КЛАСИФІКАЦІЯ МАРКЕТИНГОВИХ ДОСЛІДЖЕНЬ ІННОВАЦІЙНОГО ПРОДУКТУ

At present innovations appear to be an important attribute in social and economic development. At the age of globalization, with customers’ anticipations and competitive business growing up, those willing to stay afloat at the market and determined to develop (the latter being often caused by external reasons) are forced to carry out continuous innovational activity. Researches done both at a company’s level and national, as well as international levels show an essential influence to the innovational activity carried out on an individual basis and the whole of economics, too.Also, studies have improved theoretical tools of innovative business process by classifying systematizing innovation and refinement of individual classifications. This will lead to leakage of the innovation process in the company, which is a set of comprehensive, constantly–implemented in time and space of advanced scientific, technical, technological, organizational, socio–economic and environmental changes that lead to increased efficiency of the enterprise.На сегодняшний день инновации являются важным фактором в развитии социально–экономического развития. В эпоху глобализации, повышения ожиданий клиентов и более конкурентоспособного бизнеса, желающих остаться на рынке, направленных на развитие (часто обусловлено внешними факторами), вынужденные осуществлять непрерывную инновационную деятельность. Проведено исследование как на уровне компании, так и на национальном, международном уровнях показывают существенное влияние на инновационную деятельность, которая осуществляется на условиях отдельных принципов, а также всей экономики. На сьогоднішній день інновації є важливим фактором у розвитку соціально–економічного розвитку. В епоху глобалізації, підвищення очікувань клієнтів і більш конкурентоспроможного бізнесу, охочих залишитися на ринку, спрямованих на розвиток (часто обумовлено зовнішніми факторами), змушені здійснювати безперервну інноваційну діяльність. Проведено дослідження як на рівні компанії, так і на національному, міжнародному рівнях показують істотний вплив на інноваційну діяльність, яка здійснюється на умовах окремих принципів, а також всієї економіки

Wave Cycle Design for Wave Rotor Gas Turbine Engines with Low NOx emissions

The wave rotor is a promising means of pressure-gain for gas turbine engines. This paper examines novel wave rotor topping cycles that incorporate low-NOx combustion strategies. This approach combines two-stage “rich-quench-lean” (RQL) combustion with intermediate expansion in the wave rotor to extract energy and reduce the peak stoichiometric temperature substantially. The thermodynamic cycle is a type of reheat cycle, with the rich-zone air undergoing a high-pressure stage. Rich-stage combustion could occur external to or within the wave rotor. An approximate analytical design method and CFD/combustion codes are used to develop and simulate wave rotor flow cycles. Engine cycles designed with a bypass turbine and external combustion demonstrate a performance enhancement equivalent to a 200–400 R (110–220 K) increase in turbine inlet temperature. The stoichiometric combustion temperature is reduced by 300–450 R (170–250 K) relative to an equivalent simple cycle, implying substantially reduced NOx formation

Propulsion over a wide Mach number range

Criteria is presented to assess the relative merits of different propulsion systems. Previous references focus mainly on subsonic or low supersonic flight speeds. The main focus here is on a higher range, from low supersonic to orbital velocities. Air breathing propulsion systems for hypersonic flight persent the engine designer with circumstances that differ in important fundamental ways from those encountered in engines designed for operation at subsonic or low supersonic speeds. This analysis highlights the importance of various features of hypersonic engine design. Since the performance of hypersonic engines are energy limited, unlike low speed engines which are stagnation pressure limited, the efficient use of the energy of the fuel used is critical to minimize the take-off fuel mass fraction of the vehicle. Furthermore, since the required energy increase of a vehicle per incremental speed change increases with speed, the engine must be designed to operate efficiently at high speed. An analysis of engine performance in terms of entropy changes of the flow passing through the engine allows comparison of various engine designs as well as a convenient method to determine the effect of individual engine component efficiencies on overall engine performance

Analytic Design Methods for Wave Rotor Cycles

A procedure to design a preliminary wave rotor cycle for any application is presented. To complete a cycle with heat addition there are two separate-but related-design steps that must be performed. Selection of a wave configuration determines the allowable amount of heat added in any case, and the ensuing wave pattern requires associated pressure discharge conditions to allow the process to be made cyclic. This procedure, when applied, gives a first estimate of the cycle performance and the necessary information for proceeding to the next step in the design process, namely, the application of a characteristic-based or other appropriate detailed one-dimensional wave calculation that locates more precisely the proper porting around the periphery of the wave rotor. Examples of the design procedure are given to demonstrate its utility and generality. These examples also illustrate the large gains In performance that might be realized with the use of wave rotor enhanced propulsion cycles

Nuclear Shell Model Calculations of Neutralino-Nucleus Cross Sections for Silicon 29 and Germanium 73

We present the results of detailed nuclear shell model calculations of the spin-dependent elastic cross section for neutralinos scattering from \si29 and \ge73. The calculations were performed in large model spaces which adequately describe the configuration mixing in these two nuclei. As tests of the computed nuclear wave functions, we have calculated several nuclear observables and compared them with the measured values and found good agreement. In the limit of zero momentum transfer, we find scattering matrix elements in agreement with previous estimates for \si29 but significantly different than previous work for \ge73. A modest quenching, in accord with shell model studies of other heavy nuclei, has been included to bring agreement between the measured and calculated values of the magnetic moment for \ge73. Even with this quenching, the calculated scattering rate is roughly a factor of 2 higher than the best previous estimates; without quenching, the rate is a factor of 4 higher. This implies a higher sensitivity for germanium dark matter detectors. We also investigate the role of finite momentum transfer upon the scattering response for both nuclei and find that this can significantly change the expected rates. We close with a brief discussion of the effects of some of the non-nuclear uncertainties upon the matrix elements.Comment: 31 pages, figures avaiable on request, UCRL-JC-11408

Nuclear spin structure in dark matter search: The finite momentum transfer limit

Spin-dependent elastic scattering of weakly interacting massive dark matter particles (WIMP) off nuclei is reviewed. All available, within different nuclear models, structure functions S(q) for finite momentum transfer (q>0) are presented. These functions describe the recoil energy dependence of the differential event rate due to the spin-dependent WIMP-nucleon interactions. This paper, together with the previous paper ``Nuclear spin structure in dark matter search: The zero momentum transfer limit'', completes our review of the nuclear spin structure calculations involved in the problem of direct dark matter search.Comment: 39 pages, 12 figures, a review in revtex

A global framework for linking alpine-treeline ecotone patterns to underlying processes

Globally, treeline ecotones vary from abrupt lines to extended zones of increasingly small, stunted and/or dispersed trees. These spatial patterns contain information about the processes that control treeline dynamics. Describing these patterns consistently along ecologically meaningful dimensions is needed for generalizing hypotheses and knowledge about controlling processes and expected treeline shifts globally. However, existing spatial categorizations of treelines are very loosely defined, leading to ambiguities in their use and interpretation. To help better understand treeline-forming processes, we present a new framework for describing alpine treeline ecotones, focusing on hillside-scale patterns, using pattern dimensions with distinct indicative values: 1) the spatial pattern in the x-y plane: a) decline in tree cover, and b) change in the level of clustering. Variation along these dimensions results in more or less 'discrete' or 'diffuse' treelines with or without islands. These patterns mainly indicate demographic processes: establishment and mortality. 2) Changes in tree stature: a) decline in tree height, and b) change in tree shape. Variation along these dimensions results in more or less 'abrupt' or 'gradual' treelines with or without the formation of environmental krummholz. These patterns mainly indicate growth and dieback processes.Additionally, tree population structure can help distinguish alternative hypotheses about pattern formation, while analysing the functional composition of the ecotonal vegetation is essential to understand community-level processes, controlled by species-specific demographic processes.Our graphical representation of this framework can be used to place any treeline pattern in the proposed multi-dimensional space to guide hypotheses on underlying processes and associated dynamics. To quantify the dimensions and facilitate comparative research, we advocate a joint effort in gathering and analysing spatial patterns from treelines globally. The improved recognition of treeline patterns should allow more effective comparative research and monitoring and advance our understanding of treeline-forming processes and vegetation dynamics in response to climate warming

A machine learning platform to optimize the translation of personalized network models to the clinic

PURPOSE Dynamic network models predict clinical prognosis and inform therapeutic intervention by elucidating disease-driven aberrations at the systems level. However, the personalization of model predictions requires the profiling of multiple model inputs, which hampers clinical translation. PATIENTS AND METHODS We applied APOPTO-CELL, a prognostic model of apoptosis signaling, to showcase the establishment of computational platforms that require a reduced set of inputs. We designed two distinct and complementary pipelines: a probabilistic approach to exploit a consistent subpanel of inputs across the whole cohort (Ensemble) and a machine learning approach to identify a reduced protein set tailored for individual patients (Tree). Development was performed on a virtual cohort of 3,200,000 patients, with inputs estimated from clinically relevant protein profiles. Validation was carried out in an in-house stage III colorectal cancer cohort, with inputs profiled in surgical resections by reverse phase protein array (n = 120) and/or immunohistochemistry (n = 117). RESULTS Ensemble and Tree reproduced APOPTO-CELL predictions in the virtual patient cohort with 92% and 99% accuracy while decreasing the number of inputs to a consistent subset of three proteins (40% reduction) or a personalized subset of 2.7 proteins on average (46% reduction), respectively. Ensemble and Tree retained prognostic utility in the in-house colorectal cancer cohort. The association between the Ensemble accuracy and prognostic value (Spearman ρ = 0.43; P = .02) provided a rationale to optimize the input composition for specific clinical settings. Comparison between profiling by reverse phase protein array (gold standard) and immunohistochemistry (clinical routine) revealed that the latter is a suitable technology to quantify model inputs. CONCLUSION This study provides a generalizable framework to optimize the development of network-based prognostic assays and, ultimately, to facilitate their integration in the routine clinical workflow