Background: Shifting cultivation (locally known as “jhum”) is a major driver of deforestation and loss of ecosystem services in rainforests. For developing any effective conservation of biodiversity and carbon service program, an in-depth understanding to the recovery of vegetation and carbon after abandonment of jhum is essential. We estimated species richness, abundance and composition of trees, shrubs and herbs, carbon distribution in aboveground and belowground components along a chronosequence of jhum fallow in northeast India, and elucidated the factors affecting the recovery processes of jhum fallows. Methods: Species composition and other plant community attributes, carbon storage in different pools were studied in 5 jhum fallows (< 5, 5–10, 11–15, 16–20, 21–25 years old) and an old-growth forest. The data were subjected to linear mixed effect modeling using R-package “nlme” for identifying the important factors contributing to the recovery of vegetation and carbon. Results: Species composition varied significantly (P < 0.05) between jhum fallows and old-growth forest. Tree density varied from 28 stems ha⁻¹ in 5 years old jhum fallow to 163 stems ha⁻¹ in old-growth forest. Both biomass carbon in all components and soil organic carbon were significantly (P = 0.01) lower in jhum fallows than in the old-growth forest except living non-woody biomass component. The recovery of aboveground biomass carbon was faster during early successive years than the mid-successive jhum fallows. Total ecosystem carbon and soil organic carbon stock in the oldest jhum fallow was 33% and 62% of those in the old-growth forest, respectively. The fallow age was found to be the most important explanatory factor in the recovery process of vegetation and carbon stock in re-growing fallows. Conclusion: The shifting cultivation fallows gradually recovered both vegetation and carbon and are potential repository sites for biodiversity conservation, which may take much longer time to reach up to old-growth forest in northeast India.

Though several statistics are used to quantify the batting and bowling performance of cricketers, there is no such measure for the fielders in cricket. This article introduces a measure that can be used to gauge the fielding performance of cricketers. The various parameters that are considered for fielding are quantified to scores based on the ball-by-ball information of a match for each fielder. Subjective weights are assigned to these parameters based on the relative importance of different alternatives under various fielding circumstances. The weights are then combined with the scores of each of the on-field performances of a fielder to get his corresponding fielding score. Two different measures of fielding performance are proposed and their relative differences are discussed. To demonstrate these measures, the final of the first Twenty20 World Cup tournament played on 24 September 2007 is considered. The outcomes obtained from the two measures are accordingly compared.

An all-rounder can take an imperative role in any version of the game of cricket, whether it is a test match or any other limited-over format of the game. The study classifies the performance of all-rounders who participated in IPL based on their strike rate and economy rate. Based on the factors mentioned, the all-rounders can be divided into four non-overlapping classes, viz., Performer, Batting All-rounder, Bowling All-rounder, and Under-performer . Several predictor variables that are supposed to influence the performance of all-rounders are considered. Step-wise multinomial logistic regression (SMLR) is used to identify the significant predictors. Samples of six incumbent all-rounders who had not participated in the first three seasons of IPL are considered. The significant predictors were then used to predict the expected class of an incumbent all-rounder using naive Bayesian classification model. The relevant data were collected from the websites, www.cricinfo.org and www.cricketnirvana.com. The key points of this study are as follows: The training sample is populated with 35 all-rounders who had performed in the first three seasons of IPL. Two variables, viz., strike rate (number of runs scored per 100 balls faced) and economy rate (average number of runs scored per over against the bowler) are used to classify the all-rounders as follows: Performer: An all-rounder with strike rate above median and economy rate below median. Batting All-rounder: An all-rounder with strike rate above median and economy rate above median. Bowling All-rounder: An all-rounder with strike rate below median and economy rate below median. Under-performer: An all-rounder with strike rate below median and economy rate above median. The step-wise multinomial logistic regression (SMLR) was used to identify the significant variables that are actually responsible for classification of the all-rounders. The strike rate in ODI, strike rate in Twenty-20, economy rate in ODI, economy rate in Twenty-20 and bowling type (Spin or Fast) of the all-rounders are found to be significant in determining the class of an all-rounder. The naive Bayesian classification model is used for forecasting the expected class of allrounders based on the significant predictors for six incumbent all-rounders who had played only in fourth season of IPL. The prediction done before IPL IV was then compared with the actual situation at the end of the tournament. It is found that four predictions were performed correctly out of the six. This model would be useful for the participating teams' management while deciding the bid of an all-rounder in the upcoming season of IPL as per their requirement.

Dyslipidemia is major problem among population those have sedentary life style as well as in diabetics. Bougainvillea spectabilis is most commonly found in India as an ornamental plant and has got anti-diabetic action due to presence of one insulin mimicking agent D-pinitol (3-O-methyl-chiroinositol). The present investigation was undertaken to evaluate the effect of alcoholic extract of leaves of Bougainvillea spectabilis (AEBSL) on serum lipid profile in albino rats fed with high fat diet (HFD) and to compare it with a standard hypolipidaemic drug simvastatin. Thirty healthy albino rats of both sexes (100-200 g) were randomized into 5 groups of 6 animals each. The groups were treated as follows: Group A: normal diet (ND); Group B: HFD (vanaspati ghee + coconut oil mixture in ratio of 3:2 at 10 ml/kg/day); Group C: HFD+ AEBSL (100 mg/kg/day); Group D: HFD + AEBSL (200 mg/kg/day); Group E: HFD + simvastatin (1.8 mg/kg/day). Lipid profile was estimated after 8 weeks of treatment. AEBSL showed a significant (p<0.01) reduction in total cholesterol (TC), Triglyceride (TG), Low density lipoprotein (LDL), Very low density lipoprotein (VLDL) levels and significant (p<0.01) increase in high density lipoproteins (HDL) in hypercholesteromic rats (Group C and Group D). AEBSL 200 mg/kg/day found to be more effective than AEBSL 100 mg/kg/day. There is also significant improvement in atherogenic index (p<0.01) and increases the percentage of protection AEBSL treated animals. Alcoholic extract of leaves of Bougainvillea spectabilis leaves have excellent lipid lowering potentiality.

A cricket squad that participates in a tournament generally comprises of fifteen players. A balanced squad contains players having different expertise like batting, bowling, wicket keeping, etc. Keeping in view the cricketing requirements, selecting an optimal squad is a difficult decision-making problem. In order to attain that, the study proposes a composite index to measure the performance of cricketers irrespective of their expertise. Then using a binary (0-1) integer programming method a balanced squad of 15 players is selected. To apply the tool to practice, optimum balanced squads are selected from amongst the Indian players who participated in the Indian Premier League (IPL), based on the proposed performance measure for different seasons. The team so selected is compared to the actual Indian team selected for the International Cricket Council (ICC) Twenty20 World Cups following the IPLs in the year 2009, 2010 and 2012. It is seen that the actual Indian team is not much variant to the optimal squad of ICC Twenty20 World Cup 2012 but things are different during the years 2009 and 2010. The optimization technique discussed in this study can be helpful for the balanced team selection of other team sports as well.