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The choice of proper incentives is a critical step in designing strategic planning systems. Incentives should integrate the behavior and action of managers with the objectives and strategies of their firms. The problem of inappropriate incentives in various types of organizations has been described by Kerr. While the literature on incentives is voluminous, the linkage between incentives and strategic planning has not been carefully explored. We propose a model that connects a firm's incentive systems with its strategic context, using incentives theory in the economics literature as basic building blocks.

There is a recurring theme in the spectacular successes of a Wal‐Mart in the U.S., a Canon in Japan, or an ISS in Europe: the ability to stake out future opportunities and make preemptive commitments to a competence platform. Commitment begets success. Being a first mover is becoming a necessary condition for success.

This article provides a framework for strategic adaptation to deregulation drawing on the lessons learned in the United States. A firm can adapt to deregulation by the mix of productmarket and domain management strategies that it chooses. The strategies open to a firm in turn depend upon its resource endowment and strategic predisposition. Since it is difficult to alter either of these at short notice, it is imperative that a firm that is faced with deregulation in its environment initiates these transformations in anticipation of change.

If strategic planning systems have failed it is because managers have failed to adapt them to the changed contexts of their businesses. Four distinct contexts are identified: Pioneer, Expand, Reorient, and Dominate. These labels are chosen to indicate the primary challenge for the business unit in each of these contexts. The contexts vary in their risk and in the adaptation and/or integration orientation that they demand of the strategic planning system. Four key elements of the strategic planning system: direction of goal setting, time‐spending patterns in planning, the relative importance of the strategic budget, and the linkage between the financial plan and the budgets, are identified. Each can be manipulated to adapt the system to suit the firm′s business context.

This study empirically explores one of the important channel issues – the relationship between various channel support given to channel partners and the perceived (by managers) goal‐orientation of a firm. Results from an emerging market, India, indicate that perceived orientation towards both profitability and market share is not associated with any of the channel support considered. Growth orientation however is strongly associated with most of the channel support activities – both business (e.g., business advice, pricing and ordering assistance, and personnel training) as well as marketing (advertising support, sales promotional material, and inventory management assistance) oriented activities. In contrast, perceived sales volume orientation is only associated with advertising support and business advice, however, the relationship is negative. These findings have interesting implications for channel management and channel motivation.

This paper presents an analysis of the current debate on “global marketing” and the degree to which multinational firms can standardise their marketing practices across countries. World markets are getting increasingly homogenised but the authors contend that the framework and associated propositions generated in the paper could help multinational firms determine the degree of standardisation that is possible in different markets.

As data centres grow in size and complexity, traditional air-cooling methods are becoming less effective and more expensive. Immersion cooling, where servers are submerged in a dielectric fluid, has emerged as a promising alternative. Ensuring reliable operations in data centre applications requires the development of an effective control framework for immersion cooling systems, which necessitates the prediction of server temperature. While deep learning-based temperature prediction models have shown effectiveness, further enhancement is needed to improve their prediction accuracy. This study aims to develop a temperature prediction model using Long Short-Term Memory (LSTM) Networks based on recursive encoder-decoder architecture.

This paper explores the use of deep learning algorithms to predict the temperature of a heater in a two-phase immersion-cooled system using NOVEC 7100. The performance of recursive-long short-term memory-encoder-decoder (R-LSTM-ED), recursive-convolutional neural network-LSTM (R-CNN-LSTM) and R-LSTM approaches are compared using mean absolute error, root mean square error, mean absolute percentage error and coefficient of determination (R²) as performance metrics. The impact of window size, sampling period and noise within training data on the performance of the model is investigated.

The R-LSTM-ED consistently outperforms the R-LSTM model by 6%, 15.8% and 12.5%, and R-CNN-LSTM model by 4%, 11% and 12.3% in all forecast ranges of 10, 30 and 60 s, respectively, averaged across all the workloads considered in the study. The optimum sampling period based on the study is found to be 2 s and the window size to be 60 s. The performance of the model deteriorates significantly as the noise level reaches 10%.

The proposed models are currently trained on data collected from an experimental setup simulating data centre loads. Future research should seek to extend the applicability of the models by incorporating time series data from immersion-cooled servers.

The proposed multivariate-recursive-prediction models are trained and tested by using real Data Centre workload traces applied to the immersion-cooled system developed in the laboratory.

Numerical simulations are performed to determine the heat transfer characteristics of slot jet impingement of air on a concave surface. The purpose of this paper is to investigate the effect of protrusions on the heat transfer by placing semi-circular protrusions on the concave surface at several positions. After identifying appropriate locations where the heat transfer is a maximum, multiple protrusions are placed at desired locations on the plate. The gap ratio, curvature ratio (d/D) and the dimensions of the plate are varied so as to obtain heat transfer data. The curvature ratio is varied first, keeping the concave diameter (D) fixed followed by a fixed slot width (d). A surrogate model based on an artificial neural network is developed to determine optimum locations of the protrusions that maximize the heat transfer from the concave surface.

The scope and objectives of the present study are two-dimensional numerical simulations of the problem by considering all the geometrical parameters (H/d, d_p, Re, θ) affecting heat transfer characteristics with the help of networking tool and numerical simulation. Development of a surrogate forward model with artificial neural networks (ANNs) with a view to explore the full parametric space. To quantitatively ascertain if protrusions hurt or help heat transfer for an impinging jet on a concave surface. Determination of the location of protrusions where higher heat transfer could be achieved by using exhaustive search with the surrogate model to replace the time consuming forward model.

A single protrusion has nearly no effect on the heat transfer. For a fixed diameter of concave surface, a smaller jet possesses high turbulence kinetic energy with greater heat transfer. ANN is a powerful tool to not only predict impingement heat transfer characteristics by considering multiple parameters but also to determine the optimum configuration from many thousands of candidate solutions. A maximum increase of 8 per cent in the heat transfer is obtained by the best configuration constituting of multiple protrusions, with respect to the baseline smooth configuration. Even this can be considered as marginal and so it can be concluded that first cut results for heat transfer for an impinging jet on a concave surface with protrusions can be obtained by geometrically modeling a much simpler plain concave surface without any significant loss of accuracy.

The heat transfer during impingement cooling depends on various geometrical parameters but, not all the pertinent parameters have been varied comprehensively in previous studies. It is known that a rough surface may improve or degrade the amount of heat transfer depending on their geometrical dimensions of the target and the rough geometry and the flow conditions. Furthermore, to the best of authors’ knowledge, scarce studies are available with inclusion of protrusions over a concave surface. The present study is devoted to development of a surrogate forward model with ANNs with a view to explore the full parametric space.

The purpose of this paper is to develop an algorithm, using PCA‐based neural network, to retrieve the vertical rainfall structure in a precipitating atmosphere. The algorithm is powered by a rigorous solution to the plane parallel radiative transfer equation for the atmosphere with thermodynamically consistent vertical profiles of humidity, temperature and cloud structures, together with “measured” vertical profiles of the rain structure derived from a radar.

The raining atmosphere is considered to be a plane parallel, radiatively participating medium. The atmospheric thermodynamic profiles such as pressure, temperature and relative humidity along with wind speed at sea surface and cloud parameters corresponding to Nargis, a category 4 tropical cyclone that made its landfall on May 2, 2008 at the Republic of Myanmar, are obtained by solving the flux form of Euler's equations in three‐dimensional form. The state‐of‐the‐art community software Weather Research and Forecasting has been used for solving the set of equations. The three‐dimensional rain profiles for the same cyclone at the same instant of time are obtained from National Aeronautics and Space Administration's space borne Tropical Rainfall Measuring Mission's precipitation radar over collocated pixels. An in‐house Micro‐Tropiques code is used to perform radiative transfer simulations for frequencies corresponding to a typical space borne radiometer, and hence to generate the database which is later used for training the neural network. The back propagation‐based neural network is optimized with reduced number of parameters using principal component analysis (PCA).

The results show that neural network is capable of retrieving the vertical rainfall structure with a correlation coefficient of over 0.99. Further, reducing the ill‐posedness in retrieving 56 parameters from just nine measurements using PCA has improved the root mean square error in the retrievals at reduced computational time.

The paper shows that combining numerically generated atmospheric profiles together with radar measurements to serve as input to a radiative transfer model brings in the much‐required synergy between numerical weather prediction, radar measurements and radiative transfer. This strategy can be gainfully used in satellite meteorology. Using principal components to reduce the ill‐posedness, thereby increasing the robustness in retrieving vertical rain structure, has been attempted for the first time. A well‐trained network can be used as one possible option for an operational algorithm for the proposed Indian climate research satellite Megha‐Tropiques, due to be launched in early 2011.

The purpose of the study is to examine the influence of the corn biofuel on the Jet engine. Each tests were carried out in a small gas turbine setup. The performance characteristics of thrust, thrust-specific fuel consumption, exhaust gas temperature and emission characteristics of Carbon monoxide(CO), Carbon dioxide (CO₂), Oxygen (O₂), Unburned hydrocarbons (UHC) and Nitrogen of oxides (NO) emissions were measured and compared with Jet-A fuel to find the suitability of the biofuel used.

Upgrading and using biofuels in aviation sector have been emerging as a fruitful method to diminish the CO emission into the atmosphere. This research paper explores the possibility of using nanoparticles-enriched bio-oil as a fuel for jet engines. The biofuel taken is corn oil and the added nanoparticles are Al₂O₃.

The biofuel blends used are B0 (100% Jet-A fuel), B10 (10 % corn oil biofuel + 90% Jet-A fuel), B20 (20% corn oil biofuel + 80% Jet-A fuel) and B30 (30% corn oil biofuel + 70% Jet-A fuel). All fuel blends were mixed with the moderate dosage level of 30 ppm. All tests were conducted at different rpm as 50,000, 60,000, 70,000 and 80,000 rpm.

The results proved that within the lower limit, use of biofuel increased the performance characteristics and reduced the emission characteristics except the emission of NO. The moderate-level biofuel with Jet-A fuel showed the equally better performance to the neat Jet-A fuel.