999 resultados para Herger, 12th cent.


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This paper analyses the influence of management on Technical Efficiency Change (TEC) and Technological Progress (TP) in the communication equipment and consumer electronics sub-sectors of Indian hardware electronics industry. Each sub-sector comprises 13 sample firms for two time periods.The primary objective is to determine the relative contribution of TP and TEC to TFP Growth (TFPG) and to establish the influence of firm specific operational management decision variables on these two components. The study finds that both the sub-sectors have strived and achieved steady TP but not TEC in the period of economic liberalisation to cope with the intensifying competition. The management decisions with respect to asset and profit utilization, vertical integration, among others, improved TP and TE in the sub-sectors. However, R&D investments and technology imports proved costly for TFP indicating inadequate efforts and/or poor resource utilisation by the management. Management was found to be complacent in terms of improving or developing their own technology as indicated by their higher dependence on import of raw materials and no influence of R&D on TP.

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The Indian subcontinent divides the north Indian Ocean into two tropical basins, namely the Arabian Sea and the Bay of Bengal. The Arabian Sea has high salinity whereas the salinity of the Bay of Bengal is much lower due to the contrast in freshwater forcing of the two basins. The freshwater received by the Bay in large amounts during the summer monsoon through river discharge is flushed out annually by ocean circulation. After the withdrawal of the summer monsoon, the Ganga – Brahmaputra river plume flows first along the Indian coast and then around Sri Lanka into the Arabian Sea creating a low salinity pool in the southeastern Arabian Sea (SEAS). In the same region, during the pre-monsoon months of February – April, a warm pool, known as the Arabian Sea Mini Warm Pool (ASMWP), which is distinctly warmer than the rest of the Indian Ocean, takes shape. In fact, this is the warmest region in the world oceans during this period. Simulation of the river plume and its movement as well as its implications to thermodynamics has been a challenging problem for models of Indian Ocean. Here we address these issues using an ocean general circulation model – first we show that the model is capable of reproducing fresh plumes in the Bay of Bengal as well as its movement and then we use the model to determine the processes that lead to formation of the ASMWP. Hydrographic observations from the western Bay of Bengal have shown the presence of a fresh plume along the northern part of the Indian coast during summer monsoon. The Indian Ocean model when forced by realistic winds and climatological river discharge reproduces the fresh plume with reasonable accuracy. The fresh plume does not advect along the Indian coast until the end of summer monsoon. The North Bay Monsoon Current, which flows eastward in the northern Bay, separates the low salinity water from the more saline southern parts of the bay and thus plays an important role in the fresh water budget of the Bay of Bengal. The model also reproduces the surge of the fresh-plume along the Indian coast, into the Arabian Sea during northeast monsoon. Mechanisms that lead to the formation of the Arabian Sea Mini Warm Pool are investigated using several numerical experiments. Contrary to the existing theories, we find that salinity effects are not necessary for the formation of the ASMWP. The orographic effects of the Sahyadris (Western Ghats) and resulting reduction in wind speed leads to the formation of the ASMWP. During November – April, the SEAS behave as a low-wind heatdominated regime where the evolution of sea surface temperature is solely determined by atmospheric forcing. In such regions the evolution of surface layer temperature is not dependent on the characteristics of the subsurface ocean such as the barrier layer and temperature inversion.

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This paper elucidates the methodology of applying artificial neural network model (ANNM) to predict the percent swell of calcitic soil in sulphuric acid solutions, a complex phenomenon involving many parameters. Swell data required for modelling is experimentally obtained using conventional oedometer tests under nominal surcharge. The phases in ANN include optimal design of architecture, operation and training of architecture. The designed optimal neural model (3-5-1) is a fully connected three layer feed forward network with symmetric sigmoid activation function and trained by the back propagation algorithm to minimize a quadratic error criterion.The used model requires parameters such as duration of interaction, calcite mineral content and acid concentration for prediction of swell. The observed strong correlation coefficient (R2 = 0.9979) between the values determined by the experiment and predicted using the developed model demonstrates that the network can provide answers to complex problems in geotechnical engineering.

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Use of engineered landfills for the disposal of industrial wastes is currently a common practice. Bentonite is attracting a greater attention not only as capping and lining materials in landfills but also as buffer and backfill materials for repositories of high-level nuclear waste around the world. In the design of buffer and backfill materials, it is important to know the swelling pressures of compacted bentonite with different electrolyte solutions. The theoretical studies on swell pressure behaviour are all based on Diffuse Double Layer (DDL) theory. To establish a relation between the swell pressure and void ratio of the soil, it is necessary to calculate the mid-plane potential in the diffuse part of the interacting ionic double layers. The difficulty in these calculations is the elliptic integral involved in the relation between half space distance and mid plane potential. Several investigators circumvented this problem using indirect methods or by using cumbersome numerical techniques. In this work, a novel approach is proposed for theoretical estimations of swell pressures of fine-grained soil from the DDL theory. The proposed approach circumvents the complex computations in establishing the relationship between mid-plane potential and diffused plates’ distances in other words, between swell pressure and void ratio.

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Molecular diffusion plays a dominant role in transport of contaminants through fine-grained soils with low hydraulic conductivity. Attenuation processes occur while contaminants travel through the soils. Effective diffusion coefficient (De) is expected to take into consideration various attenuation processes. Effective diffusion coefficient has been considered to develop a general approach for modelling of contaminant transport in soils.The effective diffusion coefficient of sodium in presence of sulphate has been obtained using the column test.The reliability of De, has been checked by comparing theoretical breakthrough curves of sodium ion in soils obtained using advection diffusion equation with the experimental curve.

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Theoretical approaches are of fundamental importance to predict the potential impact of waste disposal facilities on ground water contamination. Appropriate design parameters are, in general, estimated by fitting the theoretical models to a field monitoring or laboratory experimental data. Double-reservoir diffusion (Transient Through-Diffusion) experiments are generally conducted in the laboratory to estimate the mass transport parameters of the proposed barrier material. These design parameters are estimated by manual parameter adjusting techniques (also called eye-fitting) like Pollute. In this work an automated inverse model is developed to estimate the mass transport parameters from transient through-diffusion experimental data. The proposed inverse model uses particle swarm optimization (PSO) algorithm which is based on the social behaviour of animals for finding their food sources. Finite difference numerical solution of the transient through-diffusion mathematical model is integrated with the PSO algorithm to solve the inverse problem of parameter estimation.The working principle of the new solver is demonstrated by estimating mass transport parameters from the published transient through-diffusion experimental data. The estimated values are compared with the values obtained by existing procedure. The present technique is robust and efficient. The mass transport parameters are obtained with a very good precision in less time

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The present study examines the geotechnical properties of Indian bentonite clays for their suitability as buffer material in deep geological repository for high-level nuclear wastes. The bentonite samples are characterized for index properties, compaction, hydraulic conductivity and swelling characteristics. Evaluation of geotechnical properties of the compacted bentonite-sand admixtures, from parts of NW India reveals swelling potentials and hydraulic conductivities in the range of 55 % - 108 % and 1.2 X 10 –10 cm/s to 5.42x 10 –11 cm/s respectively. Strong correlation was observed between ESP (exchangeable sodium percentage) and liquid limit/swell potential of tested specimens. Relatively less well-defined trends emerged between ESP and swell pressure/hydraulic conductivity. The Barmer-1 bentonite despite possessing relatively lower montmorillonite content of 68 %, developed higher Atterberg limit and swell potential, and exhibited comparable swelling pressure and hydraulic conductivity as other bentonites with higher montmorillonite contents (82 to 86 %). The desirable geotechnical properties of Barmer clay as a buffer material is attributed to its large ESP (63 %) and, EMDD (1.17 Mg/m3) attained at the experimental compactive stress(5 MPa).

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The methods of design available for geocell-supported embankments are very few. Two of the earlier methods are considered in this paper and a third method is proposed and compared with them. The first method is the slip line method proposed by earlier researchers. The second method is based on slope stability analysis proposed by this author earlier and the new method proposed is based on the finite element analyses. In the first method, plastic bearing failure of the soil was assumed and the additional resistance due to geocell layer is calculated using a non-symmetric slip line field in the soft foundation soil. In the second method, generalpurpose slope stability program was used to design the geocell mattress of required strength for embankment using a composite model to represent the shear strength of geocell layer. In the third method proposed in this paper, geocell reinforcement is designed based on the plane strain finite element analysis of embankments. The geocell layer is modelled as an equivalent composite layer with modified strength and stiffness values. The strength and dimensions of geocell layer is estimated for the required bearing capacity or permissible deformations. These three design methods are compared through a design example.

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This paper describes some of the physical and numerical model tests of reinforced soil retaining walls subjected to dynamic excitation through uni-axial shaking tests. Models of retaining walls are constructed in a perspex box with geotextile reinforcement using the wrap around technique with dry sand backfill and instrumented with displacement sensors, accelerometers and soil pressure sensors. Numerical modelling of these shaking table tests is carried using FLAC. Numerical model is validated by comparing physical model results. Responses of wrap faced walls with different number of reinforcement layers are discussed from both the physical and numerical model tests. Results showed that the displacements are decreasing with the increase in number of reinforcement layers while acceleration amplifications are not affected significantly.

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The applicability of Artificial Neural Networks for predicting the stress-strain response of jointed rocks at varied confining pressures, strength properties and joint properties (frequency, orientation and strength of joints) has been studied in the present paper. The database is formed from the triaxial compression tests on different jointed rocks with different confining pressures and different joint properties reported by various researchers. This input data covers a wide range of rock strengths, varying from very soft to very hard. The network was trained using a 3 layered network with feed forward back propagation algorithm. About 85% of the data was used for training and remaining15% for testing the predicting capabilities of the network. Results from the analyses were very encouraging and demonstrated that the neural network approach is efficient in capturing the complex stress-strain behaviour of jointed rocks. A single neural network is demonstrated to be capable of predicting the stress-strain response of different rocks, whose intact strength vary from 11.32 MPa to 123 MPa and spacing of joints vary from 10 cm to 100 cm for confining pressures ranging from 0 to 13.8 MPa.

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This study presents the characterization of DNAPL and water flow in a fracture under confining pressure. A comprehensive mathematical model and the conditions under which DNAPL will enter an initially water-saturated deforming rock fracture are discussed. A numerical model with which to predict the quantity of each phase in terms of their saturations in deforming rock joint is developed. The effect of varying confining stresses on the traverse time of DNAPL across a fractured aquitard is studied. The sensitivity analysis for physical and hydraulic properties like initial fracture apertures, fracture dips, equivalent fracture aperture and confining pressures are performed and discussed.