Physiological and Genetic Diversity Studies on Regeneration of Santalum Album L.

S. album L. is the source of highly priced and fragrant heartwood which on steam distillation yields on an average 57 per cent oil of high perfumery value. Global demand for sandalwood is about 5000-6000 tons/year and that of oil is 100 tons/year. Heartwood of sandal is estimated to fetch up to Rs. 3.7 million/ton and wood oil Rs.70,000-100,000/ kg in the international market. Sandal heartwood prices have increased from Rs. 365/ton in 1900 to Rs. 6.5 lakhs/ton in 1999-2000 and to Rs. 37 lakhs/ton in 2007. Substantial decline in sandalwood production has occurred from 3176 tons/year during 1960-‘ 65 to 1500 tons/year in 1997-98, and to 500 tons/year in 2007.Depletion of sandal resources is attributed to several factors, both natural and anthropogenic. Low seed setting, poor seed germination, seedling mortality, lack of haustorial connection with host plant roots, recurrent annual fires in natural sandal forests, lopping of trees for fodder, excessive grazing, hacking, encroachments, seedling diseases and spread of sandal spike disease are the major problems facing sandal. While these factors hinder sandal regeneration in forest areas, the situation is accelerated by human activities of chronic overexploitation and illicit felling.Deterioration of natural sandal populations due to illicit felling, encroachments and diseases has an adverse effect on genetic diversity of the species. The loss of genetic diversity has aggravated during recent years due to extensive logging, changing landuse patterns and poor natural regeneration. The consequent genetic erosion is of serious concern affecting tree improvement programme in sandal. Conservation as well as mass propagation are the two strategies to be given due importance. To initiate any conservation programme, precise knowledge of the factors influencing regeneration and survival of the species is essential. Hence, the present study was undertaken with the objective of investigating the autotrophic and parasitic phase of sandal seedlings growth, the effects of shade on morphology, chlorophyll concentration and chlorophyll fluorescence of sandal seedlings, genetic diversity in sandal seed stands using ISSR markers, and the diversity of fungal isolates causing sandal seedling wilt using RAPD markers. All these factors directly influence regeneration and survival of sandal seedlings in natural forests and plantations.

Low Temperature Curing of NBR for Property Improvement

Zinc salts of ethyl, isopropyl, and butyl xanthates are prepared in the laboratory, and the effect of these xanthates with zinc diethyl dithiocarbamate (ZDC) on the vulcanization of HAF-filled nitrile butadiene rubber (NBR) compounds has been studied at different temperatures. The cure times of these compounds have been compared with that of NBR compounds containing TMTD/MBTS. The rubber compounds with the three xanthate accelerators and ZDC are cured at various temperatures from 60 to 150°C. The sheets are molded and properties such as tensile strength, tear strength, cross-link density, elongation at break, compression set, abrasion resistance, flex resistance, etc. have been evaluated. The properties show that zinc salt of the xanthate/ZDC accelerator system has a positive synergistic effect on the cure rate and mechanical properties of NBR compounds.

Starved Extrusion for the Improvement of Mechanical Properties of Rubber Vulcanizates

Filled compounds of natural rubber, isobutylene-isoprene rubber and styrene-butadiene rubber compounds were extruded through a laboratory extruder by varying the feeding rate at different temperatures and revolutions per minute. The extruded compounds were vulcanized up to their optimum cure times and the mechanical properties of the vulcanizates were determined. The properties suggest that there is a particular feeding rate in the starved fed region which results in maximum mechanical properties. The study shows that running the extruder at a slightly starved condition is an attractive means of improving the physical properties.

Consequence modelling,vulnerability assessment and fuzzy fault tree analysis of hazardous storages in an industrial area

The hazards associated with major accident hazard (MAH) industries are fire, explosion and toxic gas releases. Of these, toxic gas release is the worst as it has the potential to cause extensive fatalities. Qualitative and quantitative hazard analyses are essential for the identitication and quantification of the hazards associated with chemical industries. This research work presents the results of a consequence analysis carried out to assess the damage potential of the hazardous material storages in an industrial area of central Kerala, India. A survey carried out in the major accident hazard (MAH) units in the industrial belt revealed that the major hazardous chemicals stored by the various industrial units are ammonia, chlorine, benzene, naphtha, cyclohexane, cyclohexanone and LPG. The damage potential of the above chemicals is assessed using consequence modelling. Modelling of pool fires for naphtha, cyclohexane, cyclohexanone, benzene and ammonia are carried out using TNO model. Vapor cloud explosion (VCE) modelling of LPG, cyclohexane and benzene are carried out using TNT equivalent model. Boiling liquid expanding vapor explosion (BLEVE) modelling of LPG is also carried out. Dispersion modelling of toxic chemicals like chlorine, ammonia and benzene is carried out using the ALOHA air quality model. Threat zones for different hazardous storages are estimated based on the consequence modelling. The distance covered by the threat zone was found to be maximum for chlorine release from a chlor-alkali industry located in the area. The results of consequence modelling are useful for the estimation of individual risk and societal risk in the above industrial area.Vulnerability assessment is carried out using probit functions for toxic, thermal and pressure loads. Individual and societal risks are also estimated at different locations. Mapping of threat zones due to different incident outcome cases from different MAH industries is done with the help of Are GIS.Fault Tree Analysis (FTA) is an established technique for hazard evaluation. This technique has the advantage of being both qualitative and quantitative, if the probabilities and frequencies of the basic events are known. However it is often difficult to estimate precisely the failure probability of the components due to insufficient data or vague characteristics of the basic event. It has been reported that availability of the failure probability data pertaining to local conditions is surprisingly limited in India. This thesis outlines the generation of failure probability values of the basic events that lead to the release of chlorine from the storage and filling facility of a major chlor-alkali industry located in the area using expert elicitation and proven fuzzy logic. Sensitivity analysis has been done to evaluate the percentage contribution of each basic event that could lead to chlorine release. Two dimensional fuzzy fault tree analysis (TDFFTA) has been proposed for balancing the hesitation factor invo1ved in expert elicitation .

Studies on seed pathology and seedling diseases of some important indigenous tree species of kerala

In forestry, availability of healthy seeds is an important factor in raising planting stock. Initial seed health and storage conditions are the major factors governing the germinability of seeds. Like seeds of agricultural and horticultural crops, forest tree seeds are also liable to be affected by micro-organisms during storage, which affects the germination, and reduces the viability. Further introduction of seed-borne diseases into newly sown crops/areas on account of using unhealthy seeds is also not ruled out. Availability of healthy stock of seedlings is intrinsic for raising plantations and to meet this requirement elimination of nursery diseases by appropriate chemicals is of prime imortance. As exotic tree species may become susceptible to various native pathogens, it is generally considered better to select indigenous tree species for large scale plantations as they are well adapted to local environment. However, before taking up large scale afforestation progranme involving any indigenous tree species, it is essential to have knowledge about seed disorders and seedling diseases and their management. with a View to select appropriate tree species with fewer seed disorders and seedling disease problems for use in further plantation programme, four indigenous tree species such as Albizia odoratissima (L.f) Benth., Lagerstroemia microcazpa Wt., Pterocazpus marsupiwn Roxb. and Xylia xylocarpa (Roxb.) Taub. were evaluated to meet the above parameters

Performance improvement of multiple Connections in AODV with the concern of Node bandwidth

Mobile Ad-hoc Networks (MANETS) consists of a collection of mobile nodes without having a central coordination. In MANET, node mobility and dynamic topology play an important role in the performance. MANET provide a solution for network connection at anywhere and at any time. The major features of MANET are quick set up, self organization and self maintenance. Routing is a major challenge in MANET due to it’s dynamic topology and high mobility. Several routing algorithms have been developed for routing. This paper studies the AODV protocol and how AODV is performed under multiple connections in the network. Several issues have been identified. The bandwidth is recognized as the prominent factor reducing the performance of the network. This paper gives an improvement of normal AODV for simultaneous multiple connections under the consideration of bandwidth of node.

A Novel Decision Tree Algorithm for Numeric Datasets - C 4.5*Stat

Decision trees are very powerful tools for classification in data mining tasks that involves different types of attributes. When coming to handling numeric data sets, usually they are converted first to categorical types and then classified using information gain concepts. Information gain is a very popular and useful concept which tells you, whether any benefit occurs after splitting with a given attribute as far as information content is concerned. But this process is computationally intensive for large data sets. Also popular decision tree algorithms like ID3 cannot handle numeric data sets. This paper proposes statistical variance as an alternative to information gain as well as statistical mean to split attributes in completely numerical data sets. The new algorithm has been proved to be competent with respect to its information gain counterpart C4.5 and competent with many existing decision tree algorithms against the standard UCI benchmarking datasets using the ANOVA test in statistics. The specific advantages of this proposed new algorithm are that it avoids the computational overhead of information gain computation for large data sets with many attributes, as well as it avoids the conversion to categorical data from huge numeric data sets which also is a time consuming task. So as a summary, huge numeric datasets can be directly submitted to this algorithm without any attribute mappings or information gain computations. It also blends the two closely related fields statistics and data mining

Prediction of Learning Disabilities in School Age Children using SVM and Decision Tree

This paper highlights the prediction of Learning Disabilities (LD) in school-age children using two classification methods, Support Vector Machine (SVM) and Decision Tree (DT), with an emphasis on applications of data mining. About 10% of children enrolled in school have a learning disability. Learning disability prediction in school age children is a very complicated task because it tends to be identified in elementary school where there is no one sign to be identified. By using any of the two classification methods, SVM and DT, we can easily and accurately predict LD in any child. Also, we can determine the merits and demerits of these two classifiers and the best one can be selected for the use in the relevant field. In this study, Sequential Minimal Optimization (SMO) algorithm is used in performing SVM and J48 algorithm is used in constructing decision trees.

Performance Improvement of Fuzzy and Neuro Fuzzy Systems: Prediction of Learning Disabilities in School-age Children

Learning Disability (LD) is a classification including several disorders in which a child has difficulty in learning in a typical manner, usually caused by an unknown factor or factors. LD affects about 15% of children enrolled in schools. The prediction of learning disability is a complicated task since the identification of LD from diverse features or signs is a complicated problem. There is no cure for learning disabilities and they are life-long. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. The aim of this paper is to develop a new algorithm for imputing missing values and to determine the significance of the missing value imputation method and dimensionality reduction method in the performance of fuzzy and neuro fuzzy classifiers with specific emphasis on prediction of learning disabilities in school age children. In the basic assessment method for prediction of LD, checklists are generally used and the data cases thus collected fully depends on the mood of children and may have also contain redundant as well as missing values. Therefore, in this study, we are proposing a new algorithm, viz. the correlation based new algorithm for imputing the missing values and Principal Component Analysis (PCA) for reducing the irrelevant attributes. After the study, it is found that, the preprocessing methods applied by us improves the quality of data and thereby increases the accuracy of the classifiers. The system is implemented in Math works Software Mat Lab 7.10. The results obtained from this study have illustrated that the developed missing value imputation method is very good contribution in prediction system and is capable of improving the performance of a classifier.

Process Improvement of Coir Pith Degradation Involving White Rot Fungi By Substitution of Urea With Nitrogen Fixing Bacteria and Application of Biodegraded Coir Pith as Plant Growth Medium

In this study, a novel improved technology could be developed to convert the recalcitrant coir pith into environmental friendly organic manure. The standard method of composting involves the substitution of urea with nitrogen fixing bacteria viz. Azotobacter vinelandii and Azospirillum brasilense leading to the development of an improved method of coir pith. The combined action of the microorganisms could enhance the biodegradation of coir pith. In the present study, Pleurotus sajor caju, an edible mushroom which has the ability to degrade coir pith, and the addition of nitrogen fixing bacteria like Azotobacter vinelandii and Azospirillum brasilense could accelerate the action of the fungi on coir pith. The use of these microorganisms brings about definite changes in the NPK, Ammonia, Organic Carbon and Lignin contents in coir pith. This study will encourage the use of biodegraded coir pith as organic manure for agri/horti purpose to get better yields and can serve as a better technology to solve the problem of accumulated coir pith in coir based industries