Rural - Urban Differentiation, Migration and Emerging Educational Inequalities.A case Study of Left-behind Children in Ganqiu Village, Yunnan

Abstract After China's policy of reform and opening up to the outside world was carried out in the late 1970s, millions of farmers are flocking to China's cities, seeking work with a hope of improving the living standard of their families in rural home villages. In Ganqiu village, over the past decade, many rural elderly, women and children have been separated from the family's breadwinner as sons, husbands and fathers head to the cities in search of an off-farm income. The rise of internal migration (also known as floating population liu dong ren kou') in China has greatly stimulated researchers' interest in studying trends and the characteristics of this population and its role in the China's economic transformations. Recent estimates suggest that as many as 58 million children are left behind in migrant-sending regions while their parents are away working. China's household registration (hukou) system makes it very difficult for parents to bring their children to cities. The absence of working parents brings both short-term and long-term consequences for children left behind. The impact of the rural-urban migration on children of migrants is of interest to both academics and policy makers, not only because they affect current social stability both in the countryside and in cities, more importantly because these children are the future of the economic and social performance of China. Generally speaking, large-scale migration of people from rural to urban areas has caused a range of consequences: a)it changes the demographic composition of local rural communities; b)it changes the balance between workers and consumers in rural households; c)it changes the importance of off-farm income (remittances) to rural households; d)it changes consumption preferences in rural communities; f)it changes people's knowledge and understanding of their position in the wider world; g)it affects the way household development cycle have impact on left-behind children. This paper focuses on the impact of migration on the education of left behind children. I shall try to address this issue by comparing the educational performance of children in families where at least one parent migrate with children who stay with parents in the rural home area. The issue of the impact of absent parents on children's education is a considered an important policy issue in China today. Given the enormous size of and variation within the Chinese population, I assume that there will also be variations in this impact and in the conditions that affect this variation. I shall try to explore this impact by a particular case study from Ganqiu village in Zhenxiong County, Yunnan province, and I don't assume that my findings are generally representative of the situation in the country as whole. However, some conditions such as hukou system, national economic policy, and the structure of the educational system are quite similar. Other conditions like local economic development, parents' social-economic positions, guardians' educational situation, etc may vary substantially. I shall try to explore of China-wide and particular local conditions are intertwined with each other to generate consequence on the left-behind children in Ganqiu. This may stimulate further research to explore how variations in local conditions impact education of left behind children.SANT355MASV-SAD

A comparison of resource allocation process in grid and cloud technologies

Grid Computing and Cloud Computing are two different technologies that have emerged to validate the long-held dream of computing as utilities which led to an important revolution in IT industry. These technologies came with several challenges in terms of middleware, programming model, resources management and business models. These challenges are seriously considered by Distributed System research. Resources allocation is a key challenge in both technologies as it causes the possible resource wastage and service degradation. This paper is addressing a comprehensive study of the resources allocation processes in both technologies. It provides the researchers with an in-depth understanding of all resources allocation related aspects and associative challenges, including: load balancing, performance, energy consumption, scheduling algorithms, resources consolidation and migration. The comparison also contributes an informal definition of the Cloud resource allocation process. Resources in the Cloud are being shared by all users in a time and space sharing manner, in contrast to dedicated resources that governed by a queuing system in Grid resource management. Cloud Resource allocation suffers from extra challenges abbreviated by achieving good load balancing and making right consolidation decision

Prediction of earnings per share for industry

Prediction of Earnings Per Share (EPS) is the fundamental problem in finance industry. Various Data Mining technologies have been widely used in computational finance. This research work aims to predict the future EPS with previous values through the use of data mining technologies, thus to provide decision makers a reference or evidence for their economic strategies and business activity. We created three models LR, RBF and MLP for the regression problem. Our experiments with these models were carried out on the real datasets provided by a software company. The performance assessment was based on Correlation Coefficient and Root Mean Squared Error. These algorithms were validated with the data of six different companies. Some differences between the models have been observed. In most cases, Linear Regression and Multilayer Perceptron are effectively capable of predicting the future EPS. But for the high nonlinear data, MLP gives better performance

New circular drawing algorithms

In the circular (other alternate concepts are outerplanar, convex and one-page) drawing one places vertices of a n-vertex m-edge connected graph G along a circle, and the edges are drawn as straight lines. The smallest possible number of crossings in such a drawing of the graph G is called circular (outerplanar, convex, or one-page) crossing number of the graph G. This paper addresses heuristic algorithms to find an ordering of vertices to minimise the number of crossings in the corresponding circular drawing of the graph. New algorithms to find low crossing circular drawings are presented, and compared with algorithm of Makinen, CIRCULAR+ algorithm of Six and Tollis and algorithm of Baur and Brandes. We get better or comparable results to the other algorithms

Multi-capacity combinatorial ordering GA in application to cloud resources allocation and efficient virtual machines consolidation

This paper describes a novel approach making use of genetic algorithms to find optimal solutions for multi-dimensional vector bin packing problems with the goal to improve cloud resource allocation and Virtual Machines (VMs) consolidation. Two algorithms, namely Combinatorial Ordering First-Fit Genetic Algorithm (COFFGA) and Combinatorial Ordering Next Fit Genetic Algorithm (CONFGA) have been developed for that and combined. The proposed hybrid algorithm targets to minimise the total number of running servers and resources wastage per server. The solutions obtained by the new algorithms are compared with latest solutions from literature. The results show that the proposed algorithm COFFGA outperforms other previous multi-dimension vector bin packing heuristics such as Permutation Pack (PP), First Fit (FF) and First Fit Decreasing (FFD) by 4%, 34%, and 39%, respectively. It also achieved better performance than the existing genetic algorithm for multi-capacity resources virtual machine consolidation (RGGA) in terms of performance and robustness. A thorough explanation for the improved performance of the newly proposed algorithm is given

An academic review: applications of data mining techniques in finance industry

With the development of Internet techniques, data volumes are doubling every two years, faster than predicted by Moore’s Law. Big Data Analytics becomes particularly important for enterprise business. Modern computational technologies will provide effective tools to help understand hugely accumulated data and leverage this information to get insights into the finance industry. In order to get actionable insights into the business, data has become most valuable asset of financial organisations, as there are no physical products in finance industry to manufacture. This is where data mining techniques come to their rescue by allowing access to the right information at the right time. These techniques are used by the finance industry in various areas such as fraud detection, intelligent forecasting, credit rating, loan management, customer profiling, money laundering, marketing and prediction of price movements to name a few. This work aims to survey the research on data mining techniques applied to the finance industry from 2010 to 2015.The review finds that Stock prediction and Credit rating have received most attention of researchers, compared to Loan prediction, Money Laundering and Time Series prediction. Due to the dynamics, uncertainty and variety of data, nonlinear mapping techniques have been deeply studied than linear techniques. Also it has been proved that hybrid methods are more accurate in prediction, closely followed by Neural Network technique. This survey could provide a clue of applications of data mining techniques for finance industry, and a summary of methodologies for researchers in this area. Especially, it could provide a good vision of Data Mining Techniques in computational finance for beginners who want to work in the field of computational finance

Brzo otkrivanje uzročnika virusnog proljeva goveda u mlijeku iz spremnika pomoću kombinacije metoda umnožene rekombinazne polimeraze i test-traka za „lateral flow“ analizu

Bovine viral diarrhea virus (BVDV) is one of the most prevalent and economically important pathogens of ruminants, and leads to significant financial losses to the livestock industry worldwide. Development of rapid and accurate diagnostic methods is of great importance for the control and eradication of BVDV infection. The aim of this study was to develop a novel isothermal recombinase polymerase amplification (RPA) method combined with a lateral flow dipstick (LFD), for rapid detection of BVDV. RPA primers and a probe targeting the specific conserved 5′-UTR of BVDV genome were designed. The RPA amplification could be finished at a constant temperature of 38 0000C for 15 min, and the amplification product was easily visualized on a simple LFD within 5 min. The detection limit of this assay was 20 copies per reaction, and there was no cross-reactivity with other bovine infectious viruses, such as infectious bovine rhinotracheitis virus (IBRV), bovine enterovirus (BEV), bovine coronavirus (BcoV), bovine parainfluenza virus type 3 (BPIV-3), bovine ephemeral fever virus (BEFV) and bovine respiratory syncytial virus (BRSV). The assay performance on bulk tank milk was also evaluated, and the sensitivity and accuracy of BVDV LFD RPA was compared with real-time RT-PCR. Of 284 pool or bulk tank milk samples, 51 were found to be positive by RPA assay, whereas 52 were positive by real-time RT-PCR. The coincidence rate between LFD RPA and real-time RT-PCR was 97.54% (277/284).Uzročnik virusnog proljeva goveda (BVDV) jedan je od najčešćih i ekonomski važnih patogena preživača koji uzrokuje znatne financijske gubitke u stočarskoj industriji širom svijeta. Razvoj brzih i točnih dijagnostičkih metoda iznimno je važan za kontrolu i iskorjenjivanje zaraze BVDV-om. Cilj ovog istraživanja bio je razviti novu metodu za brzo otkrivanje BVDV-a baziranu na kombinaciji metoda umnožene rekombinazne polimeraze i test-traka za „lateral flow“ analizu. Oblikovane su početnice i probe za umnažanje rekombinazne polimeraze usmjerene na specifični konzervirani 5’-UTR u genomu BVDV-a. Umnažanje se moglo završiti pri konstantnoj temperaturi od 38 °C tijekom 15 minuta i produkt umnažanja je lako vizualiziran na jednostavnoj test-traci za „lateral flow“ analizu unutar 5 minuta. Test je ograničen na 20 kopija po reakciji, pri čemu nije bilo križne reaktivnosti s drugim goveđim zaraznim virusima kao što su infektivni rinotraheitis virusa goveda (IBRV), goveđi enterovirus (BEV), goveđi koronavirus (BcoV), virus goveđe parainfluence tipa 3 (BPIV-3), virus gljivične ephemeralne groznice (BEFV) i goveđi respiratorni sincicijski virus (BRSV). Učinkovitost kombinacije navedenih metoda istražena je i s obzirom na usporedbu osjetljivosti odnosno točnosti koja se dobiva uporabom RT-PCR metode. Od 284 skupna uzorka mlijeka iz spremnika, kombinacijom metoda umnožene rekombinazne polimeraze i test-traka za „lateral flow“ analizu utvrđen je 51 pozitivan uzorak, a RT-PCR 52 pozitivna uzorka. Stopa podudarnosti između navedenih metoda bila je 97,54 % (277/284)

Various heuristic algorithms to minimise the two-page crossing numbers of graphs

We propose several new heuristics for the twopage book crossing problem, which are based on recent algorithms for the corresponding one-page problem. Especially, the neural network model for edge allocation is combined for the first time with various one-page algorithms. We investigate the performance of the new heuristics by testing them on various benchmark test suites. It is found out that the new heuristics outperform the previously known heuristics and produce good approximations of the planar crossing number for severalwell-known graph families. We conjecture that the optimal two-page drawing of a graph represents the planar drawing of the graph