Product and Process Innovation: Antecedents and Performance outcomes in small IT firms in India

Innovation is a strategic necessity for the survival of today’s organizations. The wide recognition of innovation as a competitive necessity, particularly in dynamic market environments, makes it an evergreen domain for research. This dissertation deals with innovation in small Information Technology (IT) firms in India. The IT industry in India has been a phenomenal success story of the last three decades, and is today facing a crucial phase in its history characterized by the need for fundamental changes in strategies, driven by innovation. This study, while motivated by the dynamics of changing times, importantly addresses the research gap on small firm innovation in Indian IT.This study addresses three main objectives: (a) drivers of innovation in small IT firms in India (b) impact of innovation on firm performance (c) variation in the extent of innovation adoption in small firms. Product and process innovation were identified as the two most contextually relevant types of innovation for small IT firms. The antecedents of innovation were identified as Intellectual Capital, Creative Capability, Top Management Support, Organization Learning Capability, Customer Involvement, External Networking and Employee Involvement.Survey method was adopted for data collection and the study unit was the firm. Surveys were conducted in 2014 across five South Indian cities. Small firm was defined as one with 10-499 employees. Responses from 205 firms were chosen for analysis. Rigorous statistical analysis was done to generate meaningful insights. The set of drivers of product innovation (Intellectual Capital, Creative Capability, Top Management Support, Customer Involvement, External Networking, and Employee Involvement)were different from that of process innovation (Creative Capability, Organization Learning Capability, External Networking, and Employee Involvement). Both product and process innovation had strong impact on firm performance. It was found that firms that adopted a combination of product innovation and process innovation had the highest levels of firm performance. Product innovation and process innovation fully mediated the relationship between all the seven antecedents and firm performance The results of this study have several important theoretical and practical implications. To the best of the researcher’s knowledge, this is the first time that an empirical study of firm level innovation of this kind has been undertaken in India. A measurement model for product and process innovation was developed, and the drivers of innovation were established statistically. Customer Involvement, External Networking and Employee Involvement are elements of Open Innovation, and all three had strong association with product innovation, and the latter twohad strong association with process innovation. The results showed that proclivity for Open Innovation is healthy in the Indian context. Practical implications have been outlined along how firms can organize themselves for innovation, the human talent for innovation, the right culture for innovation and for open innovation. While some specific examples of possible future studies have been recommended, the researcher believes that the study provides numerous opportunities to further this line of enquiry.

Enhanced /3-adrenergic receptors in the brain and pancreas during pancreatic regeneration in weanling rats

Adrenergic stimulation has an inyortant role in the pancreatic It-cell proliferation and insulin secretion. In the present study. we have investigaled how sympathetic system mgulales the panrrealic n I rnerui nr ht an:ilyiing I'pinephi inn 1111 ), Norepinephrinc (NE) and /1-adrenergic receptor changes in the brain as (%eli is in the I swirls. Fill and NII showed a significant decrease in the brain regions, pancreas and plasma :rt 72Ius iller partial prurcrealectonty. We observed an increase in the circulating insulin levels at 72 hrs. Scatchard analysis using I CHI propranolol showed a significant increase in the number of loth the low affinity and high affinity t-adrenergic receplors in cerebral cortex and hypothalamus of partially pancreatectornised rats during peak DNA synthesis. The affinity of the receptors decrea,ed significantly in the low and high affinity receptors of cerebral cortex and the high affinity hypothalamic receptors. In file brain stein, low affinity receptors were increased significantly during regeneration whereas there was no change in the high affinity receptors. The pancreatic ff-adrenergic receptors were also up regulated at 72 firs after partial panerealectony. In vitro studies showed that /i-adrenergic receptors are positive regulators of islet cell proliferation and insulin secretion. Thus our results suggest that the t-adrenergic receptors are functionally enhanced during pancreatic regeneration, which in turn increases pancreatic ft-cell proliferation an(hilisulin secretion in wean hug rats.

Dielecting-resonator-loaded microstrip antenna for enhanced impedance bandwidth and efficiency

A new method for enhancing the 2.1 VSWR impedance bandwidth of microstrip antennas is presented. Bandwidth enhancement is achieved by loading the microstrip antenna by a ceramic microwave dielectric resonator (DR). The validity of this technique has been established using rectangular and circular radiating geometries. This method improves the bandwidth of a rectangular microstrip antenna to more than 10% (= 5 times that of a conventional rectangular microstrip antenna) with an enhanced gain of I dB

Enhanced Bandwidth Microstrip Patch Antennas Loaded with High Permittivity Dielectric Resonators

A novel technique fitr the bat dividth enhancement of conventional rectangular microstrip antenna is proposed in this paper. When a high permittivity dielectric resonator of suitable resonant frequency was loaded over the patch. the % bandwidth of the antenna was increased by more than five tunes without much affecting its gain and radiation performance. A much more improved bandwidth was obtained when the dielectric resonator was placed on the feedline. Experimental study shows a 2:1 VSWR bandwidth of more than 10% and excellent cross polarization performance with increased pass band and radiation coverage abnost the same as that of rectangular microstrip antenna

Enhanced dopamine D2 receptor function in hypothalamus and corpus striatum: their role in liver, plasma and in vitro hepatocyte ALDH regulation in ethahol treated rats

Dopamine D2 receptors are involved in ethanol self- administration behavior and also suggested to mediate the onset and offset of ethanol drinking. In the present study, we investigated dopamine (DA) content and Dopamine D2 (DA D2) receptors in the hypothalamus and corpus striatum of ethanol treated rats and aldehyde dehydrogenase (ALDH) activity in the liver and plasma of ethanol treated rats and in vitro hepatocyte cultures. Hypothalamic and corpus striatal DA content decreased significantly (P\0.05, P\0.001 respectively) and homovanillic acid/ dopamine (HVA/DA) ratio increased significantly (P\0.001) in ethanol treated rats when compared to control. Scatchard analysis of [3H] YM-09151-2 binding to DA D2 receptors in hypothalamus showed a significant increase (P\0.001) in Bmax without any change in Kd in ethanol treated rats compared to control. The Kd of DA D2 receptors significantly decreased (P\0.05) in the corpus striatum of ethanol treated rats when compared to control. DA D2 receptor affinity in the hypothalamus and corpus striatum of control and ethanol treated rats fitted to a single site model with unity as Hill slope value. The in vitro studies on hepatocyte cultures showed that 10-5 M and 10-7 M DA can reverse the increased ALDH activity in 10% ethanol treated cells to near control level. Sulpiride, an antagonist of DA D2, reversed the effect of dopamine on 10% ethanol induced ALDH activity in hepatocytes. Our results showed a decreased dopamine concentration with enhanced DA D2 receptors in the hypothalamus and corpus striatum of ethanol treated rats. Also, increased ALDH was observed in the plasma and liver of ethanol treated rats and in vitro hepatocyte cultures with 10% ethanol as a compensatory mechanism for increased aldehyde production due to increased dopamine metabolism. A decrease in dopamine concentration in major brain regions is coupled with an increase in ALDH activity in liver and plasma, which contributes to the tendency for alcoholism. Since the administration of 10-5 M and 10-7 M DA can reverse the increased ALDH activity in ethanol treated cells to near control level, this has therapeutic application to correct ethanol addicts from addiction due to allergic reaction observed in aldehyde accumulation.

Luminescence tuning and enhanced nonlinear optical properties of nanocomposites of ZnO–TiO2

In this article we present the spectral and nonlinear optical properties of ZnO–TiO2 nanocomposites prepared by colloidal chemical synthesis. Emission peaks of ZnO–TiO2 nanocomposites change from 340 nm to 385 nm almost in proportion to changes in Eg. The nanocomposites show self-defocusing nonlinearity and good nonlinear absorption behaviour. The nonlinear refractive index and the nonlinear absorption increase with increasing TiO2 volume fraction at 532 nm and can be attributed to the enhancement of exciton oscillator strength. ZnO–TiO2 is a potential nanocomposite material for the tunable light emission and for the development of nonlinear optical devices with a relatively small limiting threshold

Enhanced pH and thermal stabilities of invertase immobilized on montmorillonite K-10

Invertase was adsorbed onto micro-porous acid-activated montmorillonite clay (K-10) by two procedures, namely adsorption and covalent binding. The immobilized enzymes were characterized by XRD, surface area measurements and 27Al NMR. XRD measurements revealed an expansion of clay layers due to immobilization which suggests that intercalation had taken place. Surface area measurements also support this observation. 27Al NMR showed that interaction of enzyme with tetrahedral and octahedral Al changes with the immobilization procedure. Sucrose hydrolysis was performed in a batch reactor. The immobilized enzymes showed enhanced pH and thermal stabilities. Optimum pH and temperature were found to increase upon immobilization. The effectiveness factor (η) and Michaelis constant (Km) suggest that diffusional resistances play a major role in the reaction. The immobilized invertase could be stored in buffer of pH 5 and 6 at 5 °C without any significant loss in activity for 20 days.

Enhanced luminescence and nonlinear optical properties of nanocomposites of ZnO–Cu

In this article, we present the spectral and nonlinear optical properties of ZnOCu nanocomposites prepared by colloidal chemical synthesis. The emission consisted of two peaks. The 385-nm ultraviolet (UV) peak is attributed to ZnO and the 550-nm visible peak is attributed to Cu nanocolloids. Obvious enhancement of UV and visible emission of the samples is observed and the strongest UV emission of a typical ZnOCu nanocomposite is over three times stronger than that of pure ZnO. Cu acts as a sensitizer and the enhancement of UV emission are caused by excitons formed at the interface between Cu and ZnO. As the volume fraction of Cu increases beyond a particular value, the intensity of the UV peak decreases while the intensity of the visible peak increases, and the strongest visible emission of a typical ZnOCu nanocomposite is over ten times stronger than that of pure Cu. The emission mechanism is discussed. Nonlinear optical response of these samples is studied using nanosecond laser pulses from a tunable laser in the wavelength range of 450650 nm, which includes the surface plasmon absorption (SPA) band. The nonlinear response is wavelength dependent and switching from reverse saturable absorption (RSA) to saturable absorption (SA) has been observed for Cu nanocolloids as the excitation wavelength changes from the low absorption window region to higher absorption regime near the SPA band. However, ZnO colloids and ZnOCu nanocomposites exhibit induced absorption at this wavelength. Such a changeover in the sign of the nonlinearity of ZnOCu nanocomposites, with respect to Cu nanocolloids, is related to the interplay of plasmon band bleach and optical limiting mechanisms. The SA again changes back to RSA when we move over to the infrared region. The ZnOCu nanocomposites show self-defocusing nonlinearity and good nonlinear absorption behavior. The nonlinear refractive index and the nonlinear absorption increases with increasing Cu volume fraction at 532 nm. The observed nonlinear absorption is explained through two-photon absorption followed by weak free-carrier absorption and interband absorption mechanisms. This study is important in identifying the spectral range and composition over which the nonlinear material acts as a RSA-based optical limiter. ZnOCu is a potential nanocomposite material for the light emission and for the development of nonlinear optical devices with a relatively small limiting threshold.

Studies on development of novel rare earth doped (Bi,Pb)-2212 superconductors with enhanced properties

The objective of the present work is to study the effect of rare-earth (RE) doping on the superconducting properties of (Bi,Pb)-2212 system and to develop novel superconductors in the system with improved properties, especially, the self- and in-field critical current densities so as to use them for practical applications. This dissertation describes a range of findings in Bi-based superconductor using the cationic substitution of rare earth (RE) elements. Most of the experiments reported here take advantage of the difference in the valency and ionic radii of dopant and doping site.

Reinforcement Learning Approaches to Power System Scheduling

One major component of power system operation is generation scheduling. The objective of the work is to develop efficient control strategies to the power scheduling problems through Reinforcement Learning approaches. The three important active power scheduling problems are Unit Commitment, Economic Dispatch and Automatic Generation Control. Numerical solution methods proposed for solution of power scheduling are insufficient in handling large and complex systems. Soft Computing methods like Simulated Annealing, Evolutionary Programming etc., are efficient in handling complex cost functions, but find limitation in handling stochastic data existing in a practical system. Also the learning steps are to be repeated for each load demand which increases the computation time.Reinforcement Learning (RL) is a method of learning through interactions with environment. The main advantage of this approach is it does not require a precise mathematical formulation. It can learn either by interacting with the environment or interacting with a simulation model. Several optimization and control problems have been solved through Reinforcement Learning approach. The application of Reinforcement Learning in the field of Power system has been a few. The objective is to introduce and extend Reinforcement Learning approaches for the active power scheduling problems in an implementable manner. The main objectives can be enumerated as:(i) Evolve Reinforcement Learning based solutions to the Unit Commitment Problem.(ii) Find suitable solution strategies through Reinforcement Learning approach for Economic Dispatch. (iii) Extend the Reinforcement Learning solution to Automatic Generation Control with a different perspective. (iv) Check the suitability of the scheduling solutions to one of the existing power systems.First part of the thesis is concerned with the Reinforcement Learning approach to Unit Commitment problem. Unit Commitment Problem is formulated as a multi stage decision process. Q learning solution is developed to obtain the optimwn commitment schedule. Method of state aggregation is used to formulate an efficient solution considering the minimwn up time I down time constraints. The performance of the algorithms are evaluated for different systems and compared with other stochastic methods like Genetic Algorithm.Second stage of the work is concerned with solving Economic Dispatch problem. A simple and straight forward decision making strategy is first proposed in the Learning Automata algorithm. Then to solve the scheduling task of systems with large number of generating units, the problem is formulated as a multi stage decision making task. The solution obtained is extended in order to incorporate the transmission losses in the system. To make the Reinforcement Learning solution more efficient and to handle continuous state space, a fimction approximation strategy is proposed. The performance of the developed algorithms are tested for several standard test cases. Proposed method is compared with other recent methods like Partition Approach Algorithm, Simulated Annealing etc.As the final step of implementing the active power control loops in power system, Automatic Generation Control is also taken into consideration.Reinforcement Learning has already been applied to solve Automatic Generation Control loop. The RL solution is extended to take up the approach of common frequency for all the interconnected areas, more similar to practical systems. Performance of the RL controller is also compared with that of the conventional integral controller.In order to prove the suitability of the proposed methods to practical systems, second plant ofNeyveli Thennal Power Station (NTPS IT) is taken for case study. The perfonnance of the Reinforcement Learning solution is found to be better than the other existing methods, which provide the promising step towards RL based control schemes for practical power industry.Reinforcement Learning is applied to solve the scheduling problems in the power industry and found to give satisfactory perfonnance. Proposed solution provides a scope for getting more profit as the economic schedule is obtained instantaneously. Since Reinforcement Learning method can take the stochastic cost data obtained time to time from a plant, it gives an implementable method. As a further step, with suitable methods to interface with on line data, economic scheduling can be achieved instantaneously in a generation control center. Also power scheduling of systems with different sources such as hydro, thermal etc. can be looked into and Reinforcement Learning solutions can be achieved.

Bioremediation of petroleum sludge through Phytoremediation.Land farming and Microbial enhanced oil separation

The objective of this research is to study the feasibility of bioremediating the oily sludge from a refinery site. Three different methods of waste treatment were tried i.e. phytoremediation, land farming and microbial enhanced oil separation in laboratory scale treatment systems. A multiprocess approach by combination of phytoremediation, biostimulation and microbial enhanced oil separation is also presented. The methods of analysis, experimental procedure, and results are incorporated into five chapters of this thesis entitled "Bioremediation of petroleum sludge through phytoremediation, land farming and microbial enhanced oil separation.

Statistical Machine Learning Techniques for the Prediction of Learning Disabilities in School-Age Children

Learning Disability (LD) is a general term that describes specific kinds of learning problems. It is a neurological condition that affects a child's brain and impairs his ability to carry out one or many specific tasks. The learning disabled children are neither slow nor mentally retarded. This disorder can make it problematic for a child to learn as quickly or in the same way as some child who isn't affected by a learning disability. An affected child can have normal or above average intelligence. They may have difficulty paying attention, with reading or letter recognition, or with mathematics. It does not mean that children who have learning disabilities are less intelligent. In fact, many children who have learning disabilities are more intelligent than an average child. Learning disabilities vary from child to child. One child with LD may not have the same kind of learning problems as another child with LD. There is no cure for learning disabilities and they are life-long. However, children with LD can be high achievers and can be taught ways to get around the learning disability. In this research work, data mining using machine learning techniques are used to analyze the symptoms of LD, establish interrelationships between them and evaluate the relative importance of these symptoms. To increase the diagnostic accuracy of learning disability prediction, a knowledge based tool based on statistical machine learning or data mining techniques, with high accuracy,according to the knowledge obtained from the clinical information, is proposed. The basic idea of the developed knowledge based tool is to increase the accuracy of the learning disability assessment and reduce the time used for the same. Different statistical machine learning techniques in data mining are used in the study. Identifying the important parameters of LD prediction using the data mining techniques, identifying the hidden relationship between the symptoms of LD and estimating the relative significance of each symptoms of LD are also the parts of the objectives of this research work. The developed tool has many advantages compared to the traditional methods of using check lists in determination of learning disabilities. For improving the performance of various classifiers, we developed some preprocessing methods for the LD prediction system. A new system based on fuzzy and rough set models are also developed for LD prediction. Here also the importance of pre-processing is studied. A Graphical User Interface (GUI) is designed for developing an integrated knowledge based tool for prediction of LD as well as its degree. The designed tool stores the details of the children in the student database and retrieves their LD report as and when required. The present study undoubtedly proves the effectiveness of the tool developed based on various machine learning techniques. It also identifies the important parameters of LD and accurately predicts the learning disability in school age children. This thesis makes several major contributions in technical, general and social areas. The results are found very beneficial to the parents, teachers and the institutions. They are able to diagnose the child’s problem at an early stage and can go for the proper treatments/counseling at the correct time so as to avoid the academic and social losses.

Studies on rubber compositions as passive acoustic materials in underwater electro acoustic transducer technology-and their aging characteristics

Rubber has become an indispensable material in Ocean technology. Rubber components play critical roles such as sealing, damping, environmental protection, electrical insulation etc. in most under water engineering applications. Technology driven innovations in electro acoustic transducers and other sophisticated end uses have enabled quantum jump in the quality and reliability of rubber components. Under water electro acoustic transducers use rubbers as a critical material in their construction. Work in this field has lead to highly reliable and high performance materials which has enhanced service life of transducers to the extent of 1015 years. Present work concentrates on these materials. Conventional rubbers are inadequate to meet many of the stringent functional of the requirements. There exists large gap of information in the rubber technology of under water rubbers, particularly in the context of under water electro acoustic transducers. Present study is towards filling up the gaps of information in this crucial area. The research work has been in the area of compounding and characterisation of rubbers for use in under water electro acoustic transducers. The study also covers specific material system such as encapsulation material, baffle material, seal material, etc. Life prediction techniques of under water rubbers in general has been established with reference to more than one functional property. This thesis is divided into 6 chapters.

Development of plant tissue culture of Plumbago rosea Linn. for enhanced production of secondary metabolites

P rosea syn. Indica belong to the family of plumbaginaceae, is an important medicinal plant, cultivated widely in India. The roots of these plant are generally used for medicinal purposes mainly as diuretic, germicidal, vessicant, and abortifacient. It is also used for anaemia, diarrhea, leprosy and common wart. The bark of the root contains orange yellow pigment named plumbagin, a crystalline substance, belongs to the class of naphthoquinone. Its chemical structure is 5-hydroxy 2-methyl 1,4naphthoquinone. Apart from P rosea, P zeylanica, P europea, Drosera and Drosophyllum also contains plumbagin. The most exploited source of plumbagin is, of course, P. rosea roots. The roots contain O.9mg/ g D.Wt. of plumbagin in the roots. These plants grow very slowly and the roots suitable for plumbagin extraction can be obtained only after several years of growth. The productivity of the plant is also rather poor. The focus of the present study was to develop alternative strategies to obtain plumbagin. The tissue culture of P rosea for micropropagation has been studied

GABA and 5-HT chitosan nanoparticles enhanced liver cell proliferation and neuronal survival in partially hepatectomised rats: GABAB and 5-HT2A receptors functional

Nanoparticulate drug delivery systems provide wide opportunities for solving problems associated with drug stability or disease states and create great expectations in the area of drug delivery (Bosselmann & Williams, 2012). Nanotechnology, in a simple way, explains the technology that deals with one billionth of a meter scale (Ochekpe, et al., 2009). Fewer side effects, poor bioavailability, absorption at intestine, solubility, specific delivery to site of action with good pharmacological efficiency, slow release, degradation of drug and effective therapeutic outcome, are the major challenges faced by most of the drug delivery systems. To a great extent, biopolymer coated drug delivery systems coupled with nanotechnology alleviate the major drawbacks of the common delivery methods. Chitosan, deacetylated chitin, is a copolymer of β-(1, 4) linked glucosamine (deacetylated unit) and N- acetyl glucosamine (acetylated unit) (Radhakumary et al., 2005). Chitosan is biodegradable, non-toxic and bio compatible. Owing to the removal of acetyl moieties that are present in the amine functional groups of chitin, chitosan is readily soluble in aqueous acidic solution. The solubilisation occurs through the protonation of amino groups on the C-2 position of D-glucosamine residues whereby polysaccharide is converted into polycation in acidic media. Chitosan interacts with many active compounds due to the presence of amine group in it. The presence of this active amine group in chitosan was exploited for the interaction with the active molecules in the present study. Nanoparticles of chitosan coupled drugs are utilized for drug delivery in eye, brain, liver, cancer tissues, treatment of spinal cord injury and infections (Sharma et al., 2007; Li, et a., 2009; Paolicelli et al., 2009; Cho et al., 2010). To deliver drugs directly to the intended site of action and to improve pharmacological efficiency by minimizing undesired side effects elsewhere in the body and decrease the long-term use of many drugs, polymeric drug delivery systems can be used (Thatte et al., 2005).