Municipal sewage sludge compost promotes Mangifera persiciforma tree growth with no risk of heavy metal contamination of soil

Application of sewage sludge compost (SSC) as a fertilizer on landscaping provides a potential way for the effective disposal of sludge. However, the response of landscape trees to SSC application and the impacts of heavy metals from SSC on soil are poorly understood. We conducted a pot experiment to investigate the effects of SSC addition on Mangifera persiciforma growth and quantified its uptake of heavy metals from SSC by setting five treatments with mass ratios of SSC to lateritic soil as 0%:100% (CK), 15%:85% (S15), 30%:70% (S30), 60%:40% (S60), and 100%:0% (S100). As expected, the fertility and heavy metal concentrations (Cu, Zn, Pb and Cd) in substrate significantly increased with SSC addition. The best performance in terms of plant height, ground diameter, biomass and N, P, K uptake were found i n S30, implying a reasonable amount of SSC could benefit the growth of M. persiciforma. The concentrations of Cu, Pb and Cd in S30 were insignificantly different from CK after harvest, indicating that M. persiciforma reduced the risk of heavy metal contamination of soil arising from SSC application. This study suggests that a reasonable rate of SSC addition can enhance M. persiciforma growth without causing the contamination of landscaping soil by heavy metals

Optimization design for roadheader cutting head by orthogonal experiment and finite element analysis

U radu se istražuje optimizacija konstrukcije rezne glave stroja za bušenje. U tu su svrhu kao varijable optimizacije izabrani brzina rotacije, brzina oscilacije, rezni kutovi pijuka i kutovi nagiba pijuka, a kao ciljevi optimizacije izabrani su srednja vrijednost rezultirajuće sile i koeficijent varijacije reznog opterećenja. Učinci ovih parametara na indekse evaluacije analiziraju se ortogonalnim eksperimentom i analizom konačnih elemenata. Također je provedena analiza promjene trenda indeksa evaluacije s promjenama eksperimentalnih čimbenika. U usporedbi s originalnim projektom, dva indeksa evaluacije smanjila su se za 18,3 % i 5,5 % nakon optimizacije, čime je značajno poboljšana rezna performansa rezne glave stroja za bušenje.Optimization design for roadheader cutting head is investigated in this paper. For this purpose, the rotation velocity, the swing velocity, the cutting angles of picks, and the inclination angles of picks are chosen as the variable for the optimization, and the mean value of resultant force and variation coefficient of cutting load are chosen as optimization objective. The effects of these parameters on evaluation indexes are studied by orthogonal experiment and finite element analysis. The change trend of the evaluation indexes with the experimental factors is also carried out. Compared with the original design, the two evaluation indexes decreased by 18,3 % and 5,5 % after optimization design separately, which improves the cutting performance of roadheader cutting head efficiently

Arabidopsis Ovate Family Proteins, a Novel Transcriptional Repressor Family, Control Multiple Aspects of Plant Growth and Development

, AtOFP4 has been shown to regulate secondary cell wall formation by interact with KNOTTED1-LIKE HOMEODOMAIN PROTEIN 7 (KNAT7), and AtOFP5 has been shown to regulate the activity of a BEL1-LIKEHOMEODOMAIN 1(BLH1)-KNAT3 complex during early embryo sac development, but little is known about the function of other AtOFPs. genes may also have diverse functions in regulating plant growth and development. Further analysis suggested that AtOFP1 regulates cotyledon development in a postembryonic manner, and global transcript profiling revealed that it suppress the expression of many other genes.Our results showed that AtOFPs function as transcriptional repressors and they regulate multiple aspects of plant growth and development. These results provided the first overview of a previously unknown transcriptional repressor family, and revealed their possible roles in plant growth and development

Unraveling the mediating role of plant color and familiarity on children’s mood in urban landscape

An important element of urban landscapes is various plants, and contact with urban landscapes can promote children’s positive mood and mental health. However, few studies focus on Asian school-aged children’s mood for different urban landscapes and the factors shaping them. This study attempted to understand the variables, including plant color, familiarity, and viewing distances (setting 0 m and 2 m), using 150 landscape scenes (68 flowering plants, 50 exotic plants, and 32 foliage plants), on the effects of the landscape preferences and mood states of 119 school-aged children (55 boys and 64 girls). Then, using partial least squares path modelling analysis to display the gender difference in children’s color perception, landscape preferences, and mood states. The results show that: (1) Plant color richness, familiarity, and the proportion of non-green parts of scenes positively affected children’s mood states. (2) Flowering plants are more likely to produce positive moods than those of exotic plants and foliage plants. (3) Plant color richness and familiarity significantly and positively correlated with children’s mood states and landscape preferences. (4) Notably, gender differences exist in children’s landscape preferences and mood states. This study underscores the importance of plant color collocation in child-friendly landscapes and considers the gender differences in urban landscape policy decisions. Besides, adding flowering plants and native plants in urban landscapes may potentially enhance children’s mood state and urban green space utilization rate

Forest Fire Prediction with Imbalanced Data Using a Deep Neural Network Method

Forests suffer from heavy losses due to the occurrence of fires. A prediction model based on environmental condition, such as meteorological and vegetation indexes, is considered a promising tool to control forest fires. The construction of prediction models can be challenging due to (i) the requirement of selection of features most relevant to the prediction task, and (ii) heavily imbalanced data distribution where the number of large-scale forest fires is much less than that of small-scale ones. In this paper, we propose a forest fire prediction method that employs a sparse autoencoder-based deep neural network and a novel data balancing procedure. The method was tested on a forest fire dataset collected from the Montesinho Natural Park of Portugal. Compared to the best prediction results of other state-of-the-art methods, the proposed method could predict large-scale forest fires more accurately, and reduces the mean absolute error by 3–19.3 and root mean squared error by 0.95–19.3. The proposed method can better benefit the management of wildland fires in advance and the prevention of serious fire accidents. It is expected that the prediction performance could be further improved if additional information and more data are available

Window-assisted nanosphere lithography for vacuum micro-nano-electronics

Development of vacuum micro-nano-electronics is quite important for combining the advantages of vacuum tubes and solid-state devices but limited by the prevailing fabricating techniques which are expensive, time consuming and low-throughput. In this work, window-assisted nanosphere lithography (NSL) technique was proposed and enabled the low-cost and high-efficiency fabrication of nanostructures for vacuum micro-nano-electronic devices, thus allowing potential applications in many areas. As a demonstration, we fabricated high-density field emitter arrays which can be used as cold cathodes in vacuum micro-nano-electronic devices by using the window-assisted NSL technique. The details of the fabricating process have been investigated. This work provided a new and feasible idea for fabricating nanostructure arrays for vacuum micro-nano-electronic devices, which would spawn the development of vacuum micro-nano-electronics