The purpose of this study is to explore how corn varieties and moisture content affect the mechanical properties of corn with high moisture content (26.0~36.4%). The mechanical properties of corn that affect corn threshing quality are determined by establishing the contact mechanical model of the threshing elements and corn kernels. These mechanical properties of corn include the size characteristics of corn (corn ear weight and corn kernel triaxial size) in addition to the mechanical characteristics of corn (corn elastic modulus, corn kernel breaking force, and corn stalk breaking force). The effects of corn varieties and moisture content on the mechanical properties of corn were tested. The results showed that the corn varieties and moisture content had significant effects on the mechanical properties of corn. Among them, the different levels of moisture content levels have significant differences in the mechanical properties of corn, while corn varieties have different significant differences in the mechanical properties of corn. The ear weight, corn kernel triaxial size, and the axial and radial breaking force of corn pedicels all increased significantly as the moisture content increased. Corn’s elastic modulus, kernel breaking force, and the normal breaking force of corn pedicels all decreased significantly as moisture content increased. The breaking force of corn kernels at different positions is in the order of ventral, lateral, and top surface, and the breaking force of corn pedicels in different directions is in the order of normal, axial, and radial. When the moisture content is between 26.0% and 36.4%, the maximum breaking force of a corn kernel is 401.62 N, and the minimum is 35.47 N. The maximum breaking force of corn pedicels is 32.72 N, and the minimum is 5.37 N. Understanding the influences of these relationships is very important for applications related to the threshing process, such as selecting the threshing mode, designing the threshing mechanism, and setting the threshing parameters.
In recent ten years, artificial bee colony (ABC) has attracted more and more attention, and many state-of-the-art ABC variants (ABCs) have been developed by introducing different biased information to the search equations. However, the same biased information is employed in employed bee and onlooker bee phases, which will cause over exploitation and lead to premature convergence. To overcome this limit, an effective framework with tristage adaptive biased learning is proposed for existing ABCs (TABL + ABCs). In TABL + ABCs, the search direction in the employed bee stage is guided by learning the ranking biased information of the parent food sources, while in the onlooker bee stage, the search direction is determined by extracting the biased information of population distribution. Moreover, a deletion-restart learning strategy is designed in scout bee stage to prevent the potential risk of population stagnation. Systematic experiment results conducted on CEC2014 competition benchmark suite show that proposed TABL + ABCs perform better than recently published AEL + ABCs and ACoS + ABCs.
Background: Childhood trauma is one of major risk factors to depression, and the incidence of depression among college students has been increasing. Previous studies have validated the effect of childhood trauma on depressive level. But few studies based on the Diathesis-stress theory to investigate the role of perceived stress in the pathway between childhood trauma and depressive level, as well as considering the role of rumination. Methods: We recruited 995 non-clinical Chinese college students and ask them to finish four self-report questionnaires, including Childhood Trauma Questionnaire-Short Form (CTQ-SF), Perceived Stress Scale (PSS), the 22-item Ruminative Responses Scale (RSS-22), and Beck Depression Scale-II (BDI-II). Then we conducted the multiple mediation analyses and multiple linear regression analyses. Results: Results revealed that perceived stress played a mediation role between childhood trauma and depressive level, and rumination played a moderation role between childhood trauma and perceived stress and between childhood trauma and depressive level. Limitations: This is a cross-sectional study and based on subjective reports of measures. Conclusions: These findings provided multi-dimensional indications for reducing the effect of childhood trauma on depressive level in college students, which would help them to improve their mental health.
Leaf anatomy varies with abiotic factors and is an important trait for understanding plant adaptive responses to environmental conditions. Leaf mass per area (LMA) is a key morphological trait and is related to leaf performance, such as light‐saturated photosynthetic rate per leaf mass, leaf mechanical strength, and leaf lifespan. LMA is the multiplicative product of leaf thickness (LT) and leaf density (LD), both of which vary with leaf anatomy. Nevertheless, how LMA, LT, and LD covary with leaf anatomy is largely unexplored along natural environmental gradients. Slope aspect is a topographic factor that underlies variations in solar irradiation, air temperature, humidity, and soil fertility. In the present study, we examined (1) how leaf anatomy varies with different slope aspects and (2) how leaf anatomy is related to LMA, LD, and LT. Leaf anatomy was measured for 30 herbaceous species across three slope aspects (south‐, west‐, and north‐facing slopes; hereafter, SFS, WFS, and NFS, respectively) in an eastern Tibetan subalpine meadow. For 18 of the 30 species, LMA data were available from previous studies. LD was calculated as LMA divided by LT. Among the slope aspects, the dominant species on the SFS exhibited the highest LTs with the thickest spongy mesophyll layers. The thicker spongy mesophyll layer was related to a lower LD via larger intercellular airspaces. In contrast, LD was the highest on NFS among the slope aspects. LMA was not significantly different among the slope aspects because higher LTs on SFS were effectively offset by lower LDs. These results suggest that the relationships between leaf anatomy and LMA were different among the slope aspects. Mechanisms underlying the variations in leaf anatomy may include different solar radiation, air temperatures, soil water, and nutrient availabilities among the slope aspects.
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