Understanding Interactions between Researches Institutes and Industry: Indian Perspective

The tangible potential benefits of interactions between public research institutes and industry are often not realized in practice due to many complexities underlying these interactions. As the interaction is between two diverse organizations, it needs considerable management effort at all stages to make it successful and to get the maximum benefit. Therefore, there is every need to study these interactions, and critically examining different dimensions and identifying key factors that matter in institute-industry interface. This will provide an insight into effective management of their interactions. The present study attempts to assess the situation existing in Indian context. It tries to understand the interactions from the perspective of actual researchers and managers of these interactions at research institutes. The study resulted in identifying key factors at different stages of interaction which if managed correctly, increase the probability of effective and successful interactions leading to development of good technologies. The study also tries to explore whether there is any difference in the perception of researchers across the experience levels and disciplines, and also between researchers and managers

Government Initiatives for Developing Technologies in Public Research Institutes through Strategic Relationship with Industry

The relationship between The country that facilitates strategic relationships among research institutes and industries, through appropriate mechanisms, gains a competitive edge through faster technology development, transfer and commercialization. Successful technology development and transfer, needs early identification of potential technologies and assessment of their commercial potential. This study examines various models for technology selection and technology development available in the literature. Also, this communication presents various government initiatives that promote technology development, transfer and commercialization in Indian public research institutes. These initiatives try to bridge the gap between research institutes and industry and help research institutes to commercialize the technologies emanating from it. Important initiatives and their implementation were identified using an explorative study through a review of secondary sources and websites of concerned government departments

Watershed development: A solution to water shortages in semi-arid India or part of the problem?

There have been dramatic changes in the hydrology of many of the dry areas of South India in recent years as a result of increased groundwater-based irrigation, watershed development and land use change. Although intensive development of water resources has brought about huge benefits, its very success has thrown up new challenges. Demand and competition for water has increased to the extent that — in some areas — current levels of annual water use are so high that, in all but the wettest years, annual water use approximates towards annual replenishment of surface and ground water resources. In these areas, it is clear that the emphasis should switch from development to the management of water resources to ensure that water is allocated to activities with the highest economic and social value. Although current watershed development programmes bring a range of benefits, they may also change the temporal and spatial pattern of water availability and use. This can result in significant negative trade-offs such as more unreliable domestic water supplies in ‘downstream’ areas, particularly during low rainfall or drought years. As part of the Karnataka Watershed Development Project (KAWAD), a water resource audit assessed the status of water resources in the project watersheds and identified resource management practices that should be promoted by the project. This paper summarises the audit’s findings and recommendations, the main lessons learned and progress to date in implementing recommendations. For comparison, findings and recommendations from a water audit in southern Andhra Pradesh are also summarised.Resource /Energy Economics and Policy,

Enantioselective synthesis of (R)-(+)-safrole oxide

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TRANSDERMAL DELIVERY OF FLUCONAZOLE MICROSPONGES: PREPARATION AND IN VITRO CHARACTERIZATION

The pivotal objective of this investigation was to formulate Fluconazole microsponge by emulsion solvent diffusion technique in order to provide sustained release. Microsponge was formulated by emulsion solvent diffusion technique with varied drug–polymer ratios. Ethyl cellulose was used as release retarding material and PVA was used as a surfactant. The prepared microsponges were characterized by SEM, FTIR, particle size analysis, and evaluated for surface morphology, drug loading, in vitro drug release as well. The formulated microsponges are spherical with a porous surface and 108.16μm of mean particle size. The microsponges were then incorporated into carbopol gel. The In vitro drug release results showed that microsponges with 1:1.5 drug–polymer ratios were more efficient to give a sustained drug release of 74.2% at the end of 8 hr. Thus the formulated microsponge-based gel of fluconazole would be a promising alternative to conventional therapy for safe and efficient treatment of fungal infections. KEY WORDS: Fluconazole, Microsponge, Ethyl cellulose, Scanning electron microscopy, particle size and in vitro drug releas

A convenient route for the synthesis of plumbagin

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DESIGN, IN VITRO AND IN VIVO EVALUATION OF CHRONOMODULATED DELIVERY SYSTEMS OF TERBUTALINE SULPHATE FOR NOCTURNAL ASTHAMA

The present study deals with the design and evaluation of chronomodulated delivery of terbutaline sulphate as a chronotherapeutic approach in the treatment of nocturnal asthma. The basic design is based on the Pulsincap technology and consisted of formaldehyde treated insoluble hard gelatin capsule body filled with glutaraldehyde cross-linked carboxymethyl chitosan microspheres of terbutaline sulphate and sealed with a hydrogel tablet plug. The entire device was enteric coated, so as to prevent the variable gastric emptying time. The glutaraldehyde cross-linked carboxymethyl chitosan microspheres appeared to be roughly spherical with the size range of 4.63±0.48 to 11.75±0.92µm. The prepared microspheres possessed good yield and high encapsulation efficiency. Particle size, encapsulation efficiency and release rate are dependent on the fabrication conditions of the microspheres. Drug release from the microspheres depended on the core: coat ratio, reaction time and the rotational speed used in the preparation of microspheres.  Formaldehyde treatment efficiently rendered the hard gelatine capsule bodies water insoluble. The ejection of the plug from the chrnomodulated delivery system depended on the nature and concentration of polymer used in the preparation of table plug. A lag time of 3-8hrs was observed for the chronomodulated delivery systems prepared with different hydrogel plugging materials. Among the different polymers studied, HPC showed highest lag time compared to HPMC K4 M and sodium alginate. The Roentgenographic studies revealed the predicted in vivo performance of the developed chronomodulated delivery systems of terbutaline sulphate. Pharmacokinetic analysis revealed the significant increase in tmax, AUC and MRT of optimized chronomodulated system of terbutaline sulphate compared to that of pure drug. The results of the study conclusively proved the suitability of carboxymethyl chitosan microspheres and the adopted Pulsincap technology in the development of chronomodulated delivery systems for terbutaline sulphate in the treatment of nocturnal asthma. Key words: Nocturnal asthma; Terbutaline sulphate; Chronomodulated systems; Lag time; Roentgenography; In vivo pharmacokinetic

Labelled Classifier with Weighted Drift Trigger Model using Machine Learning for Streaming Data Analysis

The term “data-drift” refers to a difference between the data used to test and validate a model and the data used to deploy it in production. It is possible for data to drift for a variety of reasons. The track of time is an important consideration. Data mining procedures such as classification, clustering, and data stream mining are critical to information extraction and knowledge discovery because of the possibility for significant data type and dimensionality changes over time. The amount of research on mining and analyzing real-time streaming data has risen dramatically in the recent decade. As the name suggests, it’s a stream of data that originates from a number of sources. Analyzing information assets has taken on increased significance in the quest for real-time analytics fulfilment. Traditional mining methods are no longer effective since data is acting in a different way. Aside from storage and temporal constraints, data streams provide additional challenges because just a single pass of the data is required. The dynamic nature of data streams makes it difficult to run any mining method, such as classification, clustering, or indexing, in a single iteration of data. This research identifies concept drift in streaming data classification. For data classification techniques, a Labelled Classifier with Weighted Drift Trigger Model (LCWDTM) is proposed that provides categorization and the capacity to tackle concept drift difficulties. The proposed classifier efficiency is contrasted with the existing classifiers and the results represent that the proposed model in data drift detection is accurate and efficient

Online) An Open Access

ABSTRACT Two models are said to be non-nested models, if one can not be derived as a special case of another. Much attention in classical statistics has been devoted to testing non-nested regression models. Within the classical framework, there are three alternative general approaches to test non-nested models namely, the use of specification error tests; the use of comprehensive model method; and the use of procedures based upon Keywords: Non-Nested Model, Studentized Residuals INTRODUCTION The selection of a good model is an art. The basic idea in statistics is how to select a good model for the purpose of the study. Once a model is given, however, there are statistical criteria to judge whether the given model is bad or not. Since, many models can explain the same set of data about equally well, a given set of data can be used to screen out bad models but not to generate good models, whatever statistical techniques are used. The subject of model selection is treated in classical statistics, which deals with the two topics of estimation and testing of hypotheses. The problem of determining an appropriate model based on a subset of the original set of variables contains three basic ingredients namely, i) The computational technique used to provide the information for the analysis; ii) The criterion used to analyze the variables and select a subset, if that is an appropriate; and iii) The estimation of coefficients in the final model. In model selection criteria, there may be two important problems those arising from nested and nonnested model structures. The nested models arise with, for instance, two models specified in such a way that one model is a special case of the other; the non-nested model arise when neither model follows as a special case of the other. The model selection criterion is a problem of choice among competing models. The choice of a model follows some preliminary data search. In the context of the linear model, it leads to the specification of explanatory variables that appear to be the most important on prior grounds. Often, some explanatory variables appear in one model and reappear in another model gives rise to the nested models; often again neither model, in the case of two models appears to be a special case of the other model gives rise to the non-nested models. In the process of choosing models, statisticians have developed a variety of diagnostic tests. These tests have been classified into two categories: (i) Tests of Nested Regression models, and (ii) Tests of Non-nested Regression models If a modelI can be derived as a special case of another modelII then modelI is said to be nested model within modelII. Two models are said to be non-nested models, if one can not be derived as a special case of another