Considering the need to adopt more sustainable agricultural systems, it is important that sweet potato breeding programs seek to increase not only root productivity, but also the productivity and quality of branches for silage production. The objective was to evaluate the genetic divergence and the importance of traits associated with the production and quality of branch silage in sweet potato genotypes. The experiment was conducted on the JK Campus of the Federal University of Vales do Jequitinhonha and Mucuri Valleys in a randomized block design with 12 treatments and four repetitions. Twelve characteristics of branches and silage were evaluated. There was genetic variability between the genotypes, making it possible to select parents divergent for future breeding programs for silage production. The genotypes BD-54 and BD-31TO were the most divergent in relation to the others, being indicated its use in crossbreeding aiming the improvement of the culture for silage, once the high performance per se of all genotypes evaluated has already been verified in previous works. The characteristics Na, TDN and NDF were those that most contributed to the divergence.

This study examines conditions that impact PPP delivery success or failure in the roadways sector in India using Qualitative Comparative Analysis. QCA is well-suited for problems where multiple factors combine to create pathways leading to an outcome. Past investigations have compared PPP and non-PPP project delivery performance, but this study examines performance within PPPs by uncovering a set of conditions that combine to influence the success or failure road PPP project delivery in India. Based on data from 21 cases, pathways explaining project delivery success or failure were identified. Specifically, PPPs with high concessionaire equity investment and low regional industrial activity led to project delivery success. Projects with lower concessionaire equity investment and low reliance on toll revenue and with either: (a) high project technical complexity or (b) high regional industrial activity, led to project delivery failure. The pathways identified did not have coverage values that they were extremely strong. Coverage strength was hindered by lack of access to information on additional conditions that could be configurationally important. Further, certain characteristics of the Indian market limit generalization. Identification of combinations of conditions leading to PPP project delivery success or failure improves knowledge of the impacts of structure and characteristics of these complex arrangements. This study is one of the first to use fuzzy QCA to understand project delivery success/failure in road PPP projects. Moreover, this study takes into account factors specific to a sector and delivery mode to explain project delivery performance.

Vegetable production is an important sector of economy for farmers in Nepal. The analysis was carried out to explore the trends in vegetable production sector in Nepal along with the recent trend of some major vegetables in terms of area, production and yield. The time series data from 1977/78 to 2016/17 (40 years) of vegetables production and 5 years data (2011/12 - 2015/16) of major vegetables were collected from reliable source and analysis was done through Microsoft Excel. The results show that between 1977/78 and 2016/17 the area under vegetables cultivation has jumped by 222.8% while production is increased by 728.21% and productivity is increased by 156.6% during this course. The result also reveals that during the period of 5 years (2011/12 - 2015/16), solanaceous and cruciferous vegetables has an increasing trend in area, production and yield except for the area under cultivation for eggplant (declined by 5.2%) and for radish (declined by 6.0%) respectively while cucurbitaceous vegetables has increasing trend in area and production but an declining trend in yield except for the yield of cucumber (increased by 15.8%). However, the trend of other major vegetables is seen highly fluctuating over the years. 

In recent years, the foundry sector has been showing an increased interest in reclamation of used sands. Grain shape, sieve analysis, chemical and thermal characteristics must be uniform while molding the sand for better casting characteristics. The problem that tackled by every foundry industry is that of processing an adequate supply of sand which has the properties to meet many requirements imposed upon while molding and core making. Recently, fluidized bed combustors are becoming core of ‘clean wastes technology’ due to their efficient and clean burning of sand. For proven energy efficient sand reclamation processing, analysis of heating system in fluidized bed combustor (FBC) is required. The objective of current study is to design heating element and analysis of heating system by calculation of heat losses and thermal analysis offluidized bed combustorfor improving efficiency.

Coal is important basic energy and important raw materials, the development of coal industry to support the rapid development of the national economy. In the 1950s and 1960s, the proportion of coal in China's primary energy production and consumption structure accounted for 90% and 80% respectively, and the proportion of coal in 2004 was 75.6% and 67.7% respectively. In recent years, with the rapid development of fully mechanized mining equipment manufacturing technology, fully mechanized mining equipment to heavy, strong and automated, so that the reliability of the equipment is guaranteed, a strong impetus to the development of large mining technology, new round of coal mining technology revolution, the current in the East, Jincheng and other mining areas have been the first in the thick coal seam f = 1.5-5 use of large mining height fully mechanized mining equipment, to achieve the highest efficiency, the lowest cost of tons of coal. The main points of this paper are: in the production of coal enterprises to improve the competitiveness of the coal market. Conditions and conditions of coal storage conditions should be allowed to give priority to the use of large mining and mining methods.

This paper mainly uses the idea of pedigree clustering analysis, gray prediction and principal component analysis. The clustering analysis model, GM (1,1) model and principal component analysis model were established by using SPSS software to analyze the correlation matrices and principal component analysis. MATLAB software was used to calculate the correlation matrices. In January, The difference in price changes of major food prices in cities is calculated, and had forecasted the various food prices in June 2016. For the first issue, the main food is classified and the data are processed. After that, the SPSS software is used to classify the 27 kinds of food into four categories by using the pedigree cluster analysis model and the system clustering. The four categories are made by EXCEL. The price of food changes over time with a line chart that analyzes the characteristics of food price volatility. For the second issue, the gray prediction model is established based on the food classification of each kind of food price. First, the original data is cumulated, test and processed, so that the data have a strong regularity, and then establish a gray differential equation, and then use MATLAB software to solve the model. And then the residual test and post-check test, have C <0.35, the prediction accuracy is better. Finally, predict the price trend in June 2016 through the function. For the third issue, we analyzed the main components of 27 kinds of food types by celery, octopus, chicken (white striped chicken), duck and Chinese cabbage by using the data of principal given and analyzed by principal component analysis. It can be detected by measuring a small amount of food, this predict CPI value relatively accurate. Through the study of the characteristics of the region, select Shanghai and Shenyang, by looking for the relevant CPI and food price data, using spss software, principal component analysis, the impact of the CPI on several types of food, and then calculated by matlab algorithm weight, and then the data obtained by the analysis and comparison, different regions should be selected for different types of food for testing.