal parameter combination of the pneumatic shooting device for wheat. The optimal parameter combination was working pressure 686 kPa, acceleration tube diameter 8 mm and throat distance 20 mm. The simulation result showed that the optimized device of pneumatic shooting performs faster and has more stable airflow field characteristics in comparison to the initial device. The field test demonstrated that the optimized device has about 68% higher seeding depth than the initial device. The average field-seeding depth of the optimized device was 19.95 mm, which is about 68% higher than the initial device. The emergence rate for the optimized device was about 88.7% without obvious reduction. CFD and response-surface methods positively affect the optimization of pneumatic wheat-shooting devices, and the significance was also confirmed.

Method for Controlling Edge Wave Defects of Parts during Roll Forming of High-Strength Steel

Cold roll forming is suitable for sheet metal processing and can provide a new method for the production and processing of anti-collision beams for commercial vehicles. In order to accurately control the edge wave defects of the parts in the roll forming process, we used the professional roll design software COPRA to design the roll pattern and used the professional finite element analysis software ABAQUS to establish a three-dimensional finite element analysis model of the “b”-shaped cross-section. We analyzed the factors affecting the edge wave by controlling different process parameters (the thickness of the sheet, the height of the flange, and the forming speed), and the best process parameter combination was determined. The results showed that the thickness of the sheet, the height of the flange, and the forming speed all had an effect on the edge wave defects of the “b”-shaped cross-section. The influence of sheet thickness was the greatest, followed by flange height and then forming speed. The final selected parameter combination was a sheet thickness of 3 mm, a flange height of 100 mm, and a forming speed of 150 mm/s. This work provides a theoretical basis for actual production.

Crown closure segmentation on wetland lowland forest using the mean shift algorithm

The availability of high and very high-resolution imagery is helpful for forest inventory, particularly to measure the stand variables such as canopy dimensions, canopy density, and crown closure. This paper describes the examination of mean shift (MS) algorithm on wetland lowland forest. The study objective was to find the optimal parameters for crown closure segmentation Pleiades-1B and SPOT-6 imageries. The study shows that the segmentation of crown closure with the red band of Pleiades-1B image would be well segmented by using the parameter combination of (hs: 6, hr: 5, M: 33) having overall accuracy of 88.93% and Kappa accuracy of 73.76%, while the red, green, blue (RGB) composite of SPOT-6 image, the optimal parameter combination was (hs:2, hr: 8, M: 11), having overall accuracy of 85.72% and kappa accuracy of 68.33%. The Pleiades-1B image with a spatial resolution of (0.5 m) provides better accuracy than SPOT-5 of (1.5 m) spatial resolution. The differences between single spectral, synthetic, and RGB does not significantly affect the accuracy of segmentation. The study concluded that the segmentation of high and very high-resolution images gives promising results on forest inventory.

A Retrospective Analysis of b-Value Changes Preceding Strong Earthquakes

Earthquake precursors have long been sought as a means to predict earthquakes with very limited success. Recently, it has been suggested that a decrease in the Gutenberg–Richter b-value after a magnitude 6 earthquake is predictive of an imminent mainshock of larger magnitude, and a three-level traffic-light system has been proposed. However, this method is dependent on parameters that must be chosen by an expert. We systematically explore the parameter space to find an optimal set of parameters based on the Matthews correlation coefficient. For each parameter combination, we analyze the temporal changes in the frequency–magnitude distribution for every M ≥ 6 earthquake sequence in the U.S. Geological Survey Comprehensive Earthquake Catalog for western North America. We then consider smaller events, those with a foreshock magnitude as small as 5, and repeat the analysis to assess its performance for events that modify stresses over smaller spatial regions. We analyze 25 M ≥ 6 events and 88 M 5–6 events. We find that no perfect parameter combination exists. Although the method generates correct retrodictions for some M 5 events, the predictions are dependent on the retrospectively selected parameters. About 80%–95% of magnitude 5–6 events have too little data to generate a result. Predictions are time dependent and have large uncertainties. Without a precise definition of precursory b-value changes, this and similar prediction schemes are incompatible with the IASPEI criteria for evaluating earthquake precursors. If limitations on measuring precursory changes in seismicity and relating them to the state of stress in the crust can be overcome, real-time forecasting of mainshocks could reduce the loss of lives.

Process Variable Optimization of Cold Metal Transfer Technique in Cladding of Stellite-6 on AISI 316 L Alloy Using Grey Relational Analysis (GRA)

In this work, an attempt was made to identify the optimised parameter combination in cold metal transfer (CMT) cladding process of AISI 316 L austenitic stainless steel. cladding process was carried out using stellite 6 filler wire. Experiments were carried out based on L31 central composite design (CCD). Cladding was done with current, Voltage, torch angle and travel speed as input parameters. Quality of the clad was analysed by measuring depth of penetration, weld area, hardness of the clad surface, corrosion rate and clad interface thickness. Grey relation analysis was used to identify the optimised parameter combination. Trial number 18 was identified as the optimised parameter combination. The optimised input parameters are Welding Current 200 Amps, Voltage 19 V, Torch Angle 70⁰ and Welding Speed 150 m/min. ANOVA was used to identify the most influencing parameters on the overall multi-objective function and it was understood that the combined effect of torch angle, travel speed had a significant influence on the clad quality. Further investigation was carried out through an optimised set of parameters. The cladding experiment was conducted and their surface was investigated through clad profile, hardness of the cladded area, interface thickness of cladding region and corrosion rate.

Experimental Evaluation of Rootstock Clamping Device for Inclined Inserted Grafting of Melons

A grafting machine is a kind of machine that can quickly graft scion to rootstock instead of manual grafting. Currently, an inclined inserted grafting machine uses the mechanical clamping method, which can easily damage the rootstock seedlings due to its high stiffness, thus, reducing the success rate of grafting. This study proposed an effective, flexible clamping device for grafting. The suction hole diameter (HD), the negative pressure (NP), and the surface inclination angle (IA) of the clamping device were studied via a single factor experiment to obtain optimal parameter ranges. Optimal HD, NP, and IA were 2–3 mm, 4–8 kPa, and 10–20°, respectively. The orthogonal experiment results showed that the optimal parameter combination for maximum holding success rate was HD, 2.5 mm; NP, 6 kPa; and IA, 10°. The experimental verification was carried out using the optimal parameter combination, with a holding success rate of 98.3% and no damage. This study provides a reference for the optimal design of an inclined inserted grafting machine.

Single-parameter and parameter combination errors in L2 productions of Swiss German Sign Language

This article presents a study of errors committed by hearing adult L2 learners of Swiss German Sign Language (Deutschschweizerische Gebärdensprache, DSGS). As part of a statistical analysis of single-parameter errors, movement was found to be the parameter most susceptible to errors, followed by location, orientation, and handshape. An analysis of production errors with respect to combinations of manual parameters was also conducted, something that previously has not been undertaken. The parameter combination most frequently involved in errors was movement with location. Possible aspects contributing to the higher error rate for movement are suggested, among which are the inherent complexity of the movement parameter. Finally, the article discusses factors influencing the judging of errors.

Hybrid Optimization Method for Correcting Synchronization Errors in Tapping Center Machines

A hybrid method is proposed for optimizing rigid tapping parameters and reducing synchronization errors in Computer Numerical Control (CNC) machines. The proposed method integrates uniform design (UD), regression analysis, Taguchi method, and fractional-order particle swarm optimizer (FPSO) to optimize rigid tapping parameters. Rigid tapping parameters were laid out in a 28-level uniform layout for the experiments in this study. Since the UD method provided a layout with uniform dispersion in the experimental space, the UD method’s uniform layout provided iconic experimental points. Next, the 28-level uniform layout results and regression analysis results were used to obtain significant parameters and a regression function. To obtain the parameter values from the regression function, FPSO was selected because its diversity and algorithmic effectiveness are enhanced compared with PSO. The experimental results indicated that the proposed method could obtain suitable parameter values. The best parameter combination in FPSO yielded the best results in comparisons of the non-systematic method. Next, the best parameter combination was used to optimize actual CNC machining tools during the factory commissioning process. From the commissioning process perspective, the proposed method rapidly and accurately minimizes synchronization error from 23 pulses to 18 pulses and processing time from 20.8 s to 20 s. In conclusion, the proposed method reduced the time needed to tune factory parameters for CNC machining tools and increased machining precision and decreased synchronization errors.

Leveraging unsupervised learning for optimizing the number of sub-grid tiles for land surface modeling over the Contiguous United States

<p>The representation of land surface’s sub-grid heterogeneity in Earth System models remains a persistent challenge. The evolution of grid-cell partitioning techniques has evolved from user-defined equally sized tiles (Chen et al., 1997) to structural partition techniques based on vegetation or soil spatial distribution (Melton & Arora, 2014), and finally, to advanced clustering techniques, based on the concept of Hydrological Response Units (HRU) (Chaney et al., 2018). These sub-grid tiling schemes for Land Surface Models (LSM) have emerged as efficient and effective options to represent sub-grid heterogeneity. However, such approaches rely on an arbitrarily-defined number of tiles per macroscale grid cell with no assurance of a robust representation of heterogeneity. To address this challenge, we introduce a physically coherent approach that uses a Random Forest Model (RFM) to precompute the optimal tile configuration per macro-grid cell. An RFM is trained on a set of environmental covariates, their spatial organization features over the modeling domain (i.e., correlation lengths), and hydrological target-variables errors of several model outputs.</p><p>We assemble and run the HydroBlocks LSM for 100 tiles’ configurations for 100 domains of 0.5x0.5-degree resolution in the Contiguous United States (CONUS). The tiles’ configuration is defined by two clustering algorithm parameters and one height discretization one. From this parameter combination, 10,000 simulations emerged. For each simulation, we compiled the spatial standard deviation of specific hydrological target-variables and evaluated the tiles’ configuration convergence by comparing various multi-objective optimization methodologies to determine the optimal compromise solutions on each study domain. Preliminary results show that as the number of tiles increases, the hydrological fluxes and states converge toward stable conditions. With the optimal parameter combination set for each domain and information on the environmental characteristics, an RFM is trained to predict the optimal cluster configuration. Using this approach, we demonstrate how a reduced-order model can effectively compute a priori the appropriate tile complexity based solely on environmental characteristics.</p><p><strong>References</strong></p><p>Chaney, N. W. el al. (2018). Harnessing big data to rethink land heterogeneity in Earth system models. Hydrology and Earth System Sciences, 22(6), 3311–3330. https://doi.org/10.5194/hess-22-3311-2018</p><p>Chen, T. H. et al. (1997). Cabauw experimental results from the Project for Intercomparison of Land-Surface Parameterization Schemes. Journal of Climate, 10(6), 1194–1215. https://doi.org/10.1175/1520-0442(1997)010<1194:CERFTP>2.0.CO;2</p><p>Melton, J. R., & Arora, V. K. (2014). Sub-grid scale representation of vegetation in global land surface schemes: implications for estimation of the terrestrial carbon sink. Biogeosciences, 11, 1021–1036. https://doi.org/10.5194/bg-11-1021-2014</p>

Value of 11C-Choline PET/CT-Based Multi-Metabolic Parameter Combination in Distinguishing Early-Stage Prostate Cancer From Benign Prostate Diseases

PurposeThe most common disadvantage of 11C-choline positron emission tomography and computed tomography (PET/CT) in diagnosing early-stage prostate cancer (PCa) is its poor sensitivity. In spite of many efforts, this imaging modality lacks the ideal parameter of choline metabolism for the diagnosis of PCa, and the single metabolic parameter, that is, maximal standardized uptake value (SUVmax), based on this imaging modality is insufficient. 11C-choline PET/CT-based multi-metabolic parameter combination can help break this limitation.Materials and MethodsBefore surgery, SUVmax of choline, which is the most common metabolic parameter of 11C-choline PET/CT, mean standardized uptake value (SUVmean), prostate-to-muscle (P/M) ratio, metabolic tumor volume (MTV) and total lesion glycolysis (TLG) from 74 patients with histologically proven PCa were quantified. A total of 13 patients with focal chronic prostatitis without severe features and 30 patients with benign prostate hyperplasia were used for comparison. Univariable and multivariable analyses were performed to compare the patient characteristics and metabolic parameters of 11C-choline PET/CT. The performance of single parameters and the combination of parameters were assessed by using logistic regression models.ResultsThe comparable c-statistics, which mean the area under the ROC curve in the logistic regression model, of SUVmax, SUVmean, and P/M ratio are 0.657, 0.667, and 0.672, respectively. The c-statistic significantly rose to 0.793 when SUVmax and SUVmean were combined with the P/M ratio. This parameter combination performed the best for PCa cases with all biochemical recurrence risks and for PCa patients grouped by different risk. The greatest improvement over a single parameter, such as P/M ratio, was noted in the group of low-risk PCa, with values of 0.535 to 0.772 for the three-parameter combination. And in the histopathological level, the Ki-67 index is positively correlated with the P/M ratio (r=0.491, p=0.002).ConclusionP/M ratio is a more ideal parameter than SUVmax as a single parameter in early-stage PCa diagnosis. According to our data, the combination of SUVmax, SUVmean, and P/M ratio as a composite parameter for diagnosis of early stage PCa improves the diagnostic accuracy of 11C-choline PET/CT.