73 resultados para two stage least square


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PURPOSE: poly(ADP ribose) polymerase inhibition has been shown to potentiate the cytotoxicity of DNA damaging agents. A phase I study of rucaparib and temozolomide showed that full-dose temozolomide could be given during PARP inhibition. We report the results of a phase II study of intravenous rucaparib 12 mg/m(2) and oral temozolomide 200 mg/m(2) on days 1-5 every 28 days in patients with advanced metastatic melanoma. METHODS: Patients with chemotherapy naïve measurable metastatic melanoma, performance status =2 and good end-organ function were recruited. Treatment was given until progression. A two stage phase II design was used, with response rate the primary endpoint. Population pharmacokinetics and pharmacodynamics were also explored. RESULTS: Forty-six patients were recruited with 37 patients receiving at least 2 cycles and 17 patients at least 6 cycles. Myelosuppression occurred with 25 patients (54 %) requiring a 25 % dose reduction in temozolomide. The response rate was 17.4 %, median time to progression 3.5 months, median overall survival 9.9 months, and 36 % of patients were progression-free at 6 months. CONCLUSIONS: This study showed that temozolomide (150-200 mg/m(2)/day) can safely be given with a PARP inhibitory dose of rucaparib, increasing progression-free survival over historical controls in metastatic melanoma patients.

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A compact differential 4-way power combiner with 2.3 dB loss and high common-mode rejection characteristic for use in mm-wave PAs is presented. A complete circuit comprised of a power splitter, two-stage cascode PA array, and a power combiner was implemented in SiGe technology. Measured small-signal gain of at least 17 dB was obtained from 74.5 GHz to 80.5 GHz with a peak 21 dB at 79 GHz. The prototype delivered 13.2 dBm P1dB and 14.3 dBm Psat when operated from a single 3.3 V supply at 75 GHz.

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Cooling techniques play a key role in improving efficiency and power output of modern gas turbines. The conjugate technique of film and impingement cooling schemes is considered in this study. The Multi-Stage Cooling Scheme (MSCS) involves coolant passing from inside to outside turbine blade through two stages. The first stage; the coolant passes through first hole to internal gap where the impinging jet cools the external layer of the blade. Finally, the coolant passes through the internal gap to the second hole which has specific designed geometry for external film cooling. The effect of design parameters, such as, offset distance between two-stage holes, gap height, and inclination angle of the first hole, on upstream conjugate heat transfer rate and downstream film cooling effectiveness performance are investigated computationally. An Inconel 617 alloy with variable properties is selected for the solid material. The conjugate heat transfer and film cooling characteristics of MSCS are analyzed across blowing ratios of Br = 1 and 2 for density ratio, 2. This study presents upstream wall temperature distributions due to conjugate heat transfer for different gap design parameters. The maximum film cooling effectiveness with upstream conjugate heat transfer is less than adiabatic film cooling effectiveness by 24–34%. However, the full coverage of cooling effectiveness in spanwise direction can be obtained using internal cooling with conjugate heat transfer, whereas adiabatic film cooling effectiveness has narrow distribution.

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This paper presents the design of a novel 8-way power-combining transformer for use in mm-wave power amplifier (PA). The combiner exhibits a record low insertion loss of 1.25 dB at 83.5 GHz. A complete circuit comprised of a power splitter, two-stage cascode PA array, a power combiner and input/output matching elements was designed and realized in SiGe technology. Measured gain of at least 16.8 dB was obtained from 76.4 GHz to 85.3 GHz with a peak 19.5 dB at 83 GHz. The prototype delivered 12.5 dBm OP and 14 dBm saturated output power when operated from a 3.2 V DC supply voltage at 78 GHz. © 2013 IEEE.

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In this study, 137 corn distillers dried grains with solubles (DDGS) samples from a range of different geographical origins (Jilin Province of China, Heilongjiang Province of China, USA and Europe) were collected and analysed. Different near infrared spectrometers combined with different chemometric packages were used in two independent laboratories to investigate the feasibility of classifying geographical origin of DDGS. Base on the same dataset, one laboratory developed a partial least square discriminant analysis model and another laboratory developed an orthogonal partial least square discriminant analysis model. Results showed that both models could perfectly classify DDGS samples from different geographical origins. These promising results encourage the development of larger scale efforts to produce datasets which can be used to differentiate the geographical origin of DDGS and such efforts are required to provide higher level food security measures on a global scale.

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Lithium-ion batteries have been widely adopted in electric vehicles (EVs), and accurate state of charge (SOC) estimation is of paramount importance for the EV battery management system. Though a number of methods have been proposed, the SOC estimation for Lithium-ion batteries, such as LiFePo4 battery, however, faces two key challenges: the flat open circuit voltage (OCV) vs SOC relationship for some SOC ranges and the hysteresis effect. To address these problems, an integrated approach for real-time model-based SOC estimation of Lithium-ion batteries is proposed in this paper. Firstly, an auto-regression model is adopted to reproduce the battery terminal behaviour, combined with a non-linear complementary model to capture the hysteresis effect. The model parameters, including linear parameters and non-linear parameters, are optimized off-line using a hybrid optimization method that combines a meta-heuristic method (i.e., the teaching learning based optimization method) and the least square method. Secondly, using the trained model, two real-time model-based SOC estimation methods are presented, one based on the real-time battery OCV regression model achieved through weighted recursive least square method, and the other based on the state estimation using the extended Kalman filter method (EKF). To tackle the problem caused by the flat OCV-vs-SOC segments when the OCV-based SOC estimation method is adopted, a method combining the coulombic counting and the OCV-based method is proposed. Finally, modelling results and SOC estimation results are presented and analysed using the data collected from LiFePo4 battery cell. The results confirmed the effectiveness of the proposed approach, in particular the joint-EKF method.

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A three-stage heavy ion acceleration scheme for generation of high-energy quasimonoenergetic heavy ion beams is investigated using two-dimensional particle-in-cell simulation and analytical modeling. The scheme is based on the interaction of an intense linearly polarized laser pulse with a compound two-layer target (a front heavy ion layer + a second light ion layer). We identify that, under appropriate conditions, the heavy ions preaccelerated by a two-stage acceleration process in the front layer can be injected into the light ion shock wave in the second layer for a further third-stage acceleration. These injected heavy ions are not influenced by the screening effect from the light ions, and an isolated high-energy heavy ion beam with relatively low-energy spread is thus formed. Two-dimensional particle-in-cell simulations show that ∼100MeV/u quasimonoenergetic Fe24+ beams can be obtained by linearly polarized laser pulses at intensities of 1.1×1021W/cm2

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Li-ion batteries have been widely used in electric vehicles, and battery internal state estimation plays an important role in the battery management system. However, it is technically challenging, in particular, for the estimation of the battery internal temperature and state-ofcharge (SOC), which are two key state variables affecting the battery performance. In this paper, a novel method is proposed for realtime simultaneous estimation of these two internal states, thus leading to a significantly improved battery model for realtime SOC estimation. To achieve this, a simplified battery thermoelectric model is firstly built, which couples a thermal submodel and an electrical submodel. The interactions between the battery thermal and electrical behaviours are captured, thus offering a comprehensive description of the battery thermal and electrical behaviour. To achieve more accurate internal state estimations, the model is trained by the simulation error minimization method, and model parameters are optimized by a hybrid optimization method combining a meta-heuristic algorithm and the least square approach. Further, timevarying model parameters under different heat dissipation conditions are considered, and a joint extended Kalman filter is used to simultaneously estimate both the battery internal states and time-varying model parameters in realtime. Experimental results based on the testing data of LiFePO4 batteries confirm the efficacy of the proposed method.

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We present a novel approach to goal recognition based on a two-stage paradigm of graph construction and analysis. First, a graph structure called a Goal Graph is constructed to represent the observed actions, the state of the world, and the achieved goals as well as various connections between these nodes at consecutive time steps. Then, the Goal Graph is analysed at each time step to recognise those partially or fully achieved goals that are consistent with the actions observed so far. The Goal Graph analysis also reveals valid plans for the recognised goals or part of these goals. Our approach to goal recognition does not need a plan library. It does not suffer from the problems in the acquisition and hand-coding of large plan libraries, neither does it have the problems in searching the plan space of exponential size. We describe two algorithms for Goal Graph construction and analysis in this paradigm. These algorithms are both provably sound, polynomial-time, and polynomial-space. The number of goals recognised by our algorithms is usually very small after a sequence of observed actions has been processed. Thus the sequence of observed actions is well explained by the recognised goals with little ambiguity. We have evaluated these algorithms in the UNIX domain, in which excellent performance has been achieved in terms of accuracy, efficiency, and scalability.

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Modern intense ultrafast pulsed lasers generate an electric field of sufficient strength to permit tunnel ionization of the valence electrons in atoms(1). This process is usually treated as a rapid succession of isolated events, in which the states of the remaining electrons are neglected(2). Such electronic interactions are predicted to be weak, the exception being recollision excitation and ionization caused by linearly polarized radiation(3). In contrast, it has recently been suggested that intense field ionization may be accompanied by a two-stage 'shake-up' reaction(4). Here we report a unique combination of experimental techniques(5-8) that allows us to accurately measure the tunnel ionization probability for argon exposed to 50-fs laser pulses. Most significantly for the current study, this measurement is independent of the optical focal geometry(7,8), equivalent to a homogenous electric field. Furthermore, circularly polarized radiation negates recollision. The present measurements indicate that tunnel ionization results in simultaneous excitation of one or more remaining electrons through shake-up(9). From an atomic physics standpoint, it may be possible to induce ionization from specific states, and will influence the development of coherent attosecond extreme-ultraviolet-radiation sources(10). Such pulses have vital scientific and economic potential in areas such as high-resolution imaging of in vivo cells and nanoscale extreme-ultraviolet lithography.

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Spectral dispersion of light on a finite-size surface plasmon polaritonic (SPP) crystal has been studied. The angular wavelength separation of one or more orders of magnitude higher than in other state-of-the-art wavelength-splitting devices available to date has been demonstrated. The two-stage process is responsible for the dispersion value, which involves conversion of the incident light into SPP Bloch modes of a nanostructure followed by the SPP Bloch waves refraction at the SPP crystal boundary. The high spectral dispersion achievable in plasmonic devices may be useful for integrated high-resolution spectroscopy in nanophotonic, optical communication and lab-on-a-chip applications.

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A system for the identification of power quality violations is proposed. It is a two-stage system that employs the potentials of the wavelet transform and the adaptive neurofuzzy networks. For the first stage, the wavelet multiresolution signal analysis is exploited to denoise and then decompose the monitored signals of the power quality events to extract its detailed information. A new optimal feature-vector is suggested and adopted in learning the neurofuzzy classifier. Thus, the amount of needed training data is extensively reduced. A modified organisation map of the neurofuzzy classifier has significantly improved the diagnosis efficiency. Simulation results confirm the aptness and the capability of the proposed system in power quality violations detection and automatic diagnosis

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The acquisition of radiotherapy planning scans on positron emission tomography (PET)-CT scanners requires the involvement of radiotherapy radiographers. This study assessed the radiation dose received by these radiographers during this process. Radiotherapy planning F- fluorodeoxyglucose (F-FDG) PET-CT scans were acquired for 28 non-small cell lung cancer patients. In order to minimise the radiation dose received, a two-stage process was used in which the most time-consuming part of the set-up was performed before the patient received their F-FDG injection. Throughout this process, the radiographers wore electronic personal dosemeters and recorded the doses received at different stages of the process. The mean total radiation dose received by a radiotherapy radiographer was 5.1±2.6 mSv per patient. The use of the two-stage process reduced the time spent in close proximity to the patient by approximately a factor of four. The two-stage process was effective in keeping radiation dose to a minimum. The use of a pre-injection set-up session reduces the radiation dose to the radiotherapy radiographers because of their involvement in PET-CT radiotherapy treatment planning scans by approximately a factor of three.

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Background
Cachexia in advanced malignancy is a debilitating syndrome which contributes to approximately two million deaths worldwide annually. In spite of advances in understanding the biomedical aspects of cancer cachexia, little attention has been paid to exploring its holistic impact on patients and those who care for them.

Objective
The aim of this paper is to describe the lived experience of cachexia from the perspective of patients with cancer and their family members.

Design
An interpretative phenomenological approach was employed.

Setting and participants
A purposive sampling strategy recruited 15 patients and 12 family members from the Regional Cancer Centre in Northern Ireland.

Method
Each participant was interviewed during 2004/2005 using an unstructured interview. All interviews were recorded and transcribed verbatim. Analysis combined a two stage approach using thematic and interpretative phenomenological analysis.

Results
Analysis generated six superordinate themes that reflected the complex dynamics of the cachexia experience. Themes were: physiological changes in appetite; visuality of cachexia; weight loss interpreted as a bad sign; response from health care professionals; conflict over food; and coping responses.

Conclusions
Findings confirmed that cancer cachexia has far reaching implications for patients and their families, extending beyond physical problems into psychological, social and emotional issues. This insight is a critical first step in the development of more responsive care for these clients.

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The study investigates how producer-specific environmental factors influence the performance of Irish credit unions. The empirical analysis uses a two-stage approach. The first stage measures efficiency by a data envelopment analysis (DEA) estimator, which explicitly incorporates the production of undesirable outputs such as bad loans in the modelling, and the second stage uses truncated regression to infer how various factors influence the (bias-corrected) estimated efficiency. A key finding of the analysis is that 68% of Irish credit unions do not incur an extra opportunity cost in meeting regulatory guidance on bad debt.