2UPR-RPU过约束并联机构刚度性能评价

运用螺旋理论和应变能方法研究了具有2R1T三自由度的2UPR-RPU过约束并联机构的静弹性刚度性能,模型考虑了杆件和关节的柔度。首先,基于螺旋理论得到分支的约束螺旋系;其次,基于材料力学得到分支中杆件的应变能,通过映射分支约束螺旋系到铰空间得到关节的应变能,通过汇总杆件、关节的应变能和卡氏定理得到与约束螺旋系对应的分支紧凑刚度矩阵;最后,通过虚功原理得到机构的总体刚度矩阵。采用有限元商业软件建立了有限元模型,并与理论模型进行对比,验证了理论模型的正确性。定义弹性元件存储的应变能与总应变能之比作为应变能因子指标,给出了应变能因子指标在规则工作空间的四维切片分布图,从应变能的角度定量评价了各弹性元件对机构刚度性能的影响程度,给出了不同载荷作用下的全局应变能因子指标。本研究为定位对机构刚度性能影响最大的弹性元件提供了新的思路。     

基于季节特征的土壤退墒模型建立与率定

利用河南省南阳市2011—2015年12个墒情站的土壤水分监测资料,分析研究了无增墒情况下土壤含水量消退规律,构建出基于不同季节(夏季、春秋季与冬季)的土壤含水量与衰减系数的数学模型,运用规划求解法率定模型参数,并利用2016—2017年资料对模型进行检验。结果表明,不同季节土壤水分衰竭系数模型分别为α=1.023(1-ω~2/4353)~(1/2)(夏季)、α=1.013(1-ω~2/7005)~(1/2)(春秋季)与α=1.008(1-ω~2/9303)~(1/2)(冬季),所建立的数学模型适用于南阳市相应季节壤土与粘壤土小麦、水稻与休闲地土壤旱情预测,但不适用于夏季与春秋季砂土特别是休闲地砂土土壤墒情预测。     

Improving wood volume predictions in dry tropical forest in the semi-arid Brazil

The volumetric variability of dry tropical forests in Brazil and the scarcity of studies on the subject show the need for the development of techniques that make it possible to obtain adequate and accurate wood volume estimates. In this study, we analyzed a database of thinning trees from a forest management plan in the Contendas de Sincorá National Forest, southwestern Bahia State, Brazil. The data set included a total of 300 trees with a trunk diameter ranging from 5 to 52 cm. Adjustments, validation and statistical selection of four volumetric models were performed. Due to the difference in height values for the same diameter and the low correlation between both variables, we do not suggest models which only use the diameter at breast height (DBH) variable as a predictor because they accommodate the largest estimation errors. In comparing the best single entry model (Hohenald-Krenn) with the Spurr model (best fit model), it is noted that the exclusion of height as a predictor causes the values of 136.44 and 0.93 for Akaike information criterion (AIC) and adjusted determination coefficient (R2 adj), which are poorer than the second best model (Schumacher-Hall). Regarding the minimum sample size, errors in estimation (root mean square error (RMSE) and bias) of the best model decrease as the sample size increases, especially when a larger number of trees with DBH≥15.0 cm are randomly sampled. Stratified sampling by diameter class produces smaller volume prediction errors than random sampling, especially when considering all trees. In summary, the Spurr and Schumacher-Hall models perform better. These models suggest that the total variance explained in the estimates is not less than 95%, producing reliable forecasts of the total volume with shell. Our estimates indicate that the bias around the average is not greater than 7%. Our results support the decision to use regression methods to build models and estimate their parameters, seeking stratification strategies in diameter classes for the sample trees. Volume estimates with valid confidence intervals can be obtained using the Spurr model for the studied dry forest. Stratified sampling of the data set for model adjustment and selection is necessary, since we find significant results with mean error square root values and bias of up to 70% of the total database.     

Mapping the current and future distributions of Onosma species endemic to Iran

Climate change may cause shifts in the natural range of species especially for those that are geographically restricted and/or endemic species. In this study, the spatial distribution of five endemic and threatened species belonging to the genus Onosma (including O. asperrima, O. bisotunensis, O. kotschyi, O. platyphylla, and O. straussii) was investigated under present and future climate change scenarios: RCP2.6 (RCP, representative concentration pathway; optimistic scenario) and RCP8.5 (pessimistic scenario) for the years 2050 and 2080 in Iran. Analysis was conducted using the maximum entropy (MaxEnt) model to provide a basis for the protection and conservation of these species. Seven environmental variables including aspect, depth of soil, silt content, slope, annual precipitation, minimum temperature of the coldest month, and annual temperature range were used as main predictors in this study. The model output for the potential habitat suitability of the studied species showed acceptable performance for all species (i.e., the area under the curve (AUC)>0.800). According to the models generated by MaxEnt, the potential current patterns of the species were consistent with the observed areas of distributions. The projected climate maps under optimistic and pessimistic scenarios (RCP2.6 and RCP8.5, respectively) of 2050 and 2080 resulted in reductions and expansions as well as positive range changes for all species in comparison to their current predicted distributions. Among all species, O. bisotunensis showed the most significant and highest increase under the pessimistic scenario of 2050 and 2080. Finally, the results of this study revealed that the studied plant species have shown an acute adaptability to environmental changes. The results can provide useful information to managers to apply appropriate strategies for the management and conservation of these valuable Iranian medicinal and threatened plant species in the future.     

Predicting the potential geographic distribution of Bactrocera bryoniae and Bactrocera neohumeralis(Diptera: Tephritidae) in China using MaxEnt ecological niche modeling

Bactrocera bryoniae and Bactrocera neohumeralis are highly destructive and major biosecurity/quarantine pests of fruit and vegetable in the tropical and subtropical regions in the South Pacific and Australia. Although these pests have not established in China, precautions must be taken due to their highly destructive nature. Thus, we predicted the potential geographic distribution of B. bryoniae and B. neohumeralis across the world and in particular China by ecological niche modeling of the Maximum Entropy(Max Ent) model with the occurrence records of these two species. Bactrocera bryoniae and B. neohumeralis exhibit similar potential geographic distribution ranges across the world and in China, and each species was predicted to be able to distribute to over 20% of the globe. Globally, the potential geographic distribution ranges for these two fruit fly species included southern Asia, the central and the southeast coast of Africa, southern North America, northern and central South America, and Australia. While within China, most of the southern Yangtze River area was found suitable for these species. Notably, southern China was considered to have the highest risk of B. bryoniae and B. neohumeralis invasions. Our study identifies the regions at high risk for potential establishment of B. bryoniae and B. neohumeralis in the world and in particular China, and informs the development of inspection and biosecurity/quarantine measures to prevent and control their invasions.     

Modeling and implementing the use of aeration to increase water temperature and dissolved oxygen in greenhouse aquaculture cages

In this study, an unique bamboo floating cage was built in order to heat the water used for rearing Nile tilapia (Oreochromis mossambicus x O. niloticus) and climbing perch (Anabas testudineus, Bloch) in Northern Thailand during the colder rainy and winter seasons. This special greenhouse fish cage (SGFC) was covered with polyethylene to heat the surface air with solar energy, and insulated to prevent heat loss. The water temperature at depths of 0.1, 0.3 and 0.8 m was modeled and measured. In various trials, the cages were aerated with and without heated air from the surface. It was found that the thermal efficiency of the SGFC integrated with hot air aeration at a water depth of 0.80 m was 32.65 %. Throughout the day, the average water temperature was 31.82 ± 0.68 °C, which was 0.76 °C higher than the water temperature outside the experiment, and the amount of oxygen in the water of 11.35 ± 0.13 mg/l. The SGFC can produce one extra fish harvest per year when compared with regular fish cages. Development equations to describe the thermal model were used for predicting the water temperature in the SGFC and thus validating the model. Moreover, the economic analysis of the SGFC found that the payback period was one year, seven months and fourteen days.     

基于Simulink的液压系统建模与仿真

针对农业机械中液压系统结构复杂、研发周期长等问题,通过分析农业机械中液压回路各个组成元件特性,基于Simulink构建元件动态数学模型。对液压回路进行研究,分析了液压回路在不同元件参数下的动态性能,为优化液压回路系统提供参考。     

便携式木材绞盘机新型卷筒离合器设计

在利用便携式木材绞盘机进行木材生产试验过程中,发现卷筒离合器在分离与接合过程中易出现卡阻现象,从而影响集材生产效率。在对牙嵌式离合器结构进行受力分析和卡阻原因分析的基础上,基于Solid Works的建模分析设计方法,设计了一种新型多孔旋转圆柱式卷筒离合器,该方法有效缩短了新型卷筒离合器的设计周期。新型卷筒离合器采用圆柱销与圆孔的嵌合,代替牙嵌式离合器牙嵌轴与牙嵌套的嵌合,同等试验条件下,旋转柱销式卷筒离合器平均分离时间分别比柱销式卷筒离合器节省3.71 s、比牙嵌式卷筒离合器节省8 s,改善了便携式木材绞盘机卡阻后离合器分离困难的现象,节约了离合器分离时间,从而有效提高了便携式木材绞盘机在集材作业中的生产效率。     

Artificial neural network modeling of microbial community structures in the Atlantic Forest of Brazil

Microbial communities vary across the landscape in forest soils, but prediction of their biomass and composition is a difficult challenge due to the large numbers of variables that influence their community structures. Here we examine the use of artificial neural network (ANN) models for extraction of patterns among soil chemical variables and microbial community structures in forest soils from three regions of the Atlantic Forest of Brazil. At each location, variations in soil chemical properties and FAME profiles of microbial community structures were mapped at 20 × 20 m intervals within 10 ha parcels. Geostatistical analyses showed that spatial variability in soil physical and chemical variables could be mapped at scale distances of 20 m, but that FAME profiles representing the microbial communities were highly variable and had no spatial dependence at the same scale in most cases. RDA analysis showed that FAME signatures representing different microbial groups were positively associated with soil pH, OM, P and base cations concentrations, whereas microbial biomass was negatively associated with the same environmental factors. In contrast, ANN models revealed clear relationships between microbial community structures at each parcel location, and generated verifiable predictions of variations in FAME profiles in relation to soil pH, texture, and the relative abundances of base cations. The results suggest that ANN modeling provides a useful approach for describing the relationships between microbial community structures and soil properties in tropical forest soils that were not able to be captured using geostatistical and RDA analyses.     

Challenges and opportunities of unmanned aerial vehicles as a new tool for crop pest control

The application of pesticides is not simply delivering chemicals to the target area. It also involves considering the negative aspects and developing strategies to deal with them during the application process, to ensure the maximization of pesticides use efficiency and the maintenance of the ecosystem. Unmanned aerial vehicle (UAV) sprayers demonstrate unique advantages compared to traditional ground sprayers, particularly in terms of maneuverability and labor intensity reduction, showed great potential for chemical application in pest control. It is undeniable that there exist challenges in the practice of UAV spraying, such as higher potential risks of pesticide drift or pathogen transmission, uncertainty canopy deposition for different crops, and unexpected leaf breakage induced by downwash flow. Maximizing the utilization of downwash flow while avoiding lateral air movement outside the intended target crop area is a major issue for chemical application with UAV sprayers, particularly in light of the increasingly apparent consensus on the need for enhanced environmental protection during the chemical application process. It must be considered that the operation strategy in different scenarios and for different crop targets is not the same, unique requirements should be given on nozzle atomization, flight parameters, adjuvants and aircraft types in specific working situations. In future, the implementation of spray drift prediction, technical procedures development, and other solutions aimed at reducing pesticide drift and improving deposition quality, is expected to promote the adoption of UAV sprayers by more farmers. © 2023 Society of Chemical Industry.